Metabolite profiling identifies pathways associated with metabolic risk in humans

Relationship between plasma glutamate and cardiovascular disease risk in Chinese patients with type 2 diabetes mellitus by gender

Objectives

This study aimed to assess the association between plasma glutamate (Glu) and the risk of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM) and whether this association differs by gender.

Material and methods

We retrieved clinical information on 1032 consecutive patients with T2DM from a same tertiary care center from May 2015 to August 2016. Glu was quantified by liquid chromatography-tandem mass spectrometry analysis. Glu was converted into a categorical variable based on the median concentration in the whole population, while logistic regression was used to obtain the odds ratio (OR) and 95% confidence interval (CI), and the correlation between Glu and various biochemical indices was analyzed.

Results

We found that Glu was positively associated with the risk of CVD in patients with T2DM. This correlation was more significant in women. In T2DM patients, the higher the age, body mass index (BMI), weight and systolic blood pressure (SBP), the lower the glycosylated hemoglobin (HbA1C) concentration and the higher the Glu. In female patients, the correlation between age, weight, BMI, SBP, and plasma Triglycerides (TG), and Glu was also statistically significant.

Conclusion

In conclusion, female T2DM patients with high levels of Glu have a higher risk of developing CVD.

The association between acylcarnitine and amino acids profile and metabolic syndrome and its components in Iranian adults: Data from STEPs 2016

BACKGROUND

Evidence, albeit with conflicting results, has suggested that cardiometabolic risk factors, including obesity, type 2 diabetes (T2D), dyslipidemia, and hypertension, are highly associated with changes in metabolic signature, especially plasma amino acids and acylcarnitines levels. Here, we aimed to evaluate the association of circulating levels of amino acids and acylcarnitines with metabolic syndrome (MetS) and its components in Iranian adults.

METHODS

This cross-sectional study was performed on 1192 participants from the large-scale cross-sectional study of Surveillance of Risk Factors of non-communicable diseases (NCDs) in Iran (STEP 2016). The circulating levels of amino acids and acylcarnitines were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in individuals with MetS (n=529) and without MetS (n=663).

RESULTS

The higher plasma levels of branched-chain amino acids (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, and the ratio of Asp to Asn were significantly associated with MetS, whereas lower circulating levels of Gly, Ser, His, Asn, and citrulline were significantly associated with MetS. As for plasma levels of free carnitine and acylcarnitines, higher levels of short-chain acylcarnitines (C2, C3, C4DC), free carnitine (C0), and long-chain acylcarnitines (C16, C18OH) were significantly associated with MetS. Principal component analysis (PCA) showed that factor 3 (Tyr, Leu, Val, Met, Trp, Phe, Thr) [OR:1.165, 95% CI: 1.121-1.210, P<0.001], factor 7 (C0, C3, C4) [OR:1.257, 95% CI: 1.150-1.374, P<0.001], factor 8 (Gly, Ser) [OR:0.718, 95% CI: 0.651-0.793, P< 0.001], factor 9 (Ala, Pro, C4DC) [OR:1.883, 95% CI: 1.669-2.124, P<0.001], factor 10 (Glu, Asp, C18:2OH) [OR:1.132, 95% CI: 1.032-1.242, P= 0.009], factor 11 (citrulline, ornithine) [OR:0.862, 95% CI: 0.778-0.955, P= 0.004] and 13 (C18OH, C18:1 OH) [OR: 1.242, 95% CI: 1.042-1.480, P= 0.016] were independently correlated with metabolic syndrome.

CONCLUSION

Change in amino acid, and acylcarnitines profiles were seen in patients with MetS. Moreover, the alteration in the circulating levels of amino acids and acylcarnitines is along with an increase in MetS component number. It also seems that amino acid and acylcarnitines profiles can provide valuable information on evaluating and monitoring MetS risk. However, further studies are needed to establish this concept.

Metabolic Changes and Their Associations with Selected Nutrients Intake in the Group of Workers Exposed to Arsenic

Arsenic (As) exposure causes numerous adverse health effects, which can be reduced by the nutrients involved in the metabolism of iAs (inorganic As). This study was carried out on two groups of copper-smelting workers: WN, workers with a urinary total arsenic (tAs) concentration within the norm (n = 75), and WH, workers with a urinary tAs concentration above the norm (n = 41). This study aimed to analyze the association between the intake level of the nutrients involved in iAs metabolism and the signal intensity of the metabolites that were affected by iAs exposure. An untargeted metabolomics analysis was carried out on urine samples using liquid chromatography-mass spectrometry, and the intake of the nutrients was analyzed based on 3-day dietary records. Compared with the WN group, five pathways (the metabolism of amino acids, carbohydrates, glycans, vitamins, and nucleotides) with twenty-five putatively annotated metabolites were found to be increased in the WH group. In the WN group, the intake of nutrients (methionine; vitamins B2, B6, and B12; folate; and zinc) was negatively associated with six metabolites (cytosine, D-glucuronic acid, N-acetyl-D-glucosamine, pyroglutamic acid, uridine, and urocanic acid), whereas in the WH group, it was associated with five metabolites (D-glucuronic acid, L-glutamic acid, N-acetyl-D-glucosamine, N-acetylneuraminic acid, and uridine). Furthermore, in the WH group, positive associations between methionine, folate, and zinc intake and the signal intensity of succinic acid and 3-mercaptolactic acid were observed. These results highlight the need to educate the participants about the intake level of the nutrients involved in iAs metabolism and may contribute to further considerations with respect to the formulation of dietary recommendations for people exposed to iAs.

Branched-chain α-keto acids and glutamate/glutamine: Biomarkers of insulin resistance in childhood obesity

OBJECTIVES

Insulin resistance (IR) in adolescents with obesity is associated with a sex-dependent metabolic 'signature' comprising the branched-chain amino acids (BCAAs), glutamate/glutamine, C3/C5 acylcarnitines and uric acid. Here, we compared the levels of branched-chain α-keto acids (BCKAs) and glutamate/glutamine, which are the byproducts of BCAA catabolism and uric acid among adolescents with obesity prior to and following a 6-month lifestyle-intervention program.

METHODS

Fasting plasma samples from 33 adolescents with obesity (16 males, 17 females, aged 12-18 year) were analysed by flow-injection tandem MS and LC-MS/MS. Multiple linear regression models were used to correlate changes in BCKAs, glutamate/glutamine and uric acid with changes in weight and insulin sensitivity as assessed by HOMA-IR, adiponectin and the ratio of triglyceride (TG) to HDL. In predictive models, BCKAs, glutamate/glutamine and uric acid at baseline were used as explanatory variables.

RESULTS

Baseline BCKAs, glutamate/glutamine and uric acid were higher in males than females despite comparable BMI-metrics. Following lifestyle-intervention, α-keto-β-methylvalerate (α-KMV, a metabolic by product of isoleucine) decreased in males but not in females. The ratio of BCKA/BCAA trended lower in males. In the cohort as a whole, BCKAs correlated positively with the ratio of TG to HDL at baseline and HOMA-IR at 6-month-follow-up. Glutamate/glutamine was positively associated with HOMA-IR at baseline and 6-month-follow-up. A reduction in BCKAs was associated with an increase in adiponectin, and those with higher BCKAs at baseline had higher adiponectin levels at 6-month-follow-up. Interestingly those adolescents with higher uric acid levels at baseline had greater reduction in weight.

CONCLUSIONS

BCKAs and glutamate/glutamine may serve as biomarkers of IR in adolescents with obesity, and uric acid might serve as a predictor of weight loss in response to lifestyle-intervention. Differential regulation of BCAA catabolism in adolescent males and females implicates critical roles for sex steroids in metabolic homeostasis.

Damp-heat constitution influences gut microbiota and urine metabolism of Chinese infants

Background

As an increasingly popular complementary and alternative approach for early detection and treatment of disease, traditional Chinese medicine constitution (TCMC) divides human beings into those with balanced constitution (BC) and unbalanced constitution, where damp-heat constitution (DHC) is one of the most unbalanced constitutions. Many studies have been carried out on the microscopic mechanism of constitution classification; however, most of these studies were conducted in adults and rarely in infants. Many diseases are closely related to intestinal microbiota, and metabolites produced by the interaction between microbiota and the body may impact constitution classification. Herein, we investigated the overall constitution distribution in Chinese infants, and analyzed the profiles of gut microbiota and urine metabolites of DHC to further promote the understanding of infants constitution classification.

Methods

General information was collected and TCMC was evaluated by Constitutional Medicine Questionnaires. 1315 questionnaires were received in a cross-sectional study to investigate the constitution composition in Chinese infants. A total of 56 infants, including 30 DHC and 26 BC, were randomly selected to analyze gut microbiota by 16S rRNA sequencing and urine metabolites by UPLC-Q-TOF/MS method.

Results

BC was the most common constitution in Chinese infants, DHC was the second common constitution. The gut microbiota and urine metabolites in the DHC group showed different composition compared to the BC group. Four differential genera and twenty differential metabolites were identified. In addition, the combined marker composed of four metabolites may have the high potential to discriminate DHC from BC with an AUC of 0.765.

Conclusions

The study revealed the systematic differences in the gut microbiota and urine metabolites between infants with DHC and BC. Moreover, the differential microbiota and metabolites may offer objective evidences for constitution classification.

Circulating Amino Acids and Risk of Peripheral Artery Disease in the PREDIMED Trial

Effective prevention and risk prediction are important for peripheral artery disease (PAD) due to its poor prognosis and the huge disease burden it produces. Circulating amino acids (AA) and their metabolites may serve as biomarkers of PAD risk, but they have been scarcely investigated. The objective was to prospectively analyze the associations of baseline levels of plasma AA (and their pathways) with subsequent risk of PAD and the potential effect modification by a nutritional intervention with the Mediterranean diet (MedDiet). A matched case-control study was nested in the PREDIMED trial, in which participants were randomized to three arms: MedDiet with tree nut supplementation group, MedDiet with extra-virgin olive oil (EVOO) supplementation group or control group (low-fat diet). One hundred and sixty-seven PAD cases were matched with 250 controls. Plasma AA was measured with liquid chromatography/mass spectrometry at the Broad Institute. Baseline tryptophan, serine and threonine were inversely associated with PAD (OR_{for 1 SD increase} = 0.78 (0.61-0.99); 0.67 (0.51-0.86) and 0.75 (0.59-0.95), respectively) in a multivariable-adjusted conditional logistic regression model. The kynurenine/tryptophan ratio was directly associated with PAD (OR_{for 1 SD increase} = 1.50 (1.14-1.98)). The nutritional intervention with the MedDiet+nuts modified the association between threonine and PAD (p-value interaction = 0.018) compared with the control group. However, subjects allocated to the MedDiet+EVOO group were protected against PAD independently of baseline threonine. Plasma tryptophan, kynurenine/tryptophan ratio, serine and threonine might serve as early biomarkers of future PAD in subjects at a high risk of cardiovascular disease. The MedDiet supplemented with EVOO exerted a protective effect, regardless of baseline levels of threonine.

Development and validation of a metabolite index for obstructive sleep apnea across race/ethnicities

Obstructive sleep apnea (OSA) is a common disorder characterized by recurrent episodes of upper airway obstruction during sleep resulting in oxygen desaturation and sleep fragmentation, and associated with increased risk of adverse health outcomes. Metabolites are being increasingly used for biomarker discovery and evaluation of disease processes and progression. Studying metabolomic associations with OSA in a diverse community-based cohort may provide insights into the pathophysiology of OSA. We aimed to develop and replicate a metabolite index for OSA and identify individual metabolites associated with OSA. We studied 219 metabolites and their associations with the apnea hypopnea index (AHI) and with moderate-severe OSA (AHI ≥ 15) in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (n = 3507) using two methods: (1) association analysis of individual metabolites, and (2) least absolute shrinkage and selection operator (LASSO) regression to identify a subset of metabolites jointly associated with OSA, which was used to develop a metabolite index for OSA. Results were validated in the Multi-Ethnic Study of Atherosclerosis (MESA) (n = 475). When assessing the associations with individual metabolites, we identified seven metabolites significantly positively associated with OSA in HCHS/SOL (FDR p < 0.05), of which four associations-glutamate, oleoyl-linoleoyl-glycerol (18:1/18:2), linoleoyl-linoleoyl-glycerol (18:2/18:2) and phenylalanine, were replicated in MESA (one sided-p < 0.05). The OSA metabolite index, composed of 14 metabolites, was associated with a 50% increased risk for moderate-severe OSA (OR = 1.50 [95% CI 1.21-1.85] per 1 SD of OSA metabolite index, p < 0.001) in HCHS/SOL and 55% increased risk (OR = 1.55 [95% CI 1.10-2.20] per 1 SD of OSA metabolite index, p = 0.013) in MESA, both adjusted for demographics, lifestyle, and comorbidities. Similar albeit less significant associations were observed for AHI. Replication of the metabolite index in an independent multi-ethnic dataset demonstrates the robustness of metabolomic-based OSA index to population heterogeneity. Replicated metabolite associations may provide insights into OSA-related molecular and metabolic mechanisms.

Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study

BACKGROUND

Previous work on predicting type 2 diabetes by integrating clinical and genetic factors has mostly focused on the Western population. In this study, we use genome-wide polygenic risk score (gPRS) and serum metabolite data for type 2 diabetes risk prediction in the Asian population.

METHODS

Data of 1425 participants from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort were used in this study. For gPRS analysis, genotypic and clinical information from KoGES health examinee (n = 58,701) and KoGES cardiovascular disease association (n = 8105) sub-cohorts were included. Linkage disequilibrium analysis identified 239,062 genetic variants that were used to determine the gPRS, while the metabolites were selected using the Boruta algorithm. We used bootstrapped cross-validation to evaluate logistic regression and random forest (RF)-based machine learning models. Finally, associations of gPRS and selected metabolites with the values of homeostatic model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) were further estimated.

FINDINGS

During the follow-up period (8.3 ± 2.8 years), 331 participants (23.2%) were diagnosed with type 2 diabetes. The areas under the curves of the RF-based models were 0.844, 0.876, and 0.883 for the model using only demographic and clinical factors, model including the gPRS, and model with both gPRS and metabolites, respectively. Incorporation of additional parameters in the latter two models improved the classification by 11.7% and 4.2% respectively. While gPRS was significantly associated with HOMA-B value, most metabolites had a significant association with HOMA-IR value.

INTERPRETATION

Incorporating both gPRS and metabolite data led to enhanced type 2 diabetes risk prediction by capturing distinct etiologies of type 2 diabetes development. An RF-based model using clinical factors, gPRS, and metabolites predicted type 2 diabetes risk more accurately than the logistic regression-based model.

FUNDING

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2019M3E5D1A02070863 and 2022R1C1C1005458). This work was also supported by the 2020 Research Fund (1.200098.01) of UNIST (Ulsan National Institute of Science & Technology).

The association of dietary glutamine supplementation with the development of high salt-induced hypertension in rats

Glutamine supplementation has been reported to affect blood pressure (BP). However, its role in the progression of hypertension induced by high salt diet (HSD) has not been elucidated. Male normotensive Wistar rats were exposed to high salt diet and treated with different doses of glutamine supplementation. Rats aged 6 weeks were assigned to five groups: (1) Normal-salt diet (0.3% NaCl, NSD); (2) High-salt diet (8% NaCl, HSD); (3) High-salt + low-dose diet (8% NaCl, 0.5 g of L-glutamine/kg body weight, HSLGD); (4) High-salt + middle-dose diet (8% NaCl, 1.5 g of L-glutamine/kg body weight, HSMGD); and (5) High-salt + high-dose diet (8% NaCl, 2.5 g of L-glutamine/kg body weight, HSHGD). After supplementing different doses of glutamine to male Wistar 6-week-old rats fed with HSD for 7 weeks, we found no difference in body weight among groups. Importantly, we showed that dietary L-glutamine supplementation could prevent the development of hypertension in a dose-dependent manner [dramatically lowering systolic blood pressure (SBP) and slightly reducing diastolic blood pressure (DBP) of hypertensive rats, while the differences of DBP between groups did not reach statistical significance]. Our data further elucidated that dietary glutamine supplementation mildly alleviated the degree of left ventricular hypertrophy, including interventricular septal thickness (IVST) and left ventricular posterior wall thickness (LVPWT) in hypertensive rats. Together, our results offer evidence that the dietary uptake of glutamine may be associated with attenuating the development of high salt-induced hypertension and slightly alleviating the degree of left ventricular hypertrophy in hypertensive rats. Therefore, glutamine supplementation may act as a prospective dietary intervention for the treatment of hypertension.

The association between plasma metabolites and future risk of all-cause mortality

BACKGROUND

Metabolite profiles provide snapshots of the overall effect of numerous exposures accumulated over life courses, which may lead to health outcomes in the future.

OBJECTIVE

We hypothesized that the risk of all-cause mortality is linked to alterations in metabolism earlier in life, which are reflected in plasma metabolite profiles. We aimed to identify plasma metabolites associated with future risk of all-cause mortality.

METHODS

Through metabolomics, 110 metabolites were measured in 3833 individuals from the Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC). A total of 1574 deaths occurred within an average follow-up time of 22.2 years. Metabolites that were significantly associated with all-cause mortality in MDC-CC were replicated in 1500 individuals from Malmö Preventive Project re-examination (MPP), among whom 715 deaths occurred within an average follow-up time of 11.3 years.

RESULTS

Twenty two metabolites were significantly associated with all-cause mortality in MDC-CC, of which 13 were replicated in MPP. Levels of trigonelline, glutamate, dimethylglycine, C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, and 1-methyladenosine were associated with an increased risk, while levels of valine, tryptophan, lysine, leucine, histidine, and 2-aminoisobutyrate were associated with a decreased risk of all-cause mortality.

CONCLUSION

We used metabolomics in two Swedish prospective cohorts and identified replicable associations between 13 metabolites and future risk of all-cause mortality. Novel associations between five metabolites-C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, trigonelline, and 2-aminoisobutyrate-and all-cause mortality were discovered. These findings suggest potential new biomarkers for the prediction of mortality and provide insights for understanding the biochemical pathways that lead to mortality.

Metabolomics strategy comprehensively unveils the effect of catechins intervention on the biomarkers of exposure to acrylamide and biomarkers of cardiometabolic risk

Polyphenolic antioxidants have been suggested to control the generation of acrylamide during thermal reactions. However, their role in protecting against the toxicity of acrylamide and the mechanism of action regarding profile alteration of biomarkers and metabolome remains unclear. A total of 65 adults were randomized into tea polyphenols (TP) and control groups and served with potato chips, which corresponded to an intake level of 12.6 μg/kg·bw of acrylamide, followed by capsules containing 200 mg, 100 mg or 50 mg TP, or equivalent placebo. Moreover, nontargeted urinary metabolomics analysis in acrylamide exposed rats was conducted using ultra-high performance liquid chromatography linked with a quadrupole-orbitrap high-resolution mass spectrometry. Our results showed that supplementation with catechins promoted the excretion of N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-l-cysteine in both humans and rats. We also found that epigallocatechin gallate (EGCG) or epicatechin (EC) intervention attenuated the ratio of hemoglobin adduct of glycidamide to hemoglobin adduct of acrylamide in rat blood. Metabolomics analysis revealed that EGCG/EC intervention regulated the differential expressed metabolites, including l-glutamic acid, 2-oxoglutarate, citric acid, and cysteinylglycine. Kyoto Encyclopedia of Genes and Genomes pathway analysis further showed acrylamide-induced metabolic disorders were improved after EGCG/EC supplementation by glycolipid metabolism (alanine, aspartate and glutamate metabolism, and d-Glutamine and d-glutamate metabolism) and energy metabolism (tricarboxylic acid cycle). Notably, the supplement use of EGCG prevented the cardiometabolic risk after exposure to acrylamide by mediating the phenylalanine and hippuric acid in phenylalanine metabolism. Here we showed the beneficial effect of catechins as major polyphenolic antioxidant ingredients on the toxicity of acrylamide by the changes in biomarkers from metabolic profile analysis based on human and animal studies. These findings shed light into the catechins as natural polyphenolic antioxidants that could be a therapeutic ingredient for preventing acrylamide-induced cardiometabolic toxicity.

Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose. Of these, 144 and 68 associations, respectively, replicated in a multiethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ = 0.29), and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.

Plasma metabolomic signatures of obesity and risk of type 2 diabetes

OBJECTIVE

The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D.

METHODS

In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk.

RESULTS

A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001).

CONCLUSIONS

Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.

Amino acid profiling to predict prognosis in patients with heart failure: an expert review

Heart failure is a complex disease with a poor prognosis. A number of widely used prognostic tools have limitations, so efforts to identify novel predictive markers and measures are important. As a metabolomics tool, amino acid profiling has shown promise in predicting heart failure prognosis; however, the evidence has not yet been sufficiently evaluated. We describe the utilization of amino acids in the healthy heart and in heart failure before reviewing the literature on amino acid profiling for prognostic prediction. We expertly interpret the findings and provide suggestions for future research to advance the understanding of the prognostic potential of amino acid profiling in heart failure. Our analysis revealed correlations between amino acid biomarkers and traditional prognostic factors, the additional prognostic value of amino acid biomarkers over traditional prognostic factors, and the successful use of amino acid biomarkers to distinguish heart failure aetiology. Although certain amino acid biomarkers have demonstrated additional prognostic value over traditional measures, such as New York Heart Association functional class, these measures are deeply rooted in clinical practice; thus, amino acid biomarkers may be best placed as additional prognostic tools to improve current risk stratification rather than as surrogate tools. Once the metabolic profiles of different heart failure aetiologies have been clearly delineated, the amino acid biomarkers with the most value in prognostic prediction should be determined. Amino acid profiling could be useful to evaluate the pathophysiology and metabolic status of different heart failure cohorts, distinguish heart failure aetiologies, and improve risk stratification and prognostic prediction.

Response of circulating metabolites to an oral glucose challenge and risk of cardiovascular disease and mortality in the community

Background

New biomarkers to identify cardiovascular disease (CVD) risk earlier in its course are needed to enable targeted approaches for primordial prevention. We evaluated whether intraindividual changes in blood metabolites in response to an oral glucose tolerance test (OGTT) may provide incremental information regarding the risk of future CVD and mortality in the community.

Methods

An OGTT (75 g glucose) was administered to a subsample of Framingham Heart Study participants free from diabetes (n = 361). Profiling of 211 plasma metabolites was performed from blood samples drawn before and 2 h after OGTT. The log2(post/pre) metabolite levels (Δmetabolites) were related to incident CVD and mortality in Cox regression models adjusted for age, sex, baseline metabolite level, systolic blood pressure, hypertension treatment, body mass index, smoking, and total/high-density lipoprotein cholesterol. Select metabolites were related to subclinical cardiometabolic phenotypes using Spearman correlations adjusted for age, sex, and fasting metabolite level.

Results

Our sample included 42% women, with a mean age of 56 ± 9 years and a body mass index of 30.2 ± 5.3 kg/m². The pre- to post-OGTT changes (Δmetabolite) were non-zero for 168 metabolites (at FDR ≤ 5%). A total of 132 CVD events and 144 deaths occurred during median follow-up of 24.9 years. In Cox models adjusted for clinical risk factors, four Δmetabolites were associated with incident CVD (higher glutamate and deoxycholate, lower inosine and lysophosphatidylcholine 18:2) and six Δmetabolites (higher hydroxyphenylacetate, triacylglycerol 56:5, alpha-ketogluturate, and lower phosphatidylcholine 32:0, glucuronate, N-monomethyl-arginine) were associated with death (P < 0.05). Notably, baseline metabolite levels were not associated with either outcome in models excluding Δmetabolites. The Δmetabolites exhibited varying cross-sectional correlation with subclinical risk factors such as visceral adiposity, insulin resistance, and vascular stiffness, but overall relations were modest. Significant Δmetabolites included those with established roles in cardiometabolic disease (e.g., glutamate, alpha-ketoglutarate) and metabolites with less defined roles (e.g., glucuronate, lipid species).

Conclusions

Dynamic changes in metabolite levels with an OGTT are associated with incident CVD and mortality and have potential relevance for identifying CVD risk earlier in its development and for discovering new potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01647-w.

A metabolomic analysis of adiposity measures and pre- and postmenopausal breast cancer risk in the Nurses' Health Studies

BACKGROUND

Adiposity is consistently positively associated with postmenopausal breast cancer and inversely associated with premenopausal breast cancer risk, though the reasons for this difference remain unclear.

METHODS

In this nested case-control study of 1649 breast cancer cases and 1649 matched controls from the Nurses' Health Study (NHS) and the NHSII, we selected lipid and polar metabolites correlated with BMI, waist circumference, weight change since age 18, or derived fat mass, and developed a metabolomic score for each measure using LASSO regression. Logistic regression was used to investigate the association between this score and breast cancer risk, adjusted for risk factors and stratified by menopausal status at blood draw and diagnosis.

RESULTS

Metabolite scores developed among only premenopausal or postmenopausal women were highly correlated with scores developed in all women (r = 0.93-0.96). Higher metabolomic adiposity scores were generally inversely related to breast cancer risk among premenopausal women. Among postmenopausal women, significant positive trends with risk were observed (e.g., metabolomic waist circumference score OR Q4 vs. Q1 = 1.47, 95% CI = 1.03-2.08, P-trend = 0.01).

CONCLUSIONS

Though the same metabolites represented adiposity in pre- and postmenopausal women, breast cancer risk associations differed suggesting that metabolic dysregulation may have a differential association with pre- vs. postmenopausal breast cancer.

Impact of Phenol-Enriched Olive Oils on Serum Metabonome and Its Relationship with Cardiometabolic Parameters: A Randomized, Double-Blind, Cross-Over, Controlled Trial

Phenol-rich foods consumption such as virgin olive oil (VOO) has been shown to have beneficial effects on cardiovascular diseases. The broader biochemical impact of VOO and phenol-enriched OOs remains, however, unclear. A randomized, double-blind, cross-over, controlled trial was performed with thirty-three hypercholesterolemic individuals who ingested for 3-weeks (25 mL/day): (1) an OO enriched with its own olive oil phenolic compounds (PCs) (500 ppm; FOO); (2) an OO enriched with its own olive oil PCs (250 ppm) plus thyme PCs (250 ppm; FOOT); and (3) a VOO with low phenolic content (80 ppm). Serum lipid and glycemic profiles, serum ¹H-NMR spectroscopy-based metabolomics, endothelial function, blood pressure, and cardiovascular risk were measured. We combined OPLS-DA with machine learning modelling to identify metabolites discrimination of the treatment groups. Both phenol-enriched OO interventions decreased the levels of glutamine, creatinine, creatine, dimethylamine, and histidine in comparison to VOO one. In addition, FOOT decreased the plasma levels of glycine and DMSO2 compared to VOO, while FOO decreased the circulating alanine concentrations but increased the plasma levels of acetone and 3-HB compared to VOO. Based on these findings, phenol-enriched OOs were shown to result in a favorable shift in the circulating metabolic phenotype, inducing a reduction in metabolites associated with cardiometabolic diseases.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Diet and BMI Correlate with Metabolite Patterns Associated with Aggressive Prostate Cancer

Three metabolite patterns have previously shown prospective inverse associations with the risk of aggressive prostate cancer within the European Prospective Investigation into Cancer and Nutrition (EPIC). Here, we investigated dietary and lifestyle correlates of these three prostate cancer-related metabolite patterns, which included: 64 phosphatidylcholines and three hydroxysphingomyelins (Pattern 1), acylcarnitines C18:1 and C18:2, glutamate, ornithine, and taurine (Pattern 2), and 8 lysophosphatidylcholines (Pattern 3). In a two-stage cross-sectional discovery (n = 2524) and validation (n = 518) design containing 3042 men free of cancer in EPIC, we estimated the associations of 24 dietary and lifestyle variables with each pattern and the contributing individual metabolites. Associations statistically significant after both correction for multiple testing (False Discovery Rate = 0.05) in the discovery set and at p < 0.05 in the validation set were considered robust. Intakes of alcohol, total fish products, and its subsets total fish and lean fish were positively associated with Pattern 1. Body mass index (BMI) was positively associated with Pattern 2, which appeared to be driven by a strong positive BMI-glutamate association. Finally, both BMI and fatty fish were inversely associated with Pattern 3. In conclusion, these results indicate associations of fish and its subtypes, alcohol, and BMI with metabolite patterns that are inversely associated with risk of aggressive prostate cancer.

Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study

Objective

Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators.

Methods

From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model.

Results

The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration.

Conclusions

The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.

Metabolic changes associated with two endocrine abnormalities in dogs: elevated fructosamine and low thyroxine

INTRODUCTION

Metabolomics studies in canine endocrine abnormalities are sparse and basic information on these abnormalities must be generated.

OBJECTIVES

To characterize the metabolic changes associated with elevated fructosamine, reflecting poor glycemic control, and low thyroxine, a thyroid hormone controlling metabolism.

METHODS

Leftovers of clinical serum samples; 25 controls, 79 high fructosamine, and 47 low thyroxine, were analyzed using ¹H NMR and differences were evaluated using Firth logistic regression.

RESULTS

Both high fructosamine and low thyroxine were associated with changes in concentrations of multiple metabolites, including glycoprotein acetyls and lipids.

CONCLUSION

These findings suggest promising makers for further research and clinical validation.

Serum Untargeted Metabolomics Reveal Potential Biomarkers of Progression of Diabetic Retinopathy in Asians

Purpose: To reveal molecular mechanisms of diabetic retinopathy (DR) in Asians and facilitate the identification of new therapeutic targets through untargeted metabolomics. To determine the differences in serum metabolites and metabolic pathways between different stages of diabetic retinopathy in patients with type 2 diabetic mellitus (T2DM) and proliferative DR (PDR) and non-proliferative DR (NPDR) and identify differential metabolites between T2DM and DR (NPDR and PDR) patients. Methods: This prospective observational registration study described the differential metabolites between 45 T2DM patients and 15 control cases with no significant differences in clinical characteristics. Their biospecimens and clinical information were collected and recorded in their medical reports. DR phenotypes of the subjects were verified by retina specialists. Serum metabolites were analyzed using high-resolution mass spectrometry with liquid chromatography. Untargeted metabolomics was performed on serum samples from 15 T2DM patients, 15 non-proliferative diabetic retinopathy patients, 15 proliferative diabetic retinopathy patients, and 15 diabetic controls. Discriminatory metabolic features were identified through partial least squares discriminant analysis (PLS-DA), hierarchical clustering analysis (HCA), and generalized linear regression models. Result: Through untargeted metabolomics, 931 features (523 in positive and 408 in negative modes) with 102 common metabolites highly relevant to the presence of DR were detected. In the adjusted analysis, 67 metabolic features differed significantly between T2DM and NPDR patients. Pathway analysis revealed alterations in metabolisms of amino acids and fatty acids. Glutamate, phosphatidylcholine, and 13-hydroperoxyoctadeca-9,11-dienoic acid (13-PHODE) were key contributors to these pathway differences. A total of 171 features distinguished PDR patients from T2DM patients, and pathway analysis revealed alterations in amino acid metabolism, fatty acid metabolism, nitrogen metabolism, and tricarboxylic acid cycle. Aspartate, glutamate, glutamine, ornithine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, citrate, succinate, N-(L-arginino)succinate, 2-oxoglutarate, 13-hydroperoxyoctadeca-9,11-dienoic acid, methionine, lysine, threonine, phenylalanine, N(pi)-methyl-l-histidine, phosphatidylcholine, and linoleate were major contributors to the pathway differences. Between NPDR patients and PDR patients, there were 79 significant differential metabolites. Enrichment pathway analysis showed changes in amino acid metabolism, fatty acid metabolism, pantothenate, and CoA biosynthesis. Aspartate, glutamine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, pantothenate, dihomo-gamma-linolenate, docosahexaenoic acid, and icosapentaenoic acid were key factors for the differences of these pathways. Conclusion: This study demonstrated that the pathways of arginine biosynthesis metabolism, linoleic acid metabolism, alanine, aspartate, and glutamate metabolism, as well as d-glutamine and d-glutamate metabolism, were dysregulated in DR patients of the Asian population. Increased levels of glutamate, aspartate, glutamine, N-acetyl-l-glutamate, and N-acetyl-l-aspartate and decreased levels of dihomo-gamma-linolenate, docosahexaenoic, and icosapentaenoic were considered as the metabolic profile that could distinguish PDR from NPDR in Asians. Phosphatidylcholine and 13-PHODE were identified as two major novel metabolite markers in advanced stages of DR in our study.

Branched-Chain and Aromatic Amino Acids Related to Visceral Adipose Tissue Impact Metabolic Health Risk Markers

CONTEXT

Visceral (VAT) and subcutaneous adipose tissue (SAT) function as endocrine organs capable of influencing metabolic health across adiposity levels.

OBJECTIVE

We aimed to investigate whether metabolites associated with VAT and SAT impact metabolic health through metabolite concentrations.

METHODS

Analyses are based on 1790 participants from the population-based Rhineland Study. We assessed plasma levels of methionine (Met), branched-chain amino acids (BCAA), aromatic amino acids (AAA), and their metabolic downstream metabolites with liquid chromatography-mass spectrometry. VAT and SAT volumes were assessed by magnetic resonance imaging (MRI). Metabolically healthy and unhealthy phenotypes were defined using Wildman criteria.

RESULTS

Metabolically unhealthy participants had higher concentrations of BCAA than metabolically healthy participants (P < 0.001). In metabolically unhealthy participants, VAT volumes were significantly associated with levels of L-isoleucine, L-leucine, indole-3-lactic acid, and indole-3-propionic acid (in log SD units: β = 0.16, P = 0.003; β = 0.12, P = 0.038; β = 0.11, P = 0.035 and β = -0.16, P = 0.010, respectively). Higher concentrations of certain BCAA and AAA-downstream metabolites significantly increased the odds of cardiometabolic risk markers. The relation between VAT volume and cardiometabolic risk markers was mediated by BCAA (indirect effects 3.7%-11%, P = 0.02 to < 0.0001), while the effect of VAT on systemic inflammation was mediated through higher kynurenine concentrations (indirect effect 6.4%, P < 0.0001).

CONCLUSION

Larger volumes of VAT in metabolically unhealthy individuals are associated with altered concentrations of circulating BCAA and AAA-downstream metabolites, increasing the odds of cardiometabolic risk markers. This suggests that these metabolites are involved in the mechanisms that underlie the relationship of abdominal VAT with metabolic health.

Plasma Metabolomic Signature of Early Abuse in Middle-Aged Women

OBJECTIVE

Metabolomic profiling may provide insights into biological mechanisms underlying the strong epidemiologic links observed between early abuse and cardiometabolic disorders in later life.

METHODS

We examined the associations between early abuse and midlife plasma metabolites in two nonoverlapping subsamples from the Nurses' Health Study II, comprising 803 (mean age = 40 years) and 211 women (mean age = 61 years). Liquid chromatography-tandem mass spectrometry assays were used to measure metabolomic profiles, with 283 metabolites consistently measured in both subsamples. Physical and sexual abuse before age 18 years was retrospectively assessed by validated questions integrating type/frequency of abuse. Analyses were conducted in each sample and pooled using meta-analysis, with multiple testing adjustment using the q value approach for controlling the positive false discovery rate.

RESULTS

After adjusting for age, race, menopausal status, body size at age 5 years, and childhood socioeconomic indicators, more severe early abuse was consistently associated with five metabolites at midlife (q value < 0.20 in both samples), including lower levels of serotonin and C38:3 phosphatidylethanolamine plasmalogen and higher levels of alanine, proline, and C40:6 phosphatidylethanolamine. Other metabolites potentially associated with early abuse (q value < 0.05 in the meta-analysis) included triglycerides, phosphatidylcholine plasmalogens, bile acids, tyrosine, glutamate, and cotinine. The association between early abuse and midlife metabolomic profiles was partly mediated by adulthood body mass index (32% mediated) and psychosocial distress (13%-26% mediated), but not by other life-style factors.

CONCLUSIONS

Early abuse was associated with distinct metabolomic profiles of multiple amino acids and lipids in middle-aged women. Body mass index and psychosocial factors in adulthood may be important intermediates for the observed association.

Nucleosides Associated With Incident Ischemic Stroke in the REGARDS and JHS Cohorts

BACKGROUND AND OBJECTIVES

Both genetic and environmental factors contribute to stroke risk. We sought to identify novel metabolites associated with incident stroke in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort and determine whether they reflected genetic or environmental variation.

METHODS

This was a stroke case-cohort observational study nested in REGARDS. Cases were defined as incident stroke and metabolomic profiles were compared to a randomly selected control cohort. In baseline plasma samples, 162 metabolites were measured using liquid chromatography-tandem mass spectrometry. Cox proportional hazards models were adjusted for age, sex, race, and age by race in the base model. Fully adjusted models included traditional stroke risk factors. Mediation analyses conducted for these stroke risk factors used the metabolite as mediator. Genome-wide associations with the leading candidate metabolites were calculated using array data. Replication analyses in the Jackson Heart Study (JHS) were conducted using random effects meta-analysis.

RESULTS

There were 2,043 participants who were followed over an average period of 7.1 years, including 1,075 stroke cases and 968 random controls. Nine metabolites were associated with stroke in the base model, 8 of which were measured and remained significant in meta-analysis with JHS. In the fully adjusted model in REGARDS, guanosine (hazard ratio [HR] 1.34, 95% CI 1.18-1.53; p = 7.26 × 10^-6) and pseudouridine (HR 1.28, 95% CI 1.13-1.45; p = 1.03 × 10^-4) were associated with incident ischemic stroke following Bonferroni adjustment. Guanosine also partially mediated the relationship between hypertension and stroke (17.6%) and pseudouridine did not mediate any risk factor. Genome-wide association analysis identified loci rs34631560 and rs34631560 associated with pseudouridine, but these did not explain the association of pseudouridine with stroke.

DISCUSSION

Guanosine and pseudouridine are nucleosides associated with incident ischemic stroke independently of other risk factors. Genetic and mediation analyses suggest that environmental exposures rather than genetic variation link nucleoside levels to stroke risk.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that guanosine and pseudouridine are associated with incident stroke.

Potential protective effect against SARS-CoV-2 infection by APOE rs7412 polymorphism

The pandemic burden caused by the SARS-CoV-2 coronavirus constitutes a global public health emergency. Increasing understanding about predisposing factors to infection and severity is now a priority. Genetic, metabolic, and environmental factors can play a crucial role in the course and clinical outcome of COVID-19. We aimed to investigate the putative relationship between genetic factors associated to obesity, metabolism and lifestyle, and the presence and severity of SARS-CoV-2 infection. A total of 249 volunteers (178 women and 71 men, with mean and ± SD age of 49 ± 11 years) characterized for dietary, lifestyle habits and anthropometry, were studied for presence and severity of COVID-19 infection, and genotyped for 26 genetic variants related to obesity, lipid profile, inflammation, and biorhythm patterns. A statistically significant association was found concerning a protective effect of APOE rs7412 against SARS-CoV-2 infection (p = 0.039; OR 0.216; CI 0.084, 0.557) after correction for multiple comparisons. This protective effect was also ascribed to the APOɛ2 allele (p = 0.001; OR 0.207; CI 0.0796, 0.538). The genetic variant rs7412 resulting in ApoE2, genetic determinant of lipid and lipoprotein levels, could play a significant role protecting against SARS-CoV-2 infection.

Metabolome-Defined Obesity and the Risk of Future Type 2 Diabetes and Mortality

OBJECTIVE

Obesity is a key risk factor for type 2 diabetes; however, up to 20% of patients are normal weight. Our aim was to identify metabolite patterns reproducibly predictive of BMI and subsequently to test whether lean individuals who carry an obese metabolome are at hidden high risk of obesity-related diseases, such as type 2 diabetes.

RESEARCH DESIGN AND METHODS

Levels of 108 metabolites were measured in plasma samples of 7,663 individuals from two Swedish and one Italian population-based cohort. Ridge regression was used to predict BMI using the metabolites. Individuals with a predicted BMI either >5 kg/m2 higher (overestimated) or lower (underestimated) than their actual BMI were characterized as outliers and further investigated for obesity-related risk factors and future risk of type 2 diabetes and mortality.

RESULTS

The metabolome could predict BMI in all cohorts (r2 = 0.48, 0.26, and 0.19). The overestimated group had a BMI similar to individuals correctly predicted as normal weight, had a similar waist circumference, were not more likely to change weight over time, but had a two times higher risk of future type 2 diabetes and an 80% increased risk of all-cause mortality. These associations remained after adjustments for obesity-related risk factors and lifestyle parameters.

CONCLUSIONS

We found that lean individuals with an obesity-related metabolome have an increased risk for type 2 diabetes and all-cause mortality compared with lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals to initiate lifestyle and pharmacological interventions.

Persistent organic pollutants and β-cell toxicity: a comprehensive review

Persistent organic pollutants (POPs) are a diverse family of contaminants that show widespread global dispersion and bioaccumulation. Humans are continuously exposed to POPs through diet, air particles, and household and commercial products; POPs are consistently detected in human tissues, including the pancreas. Epidemiological studies show a modest but consistent correlation between exposure to POPs and increased diabetes risk. The goal of this review is to provide an overview of epidemiological evidence and an in-depth evaluation of the in vivo and in vitro evidence that POPs cause β-cell toxicity. We review evidence for six classes of POPs: dioxins, polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), organophosphate pesticides (OPPs), flame retardants, and per- and polyfluoroalkyl substances (PFAS). The available data provide convincing evidence implicating POPs as a contributing factor driving impaired glucose homeostasis, β-cell dysfunction, and altered metabolic and oxidative stress pathways in islets. These findings support epidemiological data showing that POPs increase diabetes risk and emphasize the need to consider the endocrine pancreas in toxicity assessments. Our review also highlights significant gaps in the literature assessing islet-specific endpoints after both in vivo and in vitro POP exposure. In addition, most rodent studies do not consider the impact of biological sex or secondary metabolic stressors in mediating the effects of POPs on glucose homeostasis and β-cell function. We discuss key gaps and limitations that should be assessed in future studies.

Dietary Branched-Chain Amino Acids (BCAAs) and Risk of Dyslipidemia in a Chinese Population

This study aimed to explore the association between dietary BCAAs, blood lipid levels and risk of dyslipidemia. In this case−control study, a total of 9541 subjects with normal blood lipids were included as a control group, and 9792 patients with dyslipidemia were included as a case group. Dietary BCAA intake data were measured using 3-day 24 h meal recalls and household condiment weighing. All samples were from China Nutrition and Health Surveillance (2015). Generalized linear model, logistic regression, and restricted cubic spline (RCS) were used to evaluate the relationship between dietary BCAAs, blood lipids and dyslipidemia. After adjusting for confounding factors, dietary BCAAs were positively correlated with TC and LDL-C (p < 0.05). Higher dietary BCAAs were associated with higher OR for Hypercholesteremia (Q4 vs. Q1, OR = 1.29, 95% CI: 1.05−1.58, p-trend = 0.034). The ORs of Hyper-LDL-cholesterolemia showed inverted U-shaped with increasing dietary BCAAs (Q3 vs. Q1, OR = 1.20, 95% CI: 1.03−1.39; Q2 vs. Q1, OR = 1.05, 95% CI: 1.01−1.31). The relationship between dietary BCAAs and the risk of Hypercholesteremia and Hyper-LDL-cholesterolemia were both nonlinear (p nonlinearity = 0.0059, 0.0198). Our study reveals that dietary BCAAs are associated with specific types of lipids and risk of dyslipidemia, some of which may be non-linear.

The association of serum sulfur amino acids and related metabolites with incident diabetes: a prospective cohort study

AIM

Plasma total cysteine (tCys) is associated with fat mass and insulin resistance, whereas taurine is inversely related to diabetes risk. We investigated the association of serum sulfur amino acids (SAAs) and related amino acids (AAs) with incident diabetes.

METHODS

Serum AAs were measured at baseline in 2997 subjects aged ≥ 65 years. Diabetes was recorded at baseline and after 4 years. Logistic regression evaluated the association of SAAs [methionine, total homocysteine (tHcy), cystathionine, tCys, and taurine] and related metabolites [serine, total glutathione (tGSH), glutamine, and glutamic acid] with diabetes risk.

RESULTS

Among 2564 subjects without diabetes at baseline, 4.6% developed diabetes. Each SD increment in serum tCys was associated with a 68% higher risk (95% CI 1.27, 2.23) of diabetes [OR for upper vs. lower quartile 2.87 (1.39, 5.91)], after full adjustments (age, sex, other AAs, adiposity, eGFR, physical activity, blood pressure, diet and medication); equivalent ORs for cystathionine were 1.33 (1.08, 1.64) and 1.68 (0.85, 3.29). Subjects who were simultaneously in the upper tertiles of both cystathionine and tCys had a fivefold risk [OR = 5.04 (1.55, 16.32)] of diabetes compared with those in the lowest tertiles. Higher serine was independently associated with a lower risk of developing diabetes [fully adjusted OR per SD = 0.68 (0.54, 0.86)]. Glutamic acid and glutamine showed positive and negative associations, respectively, with incident diabetes in age- and sex-adjusted analysis, but only the glutamic acid association was independent of other confounders [fully adjusted OR per SD = 1.95 (1.19, 3.21); for upper quartile = 7.94 (3.04, 20.75)]. tGSH was inversely related to diabetes after adjusting for age and sex, but not other confounders. No consistent associations were observed for methionine, tHcy or taurine.

CONCLUSION

Specific SAAs and related metabolites show strong and independent associations with incident diabetes. This suggests that perturbations in the SAA metabolic pathway may be an early marker for diabetes risk.

Bibliometric and Visual Analysis on Metabolomics in Coronary Artery Disease Research

Background

Metabolomics has immense research value in coronary artery disease and has drawn increasing attention over the past decades. Many articles have been published in this field, which may challenge researchers aiming to investigate all the available information. However, bibliometrics can provide deep insights into this research field.

Objective

We aimed to qualitatively and quantitatively study metabolomics and coronary artery disease research, visually analyse the development status, trends, research hotspots, and frontiers of this field, and provide a reference for research on coronary artery disease.

Methods

Articles were acquired from the Web of Science Core Collection. VOSviewer and CiteSpace software were used to analyse publication growth, country/region, institution, journal distribution, author, reference, and keywords, and detected the keywords with strong citation burstness to identify emerging topics.

Results

A total of 1121 references were obtained, and the annual number of publications increased over the past 16 years. Metabolomics research has shown a gradual upward trend in coronary artery disease. The United States of America and China ranked at the top in terms of percentage of articles. The institution with the highest number of research publications in this field was Harvard University, followed by the University of California System and Brigham Women's Hospital. The most frequently cited authors included Hazen SL, Tang WH, and Wang ZN. Ala-Korpela M was the most productive author, followed by Clish CB and Adamski J. The journal with the most publications in this field was Scientific Reports, followed by PLoS One and the Journal of Proteome Research. The keywords used at a high frequency were "risk," "biomarkers," "insulin resistance," and "atherosclerosis." Burst detection analysis of top keywords showed that "microbiota," "tryptophan," and "diabetes" are the current research frontiers in this field.

Conclusion

This study provides useful information for acquiring knowledge on metabolomics and coronary artery diseases. Metabolomics research has shown a gradual upward trend in coronary artery disease studies over the past 16 years. Research on tryptophan metabolism regulated by intestinal flora will become an emerging academic trend in this field, which can offer guidance for more extensive and in-depth studies in the future.

Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus

Background: Type 2 diabetes mellitus (T2DM) is a multifaceted disorder affecting epidemic proportion at global scope. Defective insulin secretion by pancreatic β-cells and the inability of insulin-sensitive tissues to respond effectively to insulin are the underlying biology of T2DM. However, circulating biomarkers indicative of early diabetic onset at the asymptomatic stage have not been well described. We hypothesized that global and targeted mass spectrometry (MS) based metabolomic discovery can identify novel serological metabolic biomarkers specifically associated with T2DM. We further hypothesized that these markers can have a unique pattern associated with latent or early asymptomatic stage, promising an effective liquid biopsy approach for population T2DM risk stratification and screening.

Methods: Four independent cohorts were assembled for the study. The T2DM cohort included sera from 25 patients with T2DM and 25 healthy individuals for the biomarker discovery and sera from 15 patients with T2DM and 15 healthy controls for the testing. The Pre-T2DM cohort included sera from 76 with prediabetes and 62 healthy controls for the model training and sera from 35 patients with prediabetes and 27 healthy controls for the model testing. Both global and targeted (amino acid, acylcarnitine, and fatty acid) approaches were used to deep phenotype the serological metabolome by high performance liquid chromatography-high resolution mass spectrometry. Different machine learning approaches (Random Forest, XGBoost, and ElasticNet) were applied to model the unique T2DM/Pre-T2DM metabolic patterns and contrasted with their effectiness to differentiate T2DM/Pre-T2DM from controls.

Results: The univariate analysis identified unique panel of metabolites (n = 22) significantly associated with T2DM. Global metabolomics and subsequent structure determination led to the identification of 8 T2DM biomarkers while targeted LCMS profiling discovered 14 T2DM biomarkers. Our panel can effectively differentiate T2DM (ROC AUC = 1.00) or Pre-T2DM (ROC AUC = 0.84) from the controls in the respective testing cohort.

Conclusion: Our serological metabolite panel can be utilized to identifiy asymptomatic population at risk of T2DM, which may provide utility in identifying population at risk at an early stage of diabetic development to allow for clinical intervention. This early detection would guide ehanced levels of care and accelerate development of clinical strategies to prevent T2DM.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

Glutamine Regulates Skeletal Muscle Immunometabolism in Type 2 Diabetes

Dysregulation of skeletal muscle metabolism influences whole-body insulin sensitivity and glucose homeostasis. We hypothesized that type 2 diabetes-associated alterations in the plasma metabolome directly contribute to skeletal muscle immunometabolism and the subsequent development of insulin resistance. To this end, we analyzed the plasma and skeletal muscle metabolite profile and identified glutamine as a key amino acid that correlates inversely with BMI and insulin resistance index (HOMA-IR) in men with normal glucose tolerance or type 2 diabetes. Using an in vitro model of human myotubes and an in vivo model of diet-induced obesity and insulin resistance in male mice, we provide evidence that glutamine levels directly influence the inflammatory response of skeletal muscle and regulate the expression of the adaptor protein GRB10, an inhibitor of insulin signaling. Moreover, we demonstrate that a systemic increase in glutamine levels in a mouse model of obesity improves insulin sensitivity and restores glucose homeostasis. We conclude that glutamine supplementation may represent a potential therapeutic strategy to prevent or delay the onset of insulin resistance in obesity by reducing inflammatory markers and promoting skeletal muscle insulin sensitivity.

Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review

OBJECTIVE

To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure.

RECENT FINDINGS

Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems.

CONCLUSIONS

There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.

An Untargeted LC-MS based approach for identification of altered metabolites in blood plasma of rheumatic heart disease patients

Rheumatic heart disease (RHD) is often considered as a disease of developing countries and India is the home of about 40% of RHD patients. Environment seems to play a major role in its causation. Since gene environment interactions can lead to alterations of various metabolic pathways, identification of altered metabolites can help in understanding the various pathways leading to RHD. Blood plasma samples from 51 RHD and 49 healthy controls were collected for the study. Untargeted metabolomics approach was used to identify the metabolites that are altered in RHD patients. Data showed 25 altered metabolites among RHD patients. These altered metabolites were those involved in Purine, Glutamine, Glutamate, Pyrimidine, Arginine, Proline and Linoleic metabolism. Thus, the present study illuminates metabolic alterations among RHD patients which can help in determining the potential therapeutic targets.

Metabolomic Profiles in Childhood and Adolescence Are Associated with Fetal Overnutrition

Fetal overnutrition predisposes offspring to increased metabolic risk. The current study used metabolomics to assess sustained differences in serum metabolites across childhood and adolescence among youth exposed to three typologies of fetal overnutrition: maternal obesity only, gestational diabetes mellitus (GDM) only, and obesity + GDM. We included youth exposed in utero to obesity only (BMI ≥ 30; n = 66), GDM only (n = 56), obesity + GDM (n = 25), or unexposed (n = 297), with untargeted metabolomics measured at ages 10 and 16 years. We used linear mixed models to identify metabolites across both time-points associated with exposure to any overnutrition, using a false-discovery-rate correction (FDR) <0.20. These metabolites were included in a principal component analysis (PCA) to generate profiles and assess metabolite profile differences with respect to overnutrition typology (adjusted for prenatal smoking, offspring age, sex, and race/ethnicity). Fetal overnutrition was associated with 52 metabolites. PCA yielded four factors accounting for 17−27% of the variance, depending on age of measurement. We observed differences in three factor patterns with respect to overnutrition typology: sphingomyelin-mannose (8−13% variance), skeletal muscle metabolism (6−10% variance), and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF; 3−4% variance). The sphingomyelin-mannose factor score was higher among offspring exposed to obesity vs. GDM. Exposure to obesity + GDM (vs. GDM or obesity only) was associated with higher skeletal muscle metabolism and CMPF scores. Fetal overnutrition is associated with metabolic changes in the offspring, but differences between typologies of overnutrition account for a small amount of variation in the metabolome, suggesting there is likely greater pathophysiological overlap than difference.

The structural basis of histone modifying enzyme specificity and promiscuity: Implications for metabolic regulation and drug design

Histone modifying enzymes regulate chromatin architecture through covalent modifications and ultimately control multiple aspects of cellular function. Disruption of histone modification leads to changes in gene expression profiles and may lead to disease. Both small molecule inhibitors and intermediary metabolites have been shown to modulate histone modifying enzyme activity although our ability to identify successful drug candidates or novel metabolic regulators of these enzymes has been limited. Using a combination of large scale in silico screens and in vivo phenotypic analysis, we identified several small molecules and intermediary metabolites with distinctive HME activity. Our approach using unsupervised learning identifies the chemical fingerprints of both small molecules and metabolites that facilitate recognition by the enzymes active sites which can be used as a blueprint to design novel inhibitors. Furthermore, this work supports the idea that histone modifying enzymes sense intermediary metabolites integrating genes, environment and cellular physiology.

Identification of Biomarkers for Preeclampsia Based on Metabolomics

Background

Preeclampsia (PE) is a significant cause of maternal and neonatal morbidity and mortality worldwide. However, the pathogenesis of PE is unclear and reliable early diagnostic methods are still lacking. The purpose of this review is to summarize potential metabolic biomarkers and pathways of PE, which might facilitate risk prediction and clinical diagnosis, and obtain a better understanding of specific metabolic mechanisms of PE.

Methods

This review included human metabolomics studies related to PE in the PubMed, Google Scholar, and Web of Science databases from January 2000 to November 2021. The reported metabolic biomarkers were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0.

Results

Forty-one human studies were included in this systematic review. Several metabolites, such as creatinine, glycine, L-isoleucine, and glucose and biomarkers with consistent trends (decanoylcarnitine, 3-hydroxyisovaleric acid, and octenoylcarnitine), were frequently reported. In addition, eight amino acid metabolism-related, three carbohydrate metabolism-related, one translation-related and one lipid metabolism-related pathways were identified. These biomarkers and pathways, closely related to renal dysfunction, insulin resistance, lipid metabolism disorder, activated inflammation, and impaired nitric oxide production, were very likely to contribute to the progression of PE.

Conclusion

This study summarized several metabolites and metabolic pathways, which may be associated with PE. These high-frequency differential metabolites are promising to be biomarkers of PE for early diagnosis, and the prominent metabolic pathway may provide new insights for the understanding of the pathogenesis of PE.

Individual and joint effects of trehalose and glutamate on diabetic retinopathy: a propensity score-matched case-control study

Although previous studies demonstrate that trehalose can help maintain glucose homeostasis in healthy humans, its role and joint effect with glutamate on diabetic retinopathy (DR) remain unclear. We aimed to comprehensively quantify the associations of trehalose and glutamate with DR. This study included 69 pairs of DR and matched type 2 diabetic (T2D) patients. Serum trehalose and glutamate were determined via ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system. Covariates were collected by a standardized questionnaire, clinical examinations and laboratory assessments. Individual and joint association of trehalose and glutamate with DR were quantified by multiple conditional logistic regression models. The adjusted odds of DR averagely decreased by 86% (odds ratio (OR): 0.14; 95% CI: 0.06, 0.33) with per interquartile range increase of trehalose. Comparing with the lowest quartile, adjusted OR (95% CI) were 0.20 (0.05, 0.83), 0.14 (0.03, 0.63) and 0.01 (<0.01, 0.05) for participants in the second, third and fourth quartiles of trehalose, respectively. In addition, as compared to their counterparts, T2D patients with lower trehalose (<median) and higher glutamate (≥median) had the highest odds of DR (OR: 36.81; 95% CI: 6.75, 200.61). An apparent super-multiplicative effect of trehalose and glutamate on DR was observed, whereas relative excess risk due to interaction was not significant. The study suggests that trehalose is beneficial to inhibit the occurrence of DR and synergistically decreases the risk of DR with reduced glutamate. Our findings also provide new insights into the mechanisms of DR and further longitudinal studies are required to confirm these findings.

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Healthy lifestyle, metabolomics and incident type 2 diabetes in a population-based cohort from Spain

Background

The contribution of metabolomic factors to the association of healthy lifestyle with type 2 diabetes risk is unknown. We assessed the association of a composite measure of lifestyle with plasma metabolite profiles and incident type 2 diabetes, and whether relevant metabolites can explain the prospective association between healthy lifestyle and incident type 2 diabetes.

Methods

A Healthy Lifestyle Score (HLS) (5-point scale including diet, physical activity, smoking status, alcohol consumption and BMI) was estimated in 1016 Hortega Study participants, who had targeted plasma metabolomic determinations at baseline examination in 2001–2003, and were followed-up to 2015 to ascertain incident type 2 diabetes.

Results

The HLS was cross-sectionally associated with 32 (out of 49) plasma metabolites (2.5% false discovery rate). In the subset of 830 participants without prevalent type 2 diabetes, the rate ratio (RR) and rate difference (RD) of incident type 2 diabetes (n cases = 51) per one-point increase in HLS was, respectively, 0.69 (95% CI, 0.51, 0.93), and − 8.23 (95% CI, − 16.34, − 0.13)/10,000 person-years. In single-metabolite models, most of the HLS-related metabolites were prospectively associated with incident type 2 diabetes. In probit Bayesian Kernel Machine Regression, these prospective associations were mostly driven by medium HDL particle concentration and phenylpropionate, followed by small LDL particle concentration, which jointly accounted for ~ 50% of the HLS-related decrease in incident type 2 diabetes.

Conclusions

The HLS showed a strong inverse association with incident type 2 diabetes, which was largely explained by plasma metabolites measured years before the clinical diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-021-01219-3.

San-Huang-Yi-Shen Capsule Ameliorates Diabetic Nephropathy in Rats Through Modulating the Gut Microbiota and Overall Metabolism

San-Huang-Yi-Shen capsule (SHYS) has been used in the treatment of diabetic nephropathy (DN) in clinic. However, the mechanisms of SHYS on DN remain unknown. In this study, we used a high-fat diet (HFD) combined with streptozotocin (STZ) injection to establish a DN rat model. Next, we used 16S rRNA sequencing and untargeted metabolomics to study the potential mechanisms of SHYS on DN. Our results showed that SHYS treatment alleviated the body weight loss, hyperglycemia, proteinuria, pathological changes in kidney in DN rats. SHYS could also inhibite the oxidative stress and inflammatory response in kidney. 16S rRNA sequencing analysis showed that SHYS affected the beta diversity of gut microbiota community in DN model rats. SHYX could also decrease the Firmicutes to Bacteroidetes (F to B) ratio in phylum level. In genus level, SHYX treatment affected the relative abundances of Lactobacillus, Ruminococcaceae UCG-005, Allobaculum, Anaerovibrio, Bacteroides and Candidatus_Saccharimonas. Untargeted metabolomics analysis showed that SHYX treatment altered the serum metabolic profile in DN model rats through affecting the levels of guanidineacetic acid, L-kynurenine, prostaglandin F1α, threonine, creatine, acetylcholine and other 21 kind of metabolites. These metabolites are mainly involved in glycerophospholipid metabolism, tryptophan metabolism, alanine, aspartate and glutamate metabolism, arginine biosynthesis, tricarboxylic acid (TCA) cycle, tyrosine metabolism, arginine and proline metabolism, arginine and proline metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, and D-glutamine and D-glutamate metabolism pathways. Spearman correlation analysis showed that Lactobacillus, Candidatus_Saccharimonas, Ruminococcaceae UCG-005, Anaerovibrio, Bacteroides, and Christensenellaceae_R-7_group were closely correlated with most of physiological data and the differential metabolites following SHYS treatment. In conclusion, our study revealed multiple ameliorative effects of SHYS on DN including the alleviation of hyperglycemia and the improvement of renal function, pathological changes in kidney, oxidative stress, and the inflammatory response. The mechanism of SHYS on DN may be related to the improvement of gut microbiota which regulates arginine biosynthesis, TCA cycle, tyrosine metabolism, and arginine and proline metabolism.

Proteomic Analysis of Mouse Kidney Tissue Associates Peroxisomal Dysfunction with Early Diabetic Kidney Disease

Background: The absence of efficient inhibitors for diabetic kidney disease (DKD) progression reflects the gaps in our understanding of DKD molecular pathogenesis. Methods: A comprehensive proteomic analysis was performed on the glomeruli and kidney cortex of diabetic mice with the subsequent validation of findings in human biopsies and omics datasets, aiming to better understand the underlying molecular biology of early DKD development and progression. Results: LC–MS/MS was employed to analyze the kidney proteome of 2 DKD models: Ins2Akita (early and late DKD) and db/db mice (late DKD). The abundance of detected proteins was defined. Pathway analysis of differentially expressed proteins in the early and late DKD versus the respective controls predicted dysregulation in DKD hallmarks (peroxisomal lipid metabolism and β-oxidation), supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, the consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal and upregulated mitochondrial proteins. Tissue sections from 16 DKD patients were analyzed by IHC confirming our results. Conclusion: Our study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity, providing new perspectives and potential markers of diabetic kidney dysfunction.

Using Metabolomics in Diabetes Management with Traditional Chinese Medicine: A Review

The incidence of diabetes worldwide continues to rise, placing a huge economic and medical burden on human society. More than 90% of diabetic cases are type 2 diabetes (T2D). At present, the pathogenesis of T2D is not yet fully understood. Metabolomics uses high-resolution analytical techniques (typically NMR and MS) to help identify biomarkers associated with the risk of T2D and reveal potential pathogenesis. Many metabolites such as branched-chain amino acids (BCAAs), aromatic amino acids, glycine, 2-hydroxybutyric acid (2-HB), lysophosphatidylcholine (LPC) (18:2), and trehalose have proven to be biomarkers of T2D. Insulin resistance (IR) induced by BCAA in T2D mice is related to the activation of mammalian target of rapamycin (mTOR) and phosphorylation of insulin receptor substrate-1 (IRS1). Incomplete LCFA [Formula: see text]-oxidation promote acylcarnitine byproduct accumulation and stimulates proinflammatory NF[Formula: see text]B-related pathways to inhibit insulin action. Traditional Chinese Medicine (TCM) presents unique advantages in the treatment of T2D. Multiple metabolites and metabolic pathways have been identified in the treatment of TCM, providing valuable biomarkers and novel targets for drug therapy and pharmacological mechanism. Therefore, this paper reviews the modern achievements of metabolomics in T2D research and the progress of TCM management in recent years, in order to provide valuable information for related research.

The Interplay between Pathophysiological Pathways in Early-Onset Severe Preeclampsia Unveiled by Metabolomics

INTRODUCTION

Preeclampsia is a multi-system disorder unique to pregnancy responsible for a great part of maternal and perinatal morbidity and mortality. The precise pathogenesis of this complex disorder is still unrevealed.

METHODS

We examined the pathophysiological pathways involved in early-onset preeclampsia, a specific subgroup representing its most severe presentation, using LC-MS/MS metabolomic analysis based on multi-level extraction of lipids and small metabolites from maternal blood samples, collected at the time of diagnosis from 14 preeclamptic and six matched healthy pregnancies. Statistical analysis comprised multivariate and univariate approaches with the application of over representation analysis to identify differential pathways.

RESULTS

A clear difference between preeclamptic and control pregnancies was observed in principal component analysis. Supervised multivariate analysis using orthogonal partial least square discriminant analysis provided a robust model with goodness of fit (R²X = 0.91, p = 0.002) and predictive ability (Q²Y = 0.72, p < 0.001). Finally, univariate analysis followed by 5% false discovery rate correction indicated 82 metabolites significantly altered, corresponding to six overrepresented pathways: (1) aminoacyl-tRNA biosynthesis; (2) arginine biosynthesis; (3) alanine, aspartate and glutamate metabolism; (4) D-glutamine and D-glutamate metabolism; (5) arginine and proline metabolism; and (6) histidine metabolism.

CONCLUSION

Metabolomic analysis focusing specifically on the early-onset severe form of preeclampsia reveals the interplay between pathophysiological pathways involved in this form. Future studies are required to explore new therapeutic approaches targeting these altered metabolic pathways in early-onset preeclampsia.

Obesity-Related Adipose Tissue Remodeling in the Light of Extracellular Mitochondria Transfer

Adipose tissue dysfunction is strongly associated with obesity and its metabolic complications such as type 2 diabetes and cardiovascular diseases. It is well established that lipid-overloaded adipose tissue produces a large range of secreted molecules that contribute a pro-inflammatory microenvironment which subsequently disseminates towards multi-organ metabolic homeostasis disruption. Besides physiopathological contribution of adipose-derived molecules, a new paradigm is emerging following the discovery that adipocytes have a propensity to extrude damaged mitochondria in the extracellular space, to be conveyed through the blood and taken up by cell acceptors, in a process called intercellular mitochondria transfer. This review summarizes the discovery of mitochondria transfer, its relation to cell quality control systems and recent data that demonstrate its relevant implication in the context of obesity-related adipose tissue dysfunction.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.