Blood Metabolite Signature of Metabolic Syndrome Implicates Alterations in Amino Acid Metabolism: Findings from the Baltimore Longitudinal Study of Aging (BLSA) and the Tsuruoka Metabolomics Cohort Study (TMCS)

Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity

The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub-groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.

Advances in Ferroptosis-Inducing Agents by Targeted Delivery System in Cancer Therapy

Currently, cancer remains one of the most significant threats to human health. Treatment of most cancers remains challenging, despite the implementation of diverse therapies in clinical practice. In recent years, research on the mechanism of ferroptosis has presented novel perspectives for cancer treatment. Ferroptosis is a regulated cell death process caused by lipid peroxidation of membrane unsaturated fatty acids catalyzed by iron ions. The rapid development of bio-nanotechnology has generated considerable interest in exploiting iron-induced cell death as a new therapeutic target against cancer. This article provides a comprehensive overview of recent advancements at the intersection of iron-induced cell death and bionanotechnology. In this respect, the mechanism of iron-induced cell death and its relation to cancer are summarized. Furthermore, the feasibility of a nano-drug delivery system based on iron-induced cell death for cancer treatment is introduced and analyzed. Secondly, strategies for inducing iron-induced cell death using nanodrug delivery technology are discussed, including promoting Fenton reactions, inhibiting glutathione peroxidase 4, reducing low glutathione levels, and inhibiting system Xc-. Additionally, the article explores the potential of combined treatment strategies involving iron-induced cell death and bionanotechnology. Finally, the application prospects and challenges of iron-induced nanoagents for cancer treatment are discussed.

Metabolite profiles and DNA methylation in metabolic syndrome: a two-sample, bidirectional Mendelian randomization

Introduction: Metabolic syndrome (MetS) increases the risk of cardiovascular disease and death. Previous '-omics' studies have identified dysregulated serum metabolites and aberrant DNA methylation in the setting of MetS. However, the relationship between the metabolome and epigenome have not been elucidated. In this study, we identified serum metabolites associated with MetS and DNA methylation, and we conducted bidirectional Mendelian randomization (MR) to assess causal relationships between metabolites and methylation. Methods: We leveraged metabolomic and genomic data from a national United States cohort of older adults (REGARDS), as well as metabolomic, epigenomic, and genomic data from a family-based study of hypertension (HyperGEN). We conducted metabolite profiling for MetS in REGARDS using weighted logistic regression models and validated them in HyperGEN. Validated metabolites were selected for methylation studies which fit linear mixed models between metabolites and six CpG sites previously linked to MetS. Statistically significant metabolite-CpG pairs were selected for two-sample, bidirectional MR. Results: Forward MR indicated that glucose and serine metabolites were causal on CpG methylation near CPT1A [B(SE): -0.003 (0.002), p = 0.028 and B(SE): 0.029 (0.011), p = 0.030, respectively] and that serine metabolites were causal on ABCG1 [B(SE): -0.008(0.003), p = 0.006] and SREBF1 [B(SE): -0.009(0.004), p = 0.018] methylation, which suggested a protective effect of serine. Reverse MR showed a bidirectional relationship between cg06500161 (ABCG1) and serine [B(SE): -1.534 (0.668), p = 0.023]. Discussion: The metabolome may contribute to the relationship between MetS and epigenetic modifications.

Unbiased proteomics and multivariable regularized regression techniques identify SMOC1, NOG, APCS, and NTN1 in an Alzheimer's disease brain proteomic signature

Advancements in omics methodologies have generated a wealth of high-dimensional Alzheimer's disease (AD) datasets, creating significant opportunities and challenges for data interpretation. In this study, we utilized multivariable regularized regression techniques to identify a reduced set of proteins that could discriminate between AD and cognitively normal (CN) brain samples. Utilizing eNetXplorer, an R package that tests the accuracy and significance of a family of elastic net generalized linear models, we identified 4 proteins (SMOC1, NOG, APCS, NTN1) that accurately discriminated between AD (n = 31) and CN (n = 22) middle frontal gyrus (MFG) tissue samples from Religious Orders Study participants with 83 percent accuracy. We then validated this signature in MFG samples from Baltimore Longitudinal Study of Aging participants using leave-one-out logistic regression cross-validation, finding that the signature again accurately discriminated AD (n = 31) and CN (n = 19) participants with a receiver operating characteristic curve area under the curve of 0.863. These proteins were strongly correlated with the burden of neurofibrillary tangle and amyloid pathology in both study cohorts. We additionally tested whether these proteins differed between AD and CN inferior temporal gyrus (ITG) samples and blood serum samples at the time of AD diagnosis in ROS and BLSA, finding that the proteins differed between AD and CN ITG samples but not in blood serum samples. The identified proteins may provide mechanistic insights into the pathophysiology of AD, and the methods utilized in this study may serve as the basis for further work with additional high-dimensional datasets in AD.

Serum Metabolome Analysis Identified Amino-Acid Metabolism Associated With Pain in People With Symptomatic Knee Osteoarthritis - A Cross-Sectional Study

Osteoarthritis (OA) is the most common arthritis affecting synovial joints such as knees and hips of millions of people globally. Usage-related joint pain and reduced function are the most common symptoms experienced by people with OA. To improve pain management, there is a need to identify validated biomarkers predicting therapeutic responses in targeted clinical trials. Our study aimed to identify the metabolic biomarkers for pain and pressure pain detection thresholds (PPTs) in participants with knee pain and symptomatic OA using metabolic phenotyping. Metabolite and cytokine measurements were done on serum samples using LC-MS/MS (liquid gas chromatography integrated magnetic resonance mass spectrometry) and Human Proinflammatory panel 1 kit respectively. Regression analysis was done in a test (n = 75) and replication study (n = 79) to investigate the metabolites associated with current knee pain scores and pressure pain detection thresholds (PPTs). Meta-analysis and correlation were done estimating precision of associated metabolites and identifying relationship between significant metabolites and cytokines respectively. Acyl ornithine, carnosine, cortisol, cortisone, cystine, DOPA, glycolithocholic acid sulphate (GLCAS), phenylethylamine (PEA) and succinic acid were found to be significantly (FDR <.1) associated with pain scores in meta-analysis of both studies. IL-10, IL-13, IL-1β, IL2, IL8 and TNF-α were also found to be associated with the significant metabolites. Significant associations of these metabolites and inflammatory markers with knee pain suggests that targeting relevant pathways of amino acid and cholesterol metabolism may modulate cytokines and these could be targeted as novel therapeutics development to improve knee pain and OA management. PERSPECTIVE: Foreseeing the global burden of knee pain in Osteoarthritis (OA) and adverse effects of current pharmacological therapies, this study is envisaged to investigate serum metabolites and molecular pathways involved in knee pain. The replicated metabolites in this study suggests targeting amino-acid pathways for better management of OA knee pain.

Metabolomics in aging research: aging markers from organs

Metabolism plays an important role in regulating aging at several levels, and metabolic reprogramming is the main driving force of aging. Due to the different metabolic needs of different tissues, the change trend of metabolites during aging in different organs and the influence of different levels of metabolites on organ function are also different, which makes the relationship between the change of metabolite level and aging more complex. However, not all of these changes lead to aging. The development of metabonomics research has opened a door for people to understand the overall changes in the metabolic level in the aging process of organisms. The omics-based "aging clock" of organisms has been established at the level of gene, protein and epigenetic modifications, but there is still no systematic summary at the level of metabolism. Here, we reviewed the relevant research published in the last decade on aging and organ metabolomic changes, discussed several metabolites with high repetition rate, and explained their role in vivo, hoping to find a group of metabolites that can be used as metabolic markers of aging. This information should provide valuable information for future diagnosis or clinical intervention of aging and age-related diseases.

Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort

BACKGROUND

Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising "omics" technology, metabolomics provides an innovative strategy to gain a deeper understanding of the pathophysiology of MetS. The study aimed to systematically investigate the metabolic alterations in MetS and identify biomarker panels for the identification of MetS using machine learning methods.

METHODS

Nuclear magnetic resonance-based untargeted metabolomics analysis was performed on 1011 plasma samples (205 MetS patients and 806 healthy controls). Univariate and multivariate analyses were applied to identify metabolic biomarkers for MetS. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS.

RESULTS

Thirteen significantly differential metabolites were identified and pathway enrichment revealed that arginine, proline, and glutathione metabolism are disturbed metabolic pathways related to MetS. The protein-metabolite-disease interaction network identified 38 proteins and 23 diseases are associated with 10 MetS-related metabolites. The areas under the receiver operating characteristic curve of the SVM, RF, KNN, and logistic regression models based on metabolic biomarkers were 0.887, 0.993, 0.914, and 0.755, respectively.

CONCLUSIONS

The plasma metabolome provides a promising resource of biomarkers for the predictive diagnosis and targeted prevention of MetS. Alterations in amino acid metabolism play significant roles in the pathophysiology of MetS. The biomarker panels and metabolic pathways could be used as preventive targets in dealing with cardiometabolic diseases related to MetS.

The metabolomic profile associated with clustering of cardiovascular risk factors—A multi-sample evaluation

Background

A clustering of cardiovascular risk factors is denoted the metabolic syndrome (MetS), but the mechanistic underpinnings of this clustering is not clear. Using large-scale metabolomics, we aimed to find a metabolic profile common for all five components of MetS.

Methods and findings

791 annotated non-xenobiotic metabolites were measured by ultra-performance liquid chromatography tandem mass spectrometry in five different population-based samples (Discovery samples: EpiHealth, n = 2342 and SCAPIS-Uppsala, n = 4985. Replication sample: SCAPIS-Malmö, n = 3978, Characterization samples: PIVUS, n = 604 and POEM, n = 501). MetS was defined by the NCEP/consensus criteria. Fifteen metabolites were related to all five components of MetS (blood pressure, waist circumference, glucose, HDL-cholesterol and triglycerides) at a false discovery rate of <0.05 with adjustments for BMI and several life-style factors. They represented different metabolic classes, such as amino acids, simple carbohydrates, androgenic steroids, corticosteroids, co-factors and vitamins, ceramides, carnitines, fatty acids, phospholipids and metabolonic lactone sulfate. All 15 metabolites were related to insulin sensitivity (Matsuda index) in POEM, but only Palmitoyl-oleoyl-GPE (16:0/18:1), a glycerophospholipid, was related to incident cardiovascular disease over 8.6 years follow-up in the EpiHealth sample following adjustment for cardiovascular risk factors (HR 1.32 for a SD change, 95%CI 1.07–1.63).

Conclusion

A complex metabolic profile was related to all cardiovascular risk factors included in MetS independently of BMI. This profile was also related to insulin sensitivity, which provide further support for the importance of insulin sensitivity as an important underlying mechanism in the clustering of cardiovascular risk factors.

Alismatis Rhizoma methanolic extract—Effects on metabolic syndrome and mechanisms of triterpenoids using a metabolomic and lipidomic approach

Alismatis rhizoma is a traditional Chinese medicine. Studies have demonstrated that Alismatis rhizoma also has therapeutic effects on metabolic syndrome. However, the pharmacodynamic material basis and mechanism are still unclear. First, UHPLC/Q-Orbitrap MS was used to detect the chemical components of the Alismatis rhizoma extract, and 31 triterpenoids and 2 sesquiterpenes were preliminarily identified. Then, to investigate the mechanism of the Alismatis rhizoma extract on metabolic syndrome, a mouse model of metabolic syndrome induced by high-fructose drinks was established. The results of serum biochemical analysis showed that the levels of TG, TC, LDL-C, and UA after the Alismatis rhizoma extract treatment were markedly decreased. ¹H-NMR was used to conduct non-targeted metabolomics studies. A total of 20 differential metabolites were associated with high-fructose–induced metabolic syndrome, which were mainly correlated with 11 metabolic pathways. Moreover, UHPLC/Q-Orbitrap MS lipidomics analysis found that a total of 53 differential lipids were screened out. The results showed that Alismatis rhizoma extract mainly reduces the synthesis of glycerophospholipid and ceramide and improves the secretion of bile acid. This study shows that the Alismatis rhizoma extract can treat metabolic syndrome mainly by inhibiting energy metabolism, amino acid metabolism, and regulating bile acid to reduce phospholipid content.

Natural Phenylethanoid Supplementation Alleviates Metabolic Syndrome in Female Mice Induced by High-Fructose Diet

Tyrosol (T), hydroxytyrosol (H), and salidroside (S) are typical phenylethanoids and also powerful dietary antioxidants. This study aimed at evaluating the influence of three natural phenylethanoids, which are dietary phenylethanoids of natural origins, on reversing gut dysbiosis and attenuating nonalcoholic fatty liver features of the liver induced by metabolic syndrome (MetS) mice. C57BL/6J female mice induced with high-fructose diet were established and administrated with salidroside, tyrosol, and hydroxytyrosol for 12 weeks, respectively. Biochemical analysis showed that S, T, and H significantly improved glucose metabolism and lipid metabolism, including reduced levels of total cholesterol insulin (INS), uric acid, low-density lipoprotein cholesterol (LDL-C), and aspartate aminotransferase (ALT). Histopathological observation of the liver confirmed the protective effects of S, T, and H against hepatic steatosis, which were demonstrated by the results of metabolomic analysis, such as the improvement in glycolysis, purine metabolism, bile acid, fatty acid metabolism, and choline metabolism. Additionally, 16S rRNA gene sequence data revealed that S, T, and H could enhance the diversity of gut microbiota. These findings suggested that S, T, and H probably suppress lipid accumulation and have hepatoprotective effects and improve intestinal microflora disorders to attenuate metabolic syndromes.

Comparative Evaluation of Plasma Metabolomic Data from Multiple Laboratories

In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.

Serum glutamate and glutamine-to-glutamate ratio are associated with coronary angiography defined coronary artery disease

BACKGROUND AND AIMS

Serum concentrations of glutamate (Glu), Glutamine (Gln) and Gln/Glu ratio have consistently been reported to be associated with metabolic disorders and diabetes. The aim of this study was to examine the relationship between these metabolites with the presence of coronary artery disease (CAD) and CAD severity in Chinese patients.

METHODS AND RESULTS

2970 Chinese patients undergoing coronary angiography (CAG) in Beijing Hospital were enrolled. Baseline demographics and medical history data was recorded by questionnaires. Serum Glu and Gln concentrations were analyzed by isotope dilution liquid chromatography-tandem mass spectrometry (LC-MS/MS). Statistical analysis showed that CAD patients had significantly higher levels of Glu and lower Gln/Glu ratios compared with non-CAD control group. Glu was significantly positively associated with body mass index (BMI), fasting blood glucose (FBG), triglycerides (TG), creatinine (Crea), and uric acid (UA), and negatively associated with high-density lipoprotein cholesterol (HDL-C), while inverse associations between Gln/Glu ratio and these risk factors were observed. Glu levels increased and Gln/Glu decreased with the increase of CAD severity as represented by either the number of stenosed vessels or the Gensini scores. Logistic regression analysis demonstrated that, after adjusting for smoking status, obesity or overweight, hypertension, dyslipidemia, diabetes, stroke and family history of premature CAD, high Glu level and low Gln/Glu ratio were positively associated with CAG defined CAD as well as CAD severity expressed by Gensini score.

CONCLUSIONS

We identified Glu and Gln/Glu ratio independently associated with CAG defined CAD as well as CAD severity in Chinese patients undergoing CAG.

Metabolite G-Protein Coupled Receptors in Cardio-Metabolic Diseases

G protein-coupled receptors (GPCRs) have originally been described as a family of receptors activated by hormones, neurotransmitters, and other mediators. However, in recent years GPCRs have shown to bind endogenous metabolites, which serve functions other than as signaling mediators. These receptors respond to fatty acids, mono- and disaccharides, amino acids, or various intermediates and products of metabolism, including ketone bodies, lactate, succinate, or bile acids. Given that many of these metabolic processes are dysregulated under pathological conditions, including diabetes, dyslipidemia, and obesity, receptors of endogenous metabolites have also been recognized as potential drug targets to prevent and/or treat metabolic and cardiovascular diseases. This review describes G protein-coupled receptors activated by endogenous metabolites and summarizes their physiological, pathophysiological, and potential pharmacological roles.

A brain proteomic signature of incipient Alzheimer's disease in young APOE ε4 carriers identifies novel drug targets

Aptamer-based proteomics revealed differentially abundant proteins in Alzheimer’s disease (AD) brains in the Baltimore Longitudinal Study of Aging and Religious Orders Study (mean age, 89 ± 9 years). A subset of these proteins was also differentially abundant in the brains of young APOE ε4 carriers relative to noncarriers (mean age, 39 ± 6 years). Several of these proteins represent targets of approved and experimental drugs for other indications and were validated using orthogonal methods in independent human brain tissue samples as well as in transgenic AD models. Using cell culture–based phenotypic assays, we showed that drugs targeting the cytokine transducer STAT3 and the Src family tyrosine kinases, YES1 and FYN, rescued molecular phenotypes relevant to AD pathogenesis. Our findings may accelerate the development of effective interventions targeting the earliest molecular triggers of AD.

Study of Longitudinal Aging in Mice: Presentation of Experimental Techniques

Aging is associated with functional and metabolic decline and is a risk factor for all noncommunicable diseases. Even though mice are routinely used for modeling human aging and aging-related conditions, no comprehensive assessment to date has been conducted on normative mouse aging. To address this gap, the Study of Longitudinal Aging in Mice (SLAM) was designed and implemented by the National Institute on Aging (NIA/NIH) as the mouse counterpart to the Baltimore Longitudinal Study of Aging (BLSA). In this manuscript, we describe the premise, study design, methodologies, and technologies currently employed in SLAM. We also discuss current and future study directions. In this large population mouse study, inbred C57BL/6J and outbred UM-HET3 mice of both sexes are longitudinally evaluated for functional, phenotypic, and biological health, and collection of biospecimens is conducted throughout their life span. Within the longitudinal cohorts, a cross-sectional arm of the study has also been implemented for the well-controlled collection of tissues to generate a biorepository. SLAM and studies stemming from SLAM seek to identify and characterize phenotypic and biological predictors of mouse aging and age-associated conditions, examine the degrees of functional and biomolecular variability that occur within inbred and genetically heterogeneous mouse populations with age, and assess whether these changes are consistent with alterations observed in human aging in BLSA. The findings from these studies will be critical for evaluating the utility of mouse models for studying different aspects of aging, both in terms of interpreting prior findings and designing and implementing future studies.

Abnormal brain cholesterol homeostasis in Alzheimer's disease-a targeted metabolomic and transcriptomic study

The role of brain cholesterol metabolism in Alzheimer's disease (AD) remains unclear. Peripheral and brain cholesterol levels are largely independent due to the impermeability of the blood brain barrier (BBB), highlighting the importance of studying the role of brain cholesterol homeostasis in AD. We first tested whether metabolite markers of brain cholesterol biosynthesis and catabolism were altered in AD and associated with AD pathology using linear mixed-effects models in two brain autopsy samples from the Baltimore Longitudinal Study of Aging (BLSA) and the Religious Orders Study (ROS). We next tested whether genetic regulators of brain cholesterol biosynthesis and catabolism were altered in AD using the ANOVA test in publicly available brain tissue transcriptomic datasets. Finally, using regional brain transcriptomic data, we performed genome-scale metabolic network modeling to assess alterations in cholesterol biosynthesis and catabolism reactions in AD. We show that AD is associated with pervasive abnormalities in cholesterol biosynthesis and catabolism. Using transcriptomic data from Parkinson's disease (PD) brain tissue samples, we found that gene expression alterations identified in AD were not observed in PD, suggesting that these changes may be specific to AD. Our results suggest that reduced de novo cholesterol biosynthesis may occur in response to impaired enzymatic cholesterol catabolism and efflux to maintain brain cholesterol levels in AD. This is accompanied by the accumulation of nonenzymatically generated cytotoxic oxysterols. Our results set the stage for experimental studies to address whether abnormalities in cholesterol metabolism are plausible therapeutic targets in AD.

The prebiotic effects of omega-3 fatty acid supplementation: A six-week randomised intervention trial

Prebiotics are compounds in food that benefit health via affecting the gut microbiome. Omega-3 fatty acids have been associated with differences in gut microbiome composition and are widely accepted to have health benefits, although recent large trials have been inconclusive. We carried out a 6-week dietary intervention comparing the effects of daily supplementation with 500 mg of omega-3 versus 20 g of a well-characterized prebiotic, inulin. Inulin supplementation resulted in large increases in Bifidobacterium and Lachnospiraceae. In contrast, omega-3 supplementation resulted in significant increases in Coprococcus spp. and Bacteroides spp, and significant decreases in the fatty-liver associated Collinsella spp. On the other hand, similar to the results with inulin supplementation which resulted in significant increases in butyrate, iso-valerate, and iso-butyrate (p < .004), omega-3 supplementation resulted in significant increases in iso-butyrate and isovalerate (p < .002) and nearly significant increases in butyrate (p < .053). Coprococcus, which was significantly increased post-supplementation with omega-3, was found to be positively associated with iso-butyric acid (Beta (SE) = 0.69 (0.02), P = 1.4 x 10-3) and negatively associated with triglyceride-rich lipoproteins such as VLDL (Beta (SE) = -0.381 (0.01), P = .001) and VLDL-TG (Beta (SE) = -0.372 (0.04), P = .001) after adjusting for confounders. Dietary omega-3 alters gut microbiome composition and some of its cardiovascular effects appear to be potentially mediated by its effect on gut microbial fermentation products indicating that it may be a prebiotic nutrient.

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.