Cardiometabolic diseases-including Type 2 diabetes and obesity-remain leading causes of global mortality. Recent advancements in metabolomics have facilitated the identification of metabolites that are integral to the development of insulin resistance, a characteristic feature of cardiometabolic disease. Key metabolites, such as branched-chain amino acids (BCAAs), ceramides, glycine, and glutamine, have emerged as valuable biomarkers for early diagnosis, risk stratification, and potential therapeutic targets. Elevated BCAAs and ceramides are strongly associated with insulin resistance and Type 2 diabetes, whereas glycine exhibits an inverse relationship with insulin resistance, making it a promising therapeutic target. Metabolites involved in energy stress, including ketone bodies, lactate, and nicotinamide adenine dinucleotide (NAD⁺), regulate insulin sensitivity and metabolic health, with ketogenic diets and NAD⁺ precursor supplementation showing potential benefits. Additionally, the novel biomarker N-lactoyl-phenylalanine further underscores the complexity of metabolic regulation and its therapeutic potential. This review underscores the potential of metabolite-based diagnostics and precision medicine, which could enhance efforts in the prevention, diagnosis, and treatment of cardiometabolic diseases, ultimately improving patient outcomes and quality of life.

Our aim was to study the metabolic effects of eight weeks of high-intensity interval training (HIIT) on the liver of rats with type 2 diabetes (T2D) using untargeted metabolomics. Twenty male Wistar rats, were divided into four groups (n = 5 per group): control (CTL), type 2 diabetes (DB), HIIT (EX), and type 2 diabetes + HIIT (DTX). A two months of a high-fat diet followed by a single dose of streptozotocin (35 mg/kg body weight) was used to induce T2D. Animals in the EX and DTX groups were trained for eight weeks (5 times per week, 4-10 running intervals at 80-100% of their maximum velocity). Metabolomic data were collected using proton nuclear magnetic resonance (¹H-NMR) to assess metabolic changes in the liver after training. Data were then pre-processed using ProMetab (MATLAB) for baseline correction, normalisation and binning. Fasting blood glucose (FBG) levels were analysed using a repeated-measures mixed ANOVA [i.e., time as the within-subject factor (Baseline - Month 0, Post-induction - Month 2, and Post-intervention - Month 4) and gruop (CTL, DB, HIIT, DTX) as the between-subject factor]. A one-way ANOVA with Tukey's post hoc test (p < 0.05) was applied to assess differences in Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Multivariate analysis - using sparse partial least squares discriminant analysis (sPLS-DA) - was performed to identify key metabolites, followed by pathway analysis (MetaboAnalyst) to determine significantly affected metabolic pathways. DB group showed higher HOMA-IR than CTL and DTX groups (p < 0.05). Furthermore, distinct clustering patterns was shown for metabolites by multivariate analysis. Key altered metabolic pathways included valine, leucine, and isoleucine biosynthesis; glutathione metabolism; pantothenate and coenzyme A biosynthesis; fructose and mannose metabolism; glycine, serine, and threonine metabolism; cysteine and methionine metabolism; arginine biosynthesis; tyrosine metabolism; histidine metabolism; beta-alanine metabolism; propanoate metabolism; glycolysis/gluconeogenesis; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine and proline metabolism; and thiamine metabolism. These results suggest that eight weeks of HIIT may reverse metabolic changes induced by T2D in the rat liver, potentially contributing to reduced FBG and HOMA-IR levels. Clinical trial number: Not applicable.

Swertiamarin is a major component of many traditional Chinese Swertia herbs that show significant antidiabetic activity. (R)-Gentiandiol and (S)-gentiandiol are metabolites of swertiamarin found in vivo. The antidiabetic activity of swertiamarin and its nitrogen-containing metabolites (R)-gentiandiol and (S)-gentiandiol was evaluated in this research, and their mechanism of action was investigated after evaluating the serum metabolic profile of KK/Upj-Ay type 2 mice. The pharmaceutical effects of swertiamarin, (R)-gentiandiol, and (S)-gentiandiol were tested by biochemical indices and histopathological observations. Moreover, the mechanism underlying the action of three compounds against type 2 diabetes was elucidated using a metabolomic method. It was shown that (R)-gentiandiol significantly improved pathological changes in the kidney and pancreas. The levels of total cholesterol, triglyceride, and high-density and low-density lipoprotein cholesterol improved considerably after treatment with (R)-gentiandiol, compared to their levels in model mice. However, the levels of these compounds showed no improvement after treatment with (S)-gentiandiol. In total, 15 biomarkers were identified in KK/Upj-Ay type 2 mice, and the levels of 10 biomarkers were measured after treatment with (R)-gentiandiol. (R)-Gentiandiol reduced the abnormalities in metabolic pathways, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and nucleotide metabolism. Additionally, glycine, serine, and threonine metabolism related to the regulation of glycine was affected the most. The study indicated that the antidiabetic effects of Swertia herbs may due to (R)-gentiandiol which is a metabolite of swertiamarin in vivo. This study helps clarify the active metabolites of swertiamarin, provide greater insights into the clinical antidiabetic effects of Swertia herbs and bring novel ideas for developing new drugs from antidiabetic herbs.

Amino acids are fundamental in several metabolic processes, and their levels can reflect metabolism impairments that contribute to obesity and related diseases. Our objective was to identify a urinary amino acid fingerprint in obese and overweight children in prepuberty and to correlate this profile with cardiometabolic alterations.

The study included 110 children, boys and girls aged 9-10 years, they were classified according to their BMI-for-age (Body Mass Index for age) into three groups: normal weight (NW) (n = 45), overweight (OW) (n = 21), and obese (OB) (n = 44). The 12-h urine samples were analyzed by LC-MS/MS to quantify 47 amino acids using the Amino Acids Analysis Kit (Zivak®, Turkey), values were corrected by creatinine concentration. Anthropometric measurements, cardiovascular parameters, and biochemical profiles were assessed following standard protocols.

When compared to NW, anthropometric measures, systolic and diastolic blood pressure, and serum uric acid levels were progressively elevated in the OW and OB groups. The OB group was characterized by elevated alpha-aminoadipic acid, asparagine, cystathionine, 1-methyl-histidine, serine, tryptophan, phenylalanine, and tyrosine. In contrast, the OW group presented the most expressive levels of glutamine, alpha-diaminopimelic, and sarcosine.

Our findings indicate that obese and overweight children exhibit a particular urinary amino acid fingerprint which is similar to that reported in studies with plasma. The altered amino acids, particularly tyrosine, are frequently associated with impairments in glucose homeostasis, insulin resistance, and diabetes mellitus type 2. Potential mechanisms for increasing the levels of these amino acids in excess of weight may include enhanced protein degradation and impaired oxidative metabolism.

Patients with inflammatory bowel diseases (IBD), including Crohn's disease (CD), are at risk of complications, including kidney disease. It is important to identify IBD patients at higher risk of chronic kidney disease (CKD) to improve prevention and treatment. Here, we investigated the clinical and metabolomic characteristics of CD patients who develop CKD.

We identified adult CD patients with (CD + CKD, n = 87) and selected CD patients without CKD (CD controls) matched by age, race, and gender. We collected data on demographic characteristics (age, smoking status, ethnicity, gender), IBD characteristics (diagnosis, Montreal classification, medication use, IBD-related surgeries, perianal disease), and kidney-related factors (primary sclerosing cholangitis, end-stage renal disease, hypertension, diabetes, organ transplantation, and nephrolithiasis). Univariate and multivariate analyses were conducted and odds ratios were calculated to identify risk factors for CKD. Serum samples were collected for untargeted metabolomic analysis.

Chronic kidney disease was far more common in CD patients than UC patients. Crohn's disease patients with kidney stones had a 10-fold higher risk of developing CKD than those without kidney stones. Crohn's disease patients with more than 2 IBD-related surgeries had a 7.3-fold higher risk of developing CKD than those who had not undergone surgery. There was no relationship between the number of biologics used or mesalamine use and the risk of CKD. The serum of CD + CKD patients had elevated levels of pro-inflammatory metabolites and those linked to kidney injury.

We recommend regular kidney function monitoring and ensuring proper hydration to prevent or manage potential kidney-related complications in CD patients. Patients with resections and kidney stones are particularly vulnerable.

Diabetes mellitus is a chronic condition caused by high blood glucose resulting from insufficient insulin production or cellular resistance to insulin action or both. It is one of the fastest-growing public health concerns worldwide. Development of long-term nephropathy, retinopathy, neuropathy, and cardiovascular disease are some of the complications commonly associated with poor blood glycemic control. Type 2 diabetes mellitus (T2DM), the most prevalent type of diabetes, accounts for around 95 % of all cases globally. Although middle-aged or older adults are more likely to develop T2DM, its prevalence has grown in children and young people due to increased obesity, sedentary lifestyle and poor nutrition. Furthermore, it is believed that more than 50 % of cases go undiagnosed annually. Routine screening is essential to ensure early detection and reduce risk of life-threatening complications. Herein, we review traditional biomarkers and highlight the ongoing pursuit of novel and efficacious biomarkers driven by the objective of achieving early, precise and prompt diagnoses. It is widely acknowledged that individual biomarkers will inevitably have certain limitations necessitating the need for integrating multiple markers in screening.

From a detailed review of 90 experimental and clinical metabolomic investigations of obesity and metabolic dysfunction-associated steatotic liver disease (MASLD), we have developed metabolomic hallmarks for both obesity and MASLD. Obesity studies were conducted in mice, rats, and humans, with consensus biomarker groups in plasma/serum being essential and nonessential amino acids, energy metabolites, gut microbiota metabolites, acylcarnitines and lysophosphatidylcholines (LPC), which formed the basis of the six metabolomic hallmarks of obesity. Additionally, mice and rats shared elevated cholesterol, humans and rats shared elevated fatty acids, and humans and mice shared elevated VLDL/LDL, bile acids and phosphatidylcholines (PC). MASLD metabolomic studies had been performed in mice, rats, hamsters, cows, geese, blunt snout breams, zebrafish, and humans, with the biomarker groups in agreement between experimental and clinical investigations being energy metabolites, essential and nonessential amino acids, fatty acids, and bile acids, which lay the foundation of the five metabolomic hallmarks of MASLD. Furthermore, the experimental group had higher LPC/PC and cholesteryl esters, and the clinical group had elevated acylcarnitines, lysophosphatidylethanolamines/phosphatidylethanolamines (LPE/PE), triglycerides/diglycerides, and gut microbiota metabolites. These metabolomic hallmarks aid in the understanding of the metabolic role played by obesity in MASLD development, inform mechanistic studies into underlying disease pathogenesis, and are critical for new metabolite-inspired therapies.

Bariatric surgery induces significant weight loss, increases insulin sensitivity, and improves dyslipidemia. As one of the most widely performed bariatric surgeries, laparoscopic sleeve gastrectomy (LSG) is thought to improve the metabolic profile along with weight loss. The objective of this study was to evaluate longitudinal changes in the serum metabolite levels after LSG and elucidate the underlying mechanisms of metabolic improvement.

Clinical metabolic parameters and serum samples were collected preoperatively and at 1, 3, and 6 months postoperatively from nine patients with obesity undergoing LSG. Serum metabolites were measured using a non-targeted metabolic liquid chromatography-mass spectrometry method.

During the 1, 3, and 6 months postoperative follow-up, the body mass index, HOMA-IR, and liver fat content showed a gradual descending trend. A total of 328 serum metabolites were detected, and 38 were differentially expressed. The up-regulated metabolites were mainly enriched in ketone body metabolism, alpha-linolenic acid and linoleic acid metabolism, pantothenate and CoA biosynthesis, glycerolipid metabolism, and fructose and mannose degradation, while the down-regulated metabolites were closely related to caffeine metabolism, oxidation of branched-chain fatty acids, glutamate metabolism, and homocysteine degradation. Notably, nine metabolites (oxoglutarate, 2-ketobutyric acid, succinic acid semialdehyde, phthalic acid, pantetheine, eicosapentaenoate, 3-hydroxybutanoate, oxamic acid, and dihydroxyfumarate) showed persistent differential expression at 1, 3, and 6 months follow-up. Some were found to be significantly associated with weight loss, insulin resistance improvement, and liver fat content reduction.

This finding may provide a new perspective for revealing novel biomarkers and mechanisms of metabolic improvement in obesity and related comorbidities.

Differences in metabolic regulation among obesity phenotypes, specifically metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) women, may lead to varied responses to interventions, which could be elucidated through metabolomics. Therefore, this study aims to investigate the differences in metabolite profiles between MHO and MUO women and the changes following a lifestyle intervention. Serum samples from 36 MHO and 34 MUO women who participated in a lifestyle intervention for weight loss were analysed using untargeted proton nuclear magnetic resonance spectroscopy (1H NMR) at baseline and 6 months post-intervention. Anthropometric, clinical, and dietary intake parameters were assessed at both time points. Both groups showed differential metabolite profiles at baseline and after six months. Seven metabolites, including trimethylamine-N-oxide (TMAO), arginine, ribose, aspartate, carnitine, choline, and tyrosine, significantly changed between groups post-intervention, which all showed a decreasing pattern in MHO. Significant reductions in body weight and body mass index (BMI) in the MUO correlated with changes in the carnitine and tyrosine levels. In conclusion, metabolite profiles differed significantly between MHO and MUO women before and after a lifestyle intervention. The changes in carnitine and tyrosine levels in MUO were correlated with weight loss, suggesting potential targets for therapeutic intervention.

Liraglutide, a long-acting glucagon-like peptide-1 receptor agonist (GLP1RA), is a well-established anti-diabetic drug, has also been approved for the treatment of obesity at a dose of 3 mg. There are a limited number of studies in the literature that have looked at changes in metabolite levels before and after liraglutide treatment in patients with obesity. To this end, in the present study we aimed to explore the changes in the plasma metabolomic profile, using liquid chromatography-high resolution mass spectrometry (LC-HRMS) in patients with obesity.

A single-center prospective study was undertaken to evaluate the effectiveness of 3 mg liraglutide therapy in twenty-three patients (M/F: 8/15) with obesity, mean BMI 40.81 ± 5.04 kg/m2, and mean age of 36 ± 10.9 years, in two groups: at baseline (pre-treatment) and after 12 weeks of treatment (post-treatment). An untargeted metabolomic profiling was conducted in plasma from the pre-treatment and post-treatment groups using LC-HRMS, along with bioinformatics analysis using ingenuity pathway analysis (IPA).

The metabolomics analysis revealed a significant (FDR p-value ≤ 0.05, FC 1.5) dysregulation of 161 endogenous metabolites (97 upregulated and 64 downregulated) with distinct separation between the two groups. Among the significantly dysregulated metabolites, the majority of them were identified as belonging to the class of oxidized lipids (oxylipins) that includes arachidonic acid and its derivatives, phosphorglycerophosphates, N-acylated amino acids, steroid hormones, and bile acids. The biomarker analysis conducted using MetaboAnalyst showed PGP (a21:0/PG/F1alpha), an oxidized lipid, as the first metabolite among the list of the top 15 biomarkers, followed by cysteine and estrone. The IPA analysis showed that the dysregulated metabolites impacted the pathway related to cell signaling, free radical scavenging, and molecular transport, and were focused around the dysregulation of NF-κB, ERK, MAPK, PKc, VEGF, insulin, and pro-inflammatory cytokine signaling pathways.

The findings suggest that liraglutide treatment reduces inflammation and modulates lipid metabolism and oxidative stress. Our study contributes to a better understanding of the drug's multifaceted impact on overall metabolism in patients with obesity.

Liraglutide, a glucagon-like peptide-1 receptor agonist, is effective in the treatment of type 2 diabetes mellitus (T2DM) and obesity. Despite its benefits, including improved glycemic control and weight loss, the common metabolic changes induced by liraglutide and correlations between those in rodents and humans remain unknown. Here, we used advanced machine learning techniques to analyze the plasma metabolomic data in diet-induced obese (DIO) mice and patients with T2DM treated with liraglutide. Among the machine learning models, Support Vector Machine was the most suitable for DIO mice, and Gradient Boosting was the most suitable for patients with T2DM. Through the cross-evaluation of machine learning models, we found that liraglutide promotes metabolic shifts and interspecies correlations in these shifts between DIO mice and patients with T2DM. Our comparative analysis helped identify metabolic correlations influenced by liraglutide between humans and rodents and may guide future therapeutic strategies for T2DM and obesity.

Dietary constituents modulate development of obesity and type 2 diabetes. The metabolic impact from different food sources in western diets (WD) on obesity development is not fully elucidated. This study aims to identify dietary sources that differentially affect obesity development and the metabolic processes involved.

Mice were fed isocaloric WDs with protein and fat from different food groups, including egg and dairy, terrestrial meat, game meat, marine, vegetarian, and a mixture of all. This study evaluates development of obesity, glucose tolerance, insulin sensitivity, and plasma and cecal metabolome. WD based on marine or vegetarian food sources protects male mice from obesity development and insulin resistance, whereas meat-based diets promote obesity. The intake of different food sources induces marked differences in the lipid-related plasma metabolome, particularly impacting phosphatidylcholines. Fifty-nine lipid-related plasma metabolites are positively associated with adiposity and a distinct cecal metabolome is found in mice fed a marine diet.

This study demonstrates differences in obesity development between the food groups. Diet specific metabolomic signatures in plasma and cecum associated with adiposity, where a marine based diet modulates the level of plasma and cecal phosphatidylcholines in addition to preventing obesity development.

Henoch-Schönlein purpura nephritis (HSPN) is the most severe manifestation of Henoch-Schönlein purpura (HSP). This study aimed to determine the role of urine metabolomics in predicting HSPN and explore the potential mechanisms of HSP. A liquid chromatography-tandem mass spectrometry-based untargeted metabolomics analysis was performed to investigate the urinary metabolic profiles of 90 participants, comprising 30 healthy children (group CON) and 60 patients with HSP, including 30 HSP patients without renal involvement (group H) and 30 HSPN patients (group HSPN). The differentially expressed metabolites (DEMs) were identified using orthogonal partial least squares discriminant analysis (OPLS-DA), and subsequent bioinformatics analysis was conducted to elucidate the perturbed metabolic pathways. A total of 43 DEMs between H and HSPN groups were analyzed by the Kyoto Encyclopedia of Gene and Genome (KEGG) database, and the result indicates that glycine, serine and threonine metabolism, and cysteine and methionine metabolism were significantly disturbed. A composite model incorporating propionylcarnitine and indophenol sulfate was developed to assess the risk of renal involvement in pediatric patients with HSP.   Conclusion: This study reveals the metabolic alterations in healthy children, HSPN patients, and HSP patients without renal involvement. Furthermore, propionylcarnitine and indophenol sulfate may be potential predictive biomarkers of the occurrence of HSPN. What is Known: • HSP is the predominant type of vasculitis observed in children. The long-term prognosis of HSP is contingent upon the extent of renal impairment. In severe nephritis, a delay in appropriate treatment may lead to fibrosis progression and subsequent development of chronic kidney disease (CKD), even leading to renal failure. • The application of metabolomics in investigating diverse renal disorders has been documented. Urine is a robust and sensitive medium for metabolomics detection. What is New: • The metabolic profiles were identified in urine samples of healthy children and those with HSP at the early stage of the disease. Different metabolites were identified between HSP patients without nephritis and those who developed HSPN. • These different metabolites may affect oxidative stress in the progression of HSPN.

This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques.

Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D.

As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures.

ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites.

Prenatal exposure to ubiquitous endocrine-disrupting chemicals (EDCs) may increase the risk of metabolic syndrome (MetS) in children, but few studies have studied chemical mixtures or explored underlying protein and metabolic signatures.

To investigate associations of prenatal exposure to EDC mixtures with MetS risk score in children and identify associated proteins and metabolites.

This population-based, birth cohort study used data collected between April 1, 2003, and February 26, 2016, from the Human Early Life Exposome cohort based in France, Greece, Lithuania, Norway, Spain, and the UK. Eligible participants included mother-child pairs with measured prenatal EDC exposures and complete data on childhood MetS risk factors, proteins, and metabolites. Data were analyzed between October 2022 and July 2023.

Nine metals, 3 organochlorine pesticides, 5 polychlorinated biphenyls, 2 polybrominated diphenyl ethers (PBDEs), 5 perfluoroalkyl substances (PFAS), 10 phthalate metabolites, 3 phenols, 4 parabens, and 4 organophosphate pesticide metabolites measured in urine and blood samples collected during pregnancy.

At 6 to 11 years of age, a composite MetS risk score was constructed using z scores of waist circumference, systolic and diastolic blood pressures, triglycerides, high-density lipoprotein cholesterol, and insulin levels. Childhood levels of 44 urinary metabolites, 177 serum metabolites, and 35 plasma proteins were quantified using targeted methods. Associations were assessed using bayesian weighted quantile sum regressions applied to mixtures for each chemical group.

The study included 1134 mothers (mean [SD] age at birth, 30.7 [4.9] years) and their children (mean [SD] age, 7.8 [1.5] years; 617 male children [54.4%] and 517 female children [45.6%]; mean [SD] MetS risk score, -0.1 [2.3]). MetS score increased per 1-quartile increase of the mixture for metals (β = 0.44; 95% credible interval [CrI], 0.30 to 0.59), organochlorine pesticides (β = 0.22; 95% CrI, 0.15 to 0.29), PBDEs (β = 0.17; 95% CrI, 0.06 to 0.27), and PFAS (β = 0.19; 95% CrI, 0.14 to 0.24). High-molecular weight phthalate mixtures (β = -0.07; 95% CrI, -0.10 to -0.04) and low-molecular weight phthalate mixtures (β = -0.13; 95% CrI, -0.18 to -0.08) were associated with a decreased MetS score. Most EDC mixtures were associated with elevated proinflammatory proteins, amino acids, and altered glycerophospholipids, which in turn were associated with increased MetS score.

This cohort study suggests that prenatal exposure to EDC mixtures may be associated with adverse metabolic health in children. Given the pervasive nature of EDCs and the increase in MetS, these findings hold substantial public health implications.

Atherosclerosis is a dominant cause of cardiovascular disease (CVD), including myocardial infarction and stroke.

To investigate metabolic states that are associated with the development of atherosclerosis.

Cross-sectional cohort study at a university hospital in the Netherlands. A total of 302 adult subjects with a body mass index (BMI) ≥ 27 kg/m2 were included. We integrated plasma metabolomics with clinical metadata to quantify the "atherogenic state" of each individual, providing a continuous spectrum of atherogenic states that ranges between nonatherogenic states to highly atherogenic states.

Analysis of groups of individuals with different clinical conditions-such as metabolically healthy individuals with obesity, and individuals with metabolic syndrome-confirmed the generalizability of this spectrum; revealed a wide variation of atherogenic states within each condition; and allowed identification of metabolites that are associated with the atherogenic state regardless of the particular condition, such as gamma-glutamyl-glutamic acid and homovanillic acid sulfate. The analysis further highlighted metabolic pathways such as catabolism of phenylalanine and tyrosine and biosynthesis of estrogens and phenylpropanoids. Using validation cohorts, we confirmed variation in atherogenic states in healthy subjects (before atherosclerosis plaques become visible), and showed that metabolites associated with the atherogenic state were also associated with future CVD.

Our results provide a global view of atherosclerosis risk states using plasma metabolomics.

Obesity is a progressive metabolic disease that begins with lipid metabolism disorders. Aromatic amino acids (AAAs), including tryptophan, phenylalanine, and tyrosine, have diverse biological activities as nutrients. However, the underlying mechanisms by which AAAs affect lipid metabolism are unclear.

This study was designed to investigate the possible roles and underlying molecular mechanisms of AAA in the pathogenesis of lipid metabolism disorders.

We added an AAA mixture to the high-fat diet (HFD) of mice. Glucose tolerance test was recorded. Protein expression of hepatic bile acid (BA) synthase and mRNA expression of BA metabolism-related genes were determined. Hepatic BA profiles and gut microbial were also determined in mice.

The results showed that AAA significantly increased body weight and white adipose tissue, aggravated liver injury, impaired glucose tolerance and intestinal integrity, and significantly increased hepatic BA synthesis by inhibiting intestinal farnesoid X receptor (FXR). Moreover, AAA increased the content of total BA in the liver and altered the hepatic BA profile, with elevated levels of lithocholic acid, glycochenodeoxycholic acid, and glycoursodeoxycholic acid. AAA markedly increased the levels of proteins involved in BA synthesis (cholesterol 7α-hydroxylase and oxysterol 7α-hydroxylase) and inhibited the intestinal FXR. Gut microbial composition also changed, reducing the abundance of some beneficial bacteria, such as Parvibacter and Lactobacillus.

Under HFD conditions, AAAs stimulate BA synthesis in both the classical and alternative pathways, leading to aggravation of liver injury and fat deposition. Excessive intake of AAA disrupts BA metabolism and contributes to the development of lipid metabolism disorders, suggesting that AAA may be a causative agent of lipid metabolism disorders.

Small noncoding RNAs (sncRNAs) are implicated in age-associated pathologies, including sarcopenia and insulin resistance (IR). As potential circulating biomarkers, most studies have focussed on microRNAs (miRNAs), one class of sncRNA. This study characterized the wider circulating sncRNA transcriptome of older individuals and associations with sarcopenia and IR. sncRNA expression including miRNAs, transfer RNAs (tRNAs), tRNA-associated fragments (tRFs), and piwi-interacting RNAs (piRNAs) was measured in serum from 21 healthy and 21 sarcopenic Hertfordshire Sarcopenia Study extension women matched for age (mean 78.9 years) and HOMA2-IR. Associations with age, sarcopenia and HOMA2-IR were examined and predicted gene targets and biological pathways characterized. Of the total sncRNA among healthy controls, piRNAs were most abundant (85.3%), followed by tRNAs (4.1%), miRNAs (2.7%), and tRFs (0.5%). Age was associated (FDR < 0.05) with 2 miRNAs, 58 tRNAs, and 14 tRFs, with chromatin organization, WNT signaling, and response to stress enriched among gene targets. Sarcopenia was nominally associated (p < .05) with 12 tRNAs, 3 tRFs, and 6 piRNAs, with target genes linked to cell proliferation and differentiation such as Notch Receptor 1 (NOTCH1), DISC1 scaffold protein (DISC1), and GLI family zinc finger-2 (GLI2). HOMA2-IR was nominally associated (p<0.05) with 6 miRNAs, 9 tRNAs, 1 tRF, and 19 piRNAs, linked with lysine degradation, circadian rhythm, and fatty acid biosynthesis pathways. These findings identify changes in circulating sncRNA expression in human serum associated with chronological age, sarcopenia, and IR. These may have clinical utility as circulating biomarkers of ageing and age-associated pathologies and provide novel targets for therapeutic intervention.

Several studies have examined metabolomic profiles in relation to Alzheimer's disease and related dementia (AD/ADRD) risk; however, few studies have focused on minorities, such as Latinos, or examined Magnetic-Resonance Imaging (MRI)-based outcomes.

We used multiple linear regression, adjusted for covariates, to examine the association between metabolite concentration and MRI-derived brain age deviation. Metabolites were measured at baseline with untargeted metabolomic profiling (Metabolon, Inc). Brain age deviation (BAD) was calculated at wave 4 (~ 9 years from Boston Puerto Rican Health Study (BPRHS) baseline) as chronologic age, minus MRI-estimated brain age, representing the rate of biological brain aging relative to chronologic age. We also examined if metabolites associated with BAD were similarly associated with hippocampal volume and global cognitive function at wave 4 in the BPRHS.

Several metabolites, including isobutyrylcarnitine, propionylcarnitine, phenylacetylglutamine, phenylacetylcarnitine (acetylated peptides), p-cresol-glucuronide, phenylacetylglutamate, and trimethylamine N-oxide (TMAO) were inversely associated with brain age deviation. Taurocholate sulfate, a bile salt, was marginally associated with better brain aging. Most metabolites with negative associations with brain age deviation scores also were inversely associations with hippocampal volumes and wave 4 cognitive function.

The metabolites identifiedin this study are generally consistent with prior literature and highlight the role of BCAA, TMAO and microbially derived metabolites in cognitive decline.

Diet-based models are commonly used to investigate obesity and related disorders. We conducted a comparative profiling of three obesogenic diets HFD, high fat diet; HFHF, high fat high fructose diet; and HFCD, high fat choline deficient diet to assess their impact on the gut microbiome and metabolome. After 20 weeks, we analyzed the gut microbiota and metabolomes of liver, plasma, cecal, and fecal samples. Fecal and plasma bile acids (BAs) and fecal short-chain fatty acids (SCFAs) were also examined. Significant changes were observed in fecal and cecal metabolites, with increased Firmicutes and decreased Bacteroidetes in the HFD, HFHF, and HFCD-fed mice compared to chow and LFD (low fat diet)-fed mice. Most BAs were reduced in plasma and fecal samples of obese groups, except taurocholic acid, which increased in HFCD mice's plasma. SCFAs like acetate and butyrate significantly decreased in obesogenic diet groups, while propionic acid specifically decreased in the HFCD group. Pathway analysis revealed significant alterations in amino acid, carbohydrate metabolism, and nucleic acid biosynthesis pathways in obese mice. Surprisingly, even LFD-fed mice showed distinct changes in microbiome and metabolite profiles compared to the chow group. This study provides insights into gut microbiome dysbiosis and metabolite alterations induced by obesogenic and LFD diets in various tissues. These findings aid in selecting suitable diet models to study the role of the gut microbiome and metabolites in obesity and associated disorders, with potential implications for understanding similar pathologies in humans.

Introduction: Fuzhuan brick tea (FBT) is a worldwide popular beverage which has the appreciable potential in regulating glycometabolism. However, the reports on the hypoglycemic mechanism of FBT remain limited. Methods: In this study, the hypoglycemic effect of FBT was evaluated in a pharmacological experiment based on Kunming mice. Global metabolomics and network pharmacology were combined to discover the potential target metabolites and genes. In addition, the real-time quantitative polymerase chain reaction (RT-qPCR) analysis was performed for verification. Results: Seven potential target metabolites and six potential target genes were screened using the integrated approach. After RT-qPCR analysis, it was found that the mRNA expression of VEGFA, KDR, MAPK14, and PPARA showed significant differences between normal and diabetes mellitus mice, with a retracement after FBT treatment. Conclusion: These results indicated that the hypoglycemic effect of FBT was associated with its anti-inflammatory activities and regulation of lipid metabolism disorders. The exploration of the hypoglycemic mechanism of FBT would be meaningful for its further application and development.

Monochorionic, diamniotic (MCDA) monozygotic twins share nearly all genetic variation and a common placenta in utero. Despite this, MCDA twins are often discordant for a range of common phenotypes, including early growth and birth weight. As such, MCDA twins represent a unique model to explore variation in early growth attributable primarily to in utero environmental factors.

MCDA twins with a range of within-pair birth weight discordance were sampled from the peri/postnatal epigenetic twin study (PETS, Melbourne; n = 26 pairs), Beijing twin study (BTS, Beijing; n = 25), and the Chongqing longitudinal twin study (LoTiS, Chongqing; n = 22). All PETS participants were of European-Australian ancestry, while all Chinese participants had Han ancestry. The average of the birth weight difference between the larger and smaller co-twins for all twin pairs was determined and metabolomic profiles of amino acids, TCA cycle intermediates, fatty acids, organic acids, and their derivatives generated from cord blood plasma by gas chromatograph mass spectrometry. Within and between co-twin pair analyses were performed to identify metabolites specifically associated with discordance in birth weight. Multivariable regression and pathway enrichment analyses between different regions were performed to evaluate the geographical effects on the metabolism of MCDA twin pairs.

PETS twins showed a markedly different metabolic profile at birth compared to the two Chinese samples. Within-pair analysis revealed an association of glutathione, creatinine, and levulinic acid with birth weight discordance. Caffeine, phenylalanine, and several saturated fatty acid levels were uniquely elevated in PETS twins and were associated with maternal BMI and average within pair birth weight, in addition to birth weight discordance. LoTiS twins had higher levels of glutathione, tyrosine, and gamma-linolenic acid relative to PETS and BTS twins, potentially associated with eating habits.

This study highlights the potential role of underlying genetic variation (shared by MZ twins), in utero (non-shared by MZ twins) and location-specific (shared by MZ twins) environmental factors, in regulating the cord blood metabolome of uncomplicated MCDA twins. Future research is needed to unravel these complex relationships that may play a key role in phenotypic metabolic alterations of twins independent of genetic diversity.

The gastrointestinal microbiota plays a key role in the host physiology and health through a complex host-microbiota co-metabolism. Metabolites produced by microbial metabolism can travel through the bloodstream to reach distal organs and affect their function, ultimately influencing the development of relevant production traits such as meat quality. Meat quality is a complex trait made up of a number of characteristics and intramuscular fat content (IMF) is considered to be one of the most important parameters. In this study, 52 rabbits from 2 lines divergently selected for IMF (high-IMF (H) and low-IMF (L) lines) were used to perform an untargeted metabolomic analysis of their cecal content, with the aim to obtain information on genetically determined microbial metabolism related to IMF. A large, correlated response to selection was found in their cecal metabolome composition. Partial least squares discriminant analysis was used to identify the pathways differentiating the lines, which showed a classification accuracy of 99%. On the other hand, 2 linear partial least squares analyses were performed, one for each line, to extract evidence on the specific pathways associated with IMF deposition within each line, which showed predictive abilities (estimated using the Q2) of approximately 60%. The most relevant pathways differentiating the lines were those related to amino acids (aromatic, branched-chain, and gamma-glutamyl), secondary bile acids, and purines. The higher content of secondary bile acids in the L-line was related to greater lipid absorption, while the differences found in purines suggested different fermentation activities, which could be related to greater nitrogen utilization and energy efficiency in the L-line. The linear analyses showed that lipid metabolism had a greater relative importance for IMF deposition in the L-line, whereas a more complex microbial metabolism was associated with the H-line. The lysophospholipids and gamma-glutamyl amino acids were associated with IMF in both lines; the nucleotide and secondary bile acid metabolisms were mostly associated in the H-line; and the long-chain and branched-chain fatty acids were mostly associated in the L-line. A metabolic signature consisting of 2 secondary bile acids and 2 protein metabolites was found with 88% classification accuracy, pointing to the interaction between lipid absorption and protein metabolism as a relevant driver of the microbiome activity influencing IMF.

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Translating aging rejuvenation strategies into clinical practice has the potential to address the unmet needs of the global aging population. However, to successfully do so requires precise quantification of aging and its reversal in a way that encompasses the complexity and variation of aging.

Here, in a cohort of 113 healthy women, tiled in age from young to old, we identified a repertoire of known and previously unknown markers associated with age based on multimodal measurements, including transcripts, proteins, metabolites, microbes, and clinical laboratory values, based on which an integrative aging clock and a suite of customized aging clocks were developed.

A unified analysis of aging-associated traits defined four aging modalities with distinct biological functions (chronic inflammation, lipid metabolism, hormone regulation, and tissue fitness), and depicted waves of changes in distinct biological pathways peak around the third and fifth decades of life. We also demonstrated that the developed aging clocks could measure biological age and assess partial aging deceleration by hormone replacement therapy, a prevalent treatment designed to correct hormonal imbalances.

We established aging metrics that capture systemic physiological dysregulation, a valuable framework for monitoring the aging process and informing clinical development of aging rejuvenation strategies.

This work was supported by the National Natural Science Foundation of China (32121001), the National Key Research and Development Program of China (2022YFA1103700 and 2020YFA0804000), the National Natural Science Foundation of China (81502304), and the Quzhou Technology Projects (2022K46).

Objectives: Alström syndrome (ALMS) and Bardet-Biedl syndrome (BBS) are among the so-called ciliopathies and are associated with the development of multiple systemic abnormalities, including early childhood obesity and progressive neurodegeneration. Given the progressive deterioration of patients' quality of life, in the absence of defined causal treatment, it seems reasonable to identify the metabolic background of these diseases and search for their progression markers. The aim of this study was to find metabolites characteristic to ALMS and BBS, correlating with clinical course parameters, and related to the diseases progression. Methods: Untargeted metabolomics of serum samples obtained from ALMS and BBS patients (study group; n = 21) and obese/healthy participants (control group; each of 35 participants; n = 70) was performed using LC-QTOF-MS method at the study onset and after 4 years of follow-up. Results: Significant differences in such metabolites as valine, acylcarnitines, sphingomyelins, phosphatidylethanolamines, phosphatidylcholines, as well as lysophosphatidylethanolamines and lysophosphatidylcholines were observed when the study group was compared to both control groups. After a follow-up of the study group, mainly changes in the levels of lysophospholipids and phospholipids (including oxidized phospholipids) were noted. In addition, in case of ALMS/BBS patients, correlations were observed between selected phospholipids and glucose metabolism parameters. We also found correlations of several LPEs with patients' age (p < 0.05), but the level of only one of them (hexacosanoic acid) correlated negatively with age in the ALMS/BBS group, but positively in the other groups. Conclusion: Patients with ALMS/BBS have altered lipid metabolism compared to controls or obese subjects. As the disease progresses, they show elevated levels of lipid oxidation products, which may suggest increased oxidative stress. Selected lipid metabolites may be considered as potential markers of progression of ALMS and BBS syndromes.

The genetic and environmental determinants of serum propionylcarnitine concentrations (PC) remain largely unexplored. This study investigated the impact of genetic and environmental factors on serum propionylcarnitine levels in middle-aged and elderly participants from the Ansan/Ansung cohort of the Korean Genome and Epidemiology Study. Our goal was to understand the role of PC on the risk of metabolic syndrome (MetS) leading to cardiovascular disease, particularly concerning branched-chain amino acid (BCAA) metabolism. We analyzed participants' demographic, lifestyle, and biochemical data with and without MetS. Serum metabolite concentrations, including carnitine, acylcarnitine, and amino acid concentrations, were measured, and the components of MetS were evaluated. Genetic variants associated with low and high PC were selected using genome-wide association studies after adjusting for MetS-related parameters. Further, genetic variants and lifestyle factors that interacted with the polygenic risk score (PRS) were analyzed. Participants with MetS were older and less educated, and their alcohol intake was higher than non-MetS participants. PC was significantly associated with the MetS risk and increased the serum levels of BCAAs and other amino acids. Higher PC positively correlated with MetS components, insulin resistance, and cardiovascular risk factors. Intake of calcium, sodium, and vitamin D were inversely associated with PC, but coffee consumption was positively linked to PC. Multiple C2 And Transmembrane Domain Containing-1 (MCTP1)_rs4290997, Kinesin Family Member-7 (KIF7)_rs2350480, Coagulation Factor-II (F2)_rs2070850, Peroxisomal Biogenesis Factor-3 (PEX3)_rs223231, TBC1 Domain Family Member-22A (TBC1D22A)_rs910543, and Phospholipase A2 Group-IV-C (PLA2G4C)_rs7252136 interact with each other to have a threefold influence on PC. The PRS for the six-genetic variant model also interacted with age; the diet rich in beans, potato, and kimchi; and smoking status, influencing PC. In conclusion, elevated PC was associated with MetS and cardiovascular disease risk, suggesting their potential as disease biomarkers.

The epidemy of metabolic syndrome (MetS) is typically preceded by adoption of a "risky" lifestyle (e.g., dietary habit) among populations. Evidence shows that those with low socioeconomic status (SES) are at an increased risk for MetS. To investigate this, we recruited 123 obese subjects (body mass index [BMI] ≥ 30) from Chicago. Multi-omic data were collected to interrogate fecal microbiota, systemic markers of inflammation and immune activation, plasma metabolites, and plasma glycans. Intestinal permeability was measured using the sugar permeability testing. Our results suggest a heterogenous metabolic dysregulation among obese populations who are at risk of MetS. Systemic inflammation, linked to poor diet, intestinal microbiome dysbiosis, and gut barrier dysfunction may explain the development of MetS in these individuals. Our analysis revealed 37 key features associated with increased numbers of MetS features. These features were used to construct a composite metabolic-inflammatory (MI) score that was able to predict progression of MetS among at-risk individuals. The MI score was correlated with several markers of poor diet quality as well as lower levels of gut microbial diversity and abnormalities in several species of bacteria. This study reveals novel targets to reduce the burden of MetS and suggests access to healthy food options as a practical intervention.

Actinidia arguta leaves (AAL) are an excellent source of bioactive components for the food industry and possess many functional properties. However, the hypoglycemic effect and mechanism of AAL remain unclear. The aim of this work was to investigate the potential hypoglycemic effect of AAL and explore its possible mechanism using 16S rRNA sequencing and serum metabolomics in diabetic mice induced by high-fat feeding in combination with streptozotocin injection. A total of 25 flavonoids from AAL were isolated and characterized, and the contents of the extract from the AAL ranged from 0.14 mg/g DW to 8.97 mg/g DW. The compound quercetin (2) had the highest content of 8.97 ± 0.09 mg/g DW, and the compound kaempferol-3-O-(2'-O-D-glucopyl)-β-D-rutinoside (12) had the lowest content of 0.14 ± 0.01 mg/g DW. In vivo experimental studies showed that AAL reduced blood glucose and cholesterol levels, improved insulin sensitivity, and ameliorated oxidative stress and liver and kidney pathological damage. In addition, gut microbiota analysis found that AAL significantly reduced the F/B ratio, enriched the beneficial bacteria Bacteroides and Bifidobacterium, and inhibited the harmful bacteria Lactobacillus and Desulfovibrio, thereby playing an active role in intestinal imbalance. In addition, metabolomics analysis showed that AAL could improve amino acid metabolism and arachidonic acid metabolism, thereby exerting a hypoglycemic effect. This study confirmed that AAL can alleviate type 2 diabetes mellitus (T2DM) by regulating intestinal flora and interfering with related metabolic pathways, providing a scientific basis for its use as a dietary supplement and for further exploration of the mechanism of AAL against T2DM.

Plasma levels of the metabolite alpha-aminoadipic acid (2-AAA) have been associated with risk of type 2 diabetes (T2D) and atherosclerosis. However, little is known about the relationship of 2-AAA to other cardiometabolic risk markers in pre-disease states, or in the setting of comorbid disease.

We measured circulating 2-AAA using two methods in 1) a sample of 261 healthy individuals (2-AAA Study), and 2) in a sample of 134 persons comprising 110 individuals with treated HIV, with or without T2D, a population at high risk of metabolic disease and cardiovascular events despite suppression of circulating virus, and 24 individuals with T2D without HIV (HATIM Study). We examined associations between plasma 2-AAA and markers of cardiometabolic health within each cohort.

We observed differences in 2-AAA by sex and race in both cohorts, with higher levels observed in men compared with women, and in Asian compared with Black or white individuals (P<0.05). There was no significant difference in 2-AAA by HIV status within individuals with T2D in the HATIM Study. We confirmed associations between 2-AAA and dyslipidemia in both cohorts, where high 2-AAA associated with low HDL cholesterol (P<0.001) and high triglycerides (P<0.05). As expected, within the cohort of people with HIV, 2-AAA was higher in the setting of T2D compared to pre-diabetes or normoglycemia (P<0.001). 2-AAA was positively associated with body mass index (BMI) in the 2-AAA Study, and with waist circumference and measures of visceral fat volume in HATIM (all P<0.05). Further, 2-AAA associated with increased liver fat in persons with HIV (P<0.001). Our study confirms 2-AAA as a marker of cardiometabolic risk in both healthy individuals and those at high cardiometabolic risk, reveals relationships with adiposity and hepatic steatosis, and highlights important differences by sex and race. Further studies are warranted to establish molecular mechanisms linking 2-AAA to disease in other high-risk populations.

Obesity has been regarded as one of the major health problems worldwide. Studies demonstrated that ursolic acid (UA) can significantly ameliorate the progress of obesity. However, whether the effect of UA on obesity depends on the regulation of gut microbiota and metabolism is uncertain. To investigate the regulatory role of UA in obese mice from the perspective of intestinal microbiome and metabolomics analyses, an obese mice model was established with a high-fat diet, and the effect of UA on obesity was evaluated. The alterations of gut microbiota and metabolism related to obesity were evaluated by bioinformatic analysis. The results of the gut microbiota analysis showed that UA intervention could shift the Firmicutes to Bacteroidetes ratio at the phylum level and increase in the genera of Lactobacillus, Bacteroides, and Akkermansia. Additionally, metabolomics analysis showed that the beneficial influence of UA on obesity partly depended on amino acid metabolism. The current study demonstrated the roles of UA in the anti-obesity process, which depends in part on alterations in the gut microbiota and metabolism. Therefore, our findings highlight the potential therapeutic effect of UA on the improvement of diet-induced obesity in humans.

Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways.

We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed.

We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism.

Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.