Plasma Metabolites Associate with All-Cause Mortality in Individuals with Type 2 Diabetes

Bilirubin bioconversion to urobilin in the gut-liver-kidney axis: A biomarker for insulin resistance in the Cardiovascular-Kidney-Metabolic (CKM) Syndrome

The rising rates of obesity worldwide have increased the incidence of cardiovascular disease (CVD), making it the number one cause of death. Higher plasma bilirubin levels have been shown to prevent metabolic dysfunction and CVD. However, reducing levels leads to deleterious outcomes, possibly due to reduced bilirubin half-life that escalates the production of its catabolized product, urobilinogen, produced by gut bacteria and naturally oxidized to urobilin. Recent findings suggest that the involvement of the microbiome catabolism of bilirubin to urobilin and its absorption via the hepatic portal vein contributes to CVD, suggesting a liver-gut axis involvement. We discuss the studies that demonstrate that urobilin is frequently raised in the urine of persons with CVD and its probable role in acquiring the disease. Urobilin is excreted from the kidneys into the urine and may serve as a biomarker for Cardiovascular-Kidney-Metabolic (CKM) Syndrome. We deliberate on the newly discovered bilirubin reductase (BilR) bacterial enzyme that produces urobilin. We discuss the bacterial species expressing BilR, how they impact CVD, and whether suppressing urobilin production and increasing bilirubin may provide new therapeutic strategies for CKM. Possible therapeutic mechanisms for achieving this goal are discussed.

Heme oxygenase, biliverdin reductase, and bilirubin pathways regulate oxidative stress and insulin resistance: a focus on diabetes and therapeutics

Metabolic and insulin-resistant diseases, such as type 2 diabetes mellitus (T2DM), have become major health issues worldwide. The prevalence of insulin resistance in the general population ranges from 15.5% to 44.6%. Shockingly, the global T2DM population is anticipated to double by 2050 compared with 2021. Prior studies indicate that oxidative stress and inflammation are instrumental in causing insulin resistance and instigating metabolic diseases. Numerous methods and drugs have been designed to combat insulin resistance, including metformin, thiazolidinediones (TZDs), sodium-glucose cotransporter 2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1RA), and dipeptidyl peptidase 4 inhibitors (DPP4i). Bilirubin is an antioxidant with fat-burning actions by binding to the PPARα nuclear receptor transcription factor, improving insulin sensitivity, reducing inflammation, and reversing metabolic dysfunction. Potential treatment with antioxidants like bilirubin and increasing the enzyme that produces it, heme oxygenase (HMOX), has also gained attention. This review discusses the relationships between bilirubin, HMOX, and insulin sensitivity, how T2DM medications affect HMOX levels and activity, and potentially using bilirubin nanoparticles to treat insulin resistance. We explore the sex differences between these treatments in the HMOX system and how bilirubin levels are affected. We discuss the emerging concept that bilirubin bioconversion to urobilin may have a role in metabolic diseases. This comprehensive review summarizes our understanding of bilirubin functioning as a hormone, discusses the HMOX isoforms and their beneficial mechanisms, analyzes the sex differences that might cause a dichotomy in responses, and examines the potential use of HMOX and bilirubin nanoparticle therapies in treating metabolic diseases.

Urobilin Derived from Bilirubin Bioconversion Binds Albumin and May Interfere with Bilirubin Interacting with Albumin: Implications for Disease Pathology

Background/Objectives: Bilirubin is a hydrophobic molecule that binds the carrier protein albumin for transport through systemic circulation. Bilirubin is cleared from the body through the liver and excreted into the intestines, where the microbiota modifies the chemical structure, forming urobilin, which can be reabsorbed into circulation by the hepatic portal vein. Urobilin has no known function. It is also unknown whether urobilin binds albumin for transport in circulation. We hypothesized that because of the likeness of their chemical structures, urobilin would also bind albumin like bilirubin does. Methods: First, we used in silico docking to predict if urobilin would bind to albumin and compared it to the bilirubin binding sites. To test this binding in vitro, we applied bilirubin's fluorescent property, which occurs when it is bound to a protein, including albumin, and exposed to light. We also used this method to determine if urobilin could exhibit autofluorescence when protein bound. Results: We found that bilirubin was predicted to bind albumin at amino acids E208, K212, D237, and K240 through hydrogen bonds. However, urobilin was predicted to bind albumin using different hydrogen bonds at amino acids H67, K240, and E252. We found that urobilin has a fluorescent property that can be quantified when bound to albumin. We performed a concentration response for urobilin-albumin fluorescent binding and observed a direct relationship between the urobilin level and the fluorescence intensity. Conclusions: The in silico docking analysis and autofluorescence results demonstrate that urobilin binds to albumin and might compete with bilirubin. This is the first study to identify a urobilin-binding protein and the important aspects of its physiological function and transport in circulation.

Gut microbiota and bilirubin metabolism: unveiling new pathways in health and disease

Bilirubin reductase (BilR), a gut microbiota-derived enzyme that reduces bilirubin to urobilinogen, was recently identified. Given the role of bilirubin in preventing modern diseases, understanding the link between the gut microbiota and health via modulation of bilirubin metabolism marks a major advance in medical research and potential treatments.

Circulating dimethylguanidino valeric acid, dietary factors, and risk of coronary heart disease

AIMS

Circulating dimethylguanidino valeric acid (DMGV) was identified as a novel metabolite related to cardiorespiratory fitness and cardiometabolic abnormalities. Circulating DMGV levels are subjective to dietary modulation; however, studies on its associations with intakes of coronary heart disease (CHD)-related foods/nutrients are limited. We investigated whether plasma DMGV was related to risk of incident CHD. We tested associations of DMGV with CHD-related dietary intakes measured by 7-day dietary records and estimated corresponding disease risk.

METHODS AND RESULTS

This nested case-control study on the incidence of CHD included 1520 women (760 incident cases of fatal CHD and nonfatal myocardial infarction and 760 controls) from the Nurses' Health Study. Separately, plasma DMGV and CHD-related dietary intakes and cardiometabolic abnormalities were assessed in the Women's Lifestyle Validation Study (WLVS; n = 724). Higher plasma DMGV was related to a greater risk of CHD [relative risk (RR) per 1 SD, 1.26 (95% CI 1.13, 1.40); P-for-linearity = 0.006]. Greater intakes of sodium, energy-dense foods, and processed/red meat were related to higher DMGV levels; every 1 SD intake of sodium was associated with β 0.13 (SE 0.05; P = 0.007) for DMGV Z-scores, which corresponded to a RR of 1.031 (1.016, 1.046) for CHD. High DMGV (the top quartile, Q4) showed a significant RR of 1.60 (1.17, 2.18) after adjusting for diet and lifestyle factors; the RR further adjusting for obesity and hypertension was 1.29 (0.93, 1.79) as compared with the lowest quartile. In both cohorts, greater adiposity and adverse cardiometabolic factor status were significantly related to higher DMGV levels.

CONCLUSION

Higher levels of plasma DMGV, a metabolite reflecting unfavourable CHD-related dietary intakes, were associated with an increased risk of CHD. The unfavourable association was attenuated by cardiometabolic risk factor status. Our study underscores the potential importance of plasma DMGV as an early biomarker associated with diet and the long-term risk of CHD among women.

Metabolomics in atrial fibrillation - A review and meta-analysis of blood, tissue and animal models

BACKGROUND

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia associated with severe cardiovascular complications. AF presents a growing global challenge, however, current treatment strategies for AF do not address the underlying pathophysiology. To advance diagnosis and treatment of AF, a deeper understanding of AF root causes is needed. Metabolomics is a fast approach to identify, quantify and analyze metabolites in a given sample, such as human serum or atrial tissue. In the past two decades, metabolomics have enabled research on metabolite biomarkers to predict AF, metabolic features of AF, and testing metabolic mechanisms of AF in animal models. Due to the field's rapid evolution, the methods of AF metabolomics studies have not always been optimal. Metabolomics research has lacked standardization and requires expertise to face methodological challenges.

PURPOSE OF THE REVIEW

We summarize and meta-analyze metabolomics research on AF in human plasma and serum, atrial tissue, and animal models. We present the current progress on metabolic biomarkers candidates, metabolic features of clinical AF, and the translation of metabolomics findings from animal to human. We additionally discuss strengths and weaknesses of the metabolomics method and highlight opportunities for future AF metabolomics research.

Metabolite signatures of chronological age, aging, survival, and longevity

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

Causal relationship between diabetes mellitus and lung cancer: a two-sample Mendelian randomization and mediation analysis

Background

Diabetes mellitus (DM) is the common comorbidity with lung cancer (LC), and metabolic disorders have been identified as significant contributors to the pathogenesis of both DM and LC. The causality between diabetes mellitus and lung cancer is still controversial. Hence, the causal effects of DM on the risk of LC was systemically investigated, and the mediating role of blood metabolites in this relationship was further explored.

Methods

This study utilized a comprehensive Mendelian randomization (MR) analysis to investigate the association between diabetes mellitus and lung cancer. The inverse variance weighted method was employed as the principle approach. MR Egger and weighted median were complementary calculations for MR assessment. A two-step MR analysis was performed to evaluate the mediating effects of blood metabolites as potential intermediate factors. Simultaneously, sensitivity analyses were performed to confirm the lack of horizontal pleiotropy and heterogeneity.

Results

The two-sample MR analysis illustrated the overall effect of type 1 diabetes mellitus (T1DM) on lung squamous cell carcinoma (LUSC) (OR: 1.040, 95% CI: 1.010-1.072, p = 0.009). No causal connection was found between T2DM and the subtypes of lung cancer. Two-step MR identified two candidate mediators partially mediating the total effect of T1DM on LUSC, including glutamine conjugate of C6H10O2 levels (17.22%) and 2-hydroxyoctanoate levels (5.85%).

Conclusion

Our findings supported a potentially causal effect of T1DM against LUSC, and shed light on the importance of metabolites as risk factors in understanding this relationship.

Physical performance and plasma metabolic profile as potential prognostic factors in metastatic lung cancer patients

BACKGROUND

Low physical performance is associated with higher mortality rate in multiple pathological conditions. Here, we aimed to determine whether body composition and physical performance could be prognostic factors in non-small cell lung cancer (NSCLC) patients. Moreover, we performed an exploratory approach to determine whether plasma samples from NSCLC patients could directly affect metabolic and structural phenotypes in primary muscle cells.

METHODS

This prospective cohort study included 55 metastatic NSCLC patients and seven age-matched control subjects. Assessments included physical performance, body composition, quality of life and overall survival rate. Plasma samples from a sub cohort of 18 patients were collected for exploratory studies in cell culture and metabolomic analysis.

RESULTS

We observed a higher survival rate in NSCLC patients with high performance in the timed up-and-go (+320%; p = .007), sit-to-stand (+256%; p = .01) and six-minute walking (+323%; p = .002) tests when compared to NSCLC patients with low physical performance. There was no significant association for similar analysis with body composition measurements (p > .05). Primary human myotubes incubated with plasma from NSCLC patients with low physical performance had impaired oxygen consumption rate (-54.2%; p < .0001) and cell proliferation (-44.9%; p = .007). An unbiased metabolomic analysis revealed a list of specific metabolites differentially expressed in the plasma of NSCLC patients with low physical performance.

CONCLUSION

These novel findings indicate that physical performance is a prognostic factor for overall survival in NSCLC patients and provide novel insights into circulating factors that could impair skeletal muscle metabolism.

Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health

Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations: a low number of participants and metabolites measured, measurements mainly using nuclear magnetic spectroscopy, and the use only of conventional statistical methods. To overcome these challenges, we applied liquid chromatography-tandem mass spectrometry and measured >1000 metabolites in the METSIM study including 10,197 men. We applied the machine learning approach together with conventional statistical methods to identify metabolites associated with all-cause mortality. The three independent machine learning methods (logistic regression, XGBoost, and Welch's t-test) identified 32 metabolites having the most impactful associations with all-cause mortality (25 increasing and 7 decreasing the risk). From these metabolites, 20 were novel and encompassed various metabolic pathways, impacting the cardiovascular, renal, respiratory, endocrine, and central nervous systems. In the Cox regression analyses (hazard ratios and their 95% confidence intervals), clinical and laboratory risk factors increased the risk of all-cause mortality by 1.76 (1.60-1.94), the 25 metabolites by 1.89 (1.68-2.12), and clinical and laboratory risk factors combined with the 25 metabolites by 2.00 (1.81-2.22). In our study, the main causes of death were cancers (28%) and cardiovascular diseases (25%). We did not identify any metabolites associated with cancer but found 13 metabolites associated with an increased risk of cardiovascular diseases. Our study reports several novel metabolites associated with an increased risk of mortality and shows that these 25 metabolites improved the prediction of all-cause mortality beyond and above clinical and laboratory measurements.

Duality of Branched-Chain Amino Acids in Chronic Cardiovascular Disease: Potential Biomarkers versus Active Pathophysiological Promoters

Branched-chain amino acids (BCAAs), comprising leucine (Leu), isoleucine (Ile), and valine (Val), are essential nutrients vital for protein synthesis and metabolic regulation via specialized signaling networks. Their association with cardiovascular diseases (CVDs) has become a focal point of scientific debate, with emerging evidence suggesting both beneficial and detrimental roles. This review aims to dissect the multifaceted relationship between BCAAs and cardiovascular health, exploring the molecular mechanisms and clinical implications. Elevated BCAA levels have also been linked to insulin resistance (IR), type 2 diabetes mellitus (T2DM), inflammation, and dyslipidemia, which are well-established risk factors for CVD. Central to these processes are key pathways such as mammalian target of rapamycin (mTOR) signaling, nuclear factor kappa-light-chain-enhancer of activate B cells (NF-κB)-mediated inflammation, and oxidative stress. Additionally, the interplay between BCAA metabolism and gut microbiota, particularly the production of metabolites like trimethylamine-N-oxide (TMAO), adds another layer of complexity. Contrarily, some studies propose that BCAAs may have cardioprotective effects under certain conditions, contributing to muscle maintenance and metabolic health. This review critically evaluates the evidence, addressing the biological basis and signal transduction mechanism, and also discusses the potential for BCAAs to act as biomarkers versus active mediators of cardiovascular pathology. By presenting a balanced analysis, this review seeks to clarify the contentious roles of BCAAs in CVD, providing a foundation for future research and therapeutic strategies required because of the rising prevalence, incidence, and total burden of CVDs.

Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow-Up in the General Population

BACKGROUND

Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality.

METHODS AND RESULTS

We included 6865 individuals from 2 Swedish population-based cohorts. Untargeted plasma metabolomics was performed by liquid-chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid-femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite-predicted aortic stiffness and risk of new-onset cardiovascular disease, cardiovascular mortality, and all-cause mortality were calculated. Metabolite-predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R2=0.27 and R2=0.39, respectively). Metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly correlated with age, sex, systolic blood pressure, body mass index, and low-density lipoprotein. After 23 years of follow-up, metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly associated with increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality.

CONCLUSIONS

Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality after more than 23 years of follow-up in the general population.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

Metabolomics signature of cardiovascular disease in patients with diabetes, a narrative review

Objectives

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

Methods

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Results

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Conclusions

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

Phenylacetylglutamine as a novel biomarker of type 2 diabetes with distal symmetric polyneuropathy by metabolomics

PURPOSE

Type 2 diabetes mellitus (T2DM) with distal symmetric polyneuropathy (DSPN) is a disease involving the nervous system caused by metabolic disorder, while the metabolic spectrum and key metabolites remain poorly defined.

METHODS

Plasma samples of 30 healthy controls, 30 T2DM patients, and 60 DSPN patients were subjected to nontargeted metabolomics. Potential biomarkers of DSPN were screened based on univariate and multivariate statistical analyses, ROC curve analysis, and logistic regression. Finally, another 22 patients with T2DM who developed DSPN after follow-up were selected for validation of the new biomarker based on target metabolomics.

RESULTS

Compared with the control group and the T2DM group, 6 metabolites showed differences in the DSPN group (P < 0.05; FDR < 0.1; VIP > 1) and a rising step trend was observed. Among them, phenylacetylglutamine (PAG) and sorbitol displayed an excellent discriminatory ability and associated with disease severity. The verification results demonstrated that when T2DM progressed to DSPN, the phenylacetylglutamine content increased significantly (P = 0.004).

CONCLUSION

The discovered and verified endogenous metabolite PAG may be a novel potential biomarker of DSPN and involved in the disease pathogenesis.

Association of Plasma Metabolomic Biomarkers With Persistent Tinnitus: A Population-Based Case-Control Study

Importance

Persistent tinnitus is common, disabling, and difficult to treat.

Objective

To evaluate the association between circulating metabolites and persistent tinnitus.

Design, Setting, and Participants

This was a population-based case-control study of 6477 women who were participants in the Nurses' Health Study (NHS) and NHS II with metabolomic profiles and tinnitus data. Information on tinnitus onset and frequency was collected on biennial questionnaires (2009-2017). For cases, metabolomic profiles were measured (2015-2021) in blood samples collected after the date of the participant's first report of persistent tinnitus (NHS, 1989-1999 and 2010-2012; NHS II, 1996-1999). Data analyses were performed from January 24, 2022, to January 14, 2023.

Exposures

In total, 466 plasma metabolites from 488 cases of persistent tinnitus and 5989 controls were profiled using 3 complementary liquid chromatography tandem mass spectrometry approaches.

Main Outcomes and Measures

Logistic regression was used to estimate odds ratios (ORs) of persistent tinnitus (per 1 SD increase in metabolite values) and 95% CIs for each individual metabolite. Metabolite set enrichment analysis was used to identify metabolite classes enriched for associations with tinnitus.

Results

Of the 6477 study participants (mean [SD] age, 52 [9] years; 6477 [100%] female; 6121 [95%] White individuals) who were registered nurses, 488 reported experiencing daily persistent (≥5 minutes) tinnitus. Compared with participants with no tinnitus (5989 controls), those with persistent tinnitus were slightly older (53.0 vs 51.8 years) and more likely to be postmenopausal, using oral postmenopausal hormone therapy, and have type 2 diabetes, hypertension, and/or hearing loss at baseline. Compared with controls, homocitrulline (OR, 1.32; (95% CI, 1.16-1.50); C38:6 phosphatidylethanolamine (PE; OR, 1.24; 95% CIs, 1.12-1.38), C52:6 triglyceride (TAG; OR, 1.22; 95% CIs, 1.10-1.36), C36:4 PE (OR, 1.22; 95% CIs, 1.10-1.35), C40:6 PE (OR, 1.22; 95% CIs, 1.09-1.35), and C56:7 TAG (OR, 1.21; 95% CIs, 1.09-1.34) were positively associated, whereas α-keto-β-methylvalerate (OR, 0.68; 95% CIs, 0.56-0.82) and levulinate (OR, 0.60; 95% CIs, 0.46-0.79) were inversely associated with persistent tinnitus. Among metabolite classes, TAGs (normalized enrichment score [NES], 2.68), PEs (NES, 2.48), and diglycerides (NES, 1.65) were positively associated, whereas phosphatidylcholine plasmalogens (NES, -1.91), lysophosphatidylcholines (NES, -2.23), and cholesteryl esters (NES,-2.31) were inversely associated with persistent tinnitus.

Conclusions and Relevance

This population-based case-control study of metabolomic profiles and tinnitus identified novel plasma metabolites and metabolite classes that were significantly associated with persistent tinnitus, suggesting that metabolomic studies may help improve understanding of tinnitus pathophysiology and identify therapeutic targets for this challenging disorder.

Mixed meal tolerance testing highlights in diabetes altered branched-chain ketoacid metabolism and pathways associated with all-cause mortality

BACKGROUND

Elevated BCAA levels are strongly associated with diabetes, but how diabetes affects BCAA, branched-chain ketoacids (BCKAs), and the broader metabolome after a meal is not well known.

OBJECTIVE

To compare quantitative BCAA and BCKA levels in a multiracial cohort with and without diabetes after a mixed meal tolerance test (MMTT) as well as to explore the kinetics of additional metabolites and their associations with mortality in self-identified African Americans.

METHODS

We administered an MMTT to 11 participants without obesity or diabetes and 13 participants with diabetes (treated with metformin only) and measured the levels of BCKAs, BCAAs, and 194 other metabolites at 8 time points across 5 h. We used mixed models for repeated measurements to compare between group metabolite differences at each timepoint with adjustment for baseline. We then evaluated the association of top metabolites with different kinetics with all-cause mortality in the Jackson Heart Study (JHS) (N = 2441).

RESULTS

BCAA levels, after adjustment for baseline, were similar at all timepoints between groups, but adjusted BCKA kinetics were different between groups for α-ketoisocaproate (P = 0.022) and α-ketoisovalerate (P = 0.021), most notably diverging at 120 min post-MMTT. An additional 20 metabolites had significantly different kinetics across timepoints between groups, and 9 of these metabolites-including several acylcarnitines-were significantly associated with mortality in JHS, irrespective of diabetes status. The highest quartile of a composite metabolite risk score was associated with higher mortality (HR:1.57; 1.20, 2.05, P = 0.00094) than the lowest quartile.

CONCLUSIONS

BCKA levels remained elevated after an MMTT among participants with diabetes, suggesting that BCKA catabolism may be a key dysregulated process in the interaction of BCAA and diabetes. Metabolites with different kinetics after an MMTT may be markers of dysmetabolism and associated with increased mortality in self-identified African Americans.

Metabolomic Markers of Ultra-Processed Food and Incident CKD

BACKGROUND

High ultra-processed food consumption is associated with higher risk of CKD. However, there is no biomarker for ultra-processed food, and the mechanism through which ultra-processed food is associated with CKD is not clear. Metabolomics can provide objective biomarkers of ultra-processed food and provide important insights into the mechanisms by which ultra-processed food is associated with risk of incident CKD. Our objective was to identify serum metabolites associated with ultra-processed food consumption and investigate whether ultra-processed food-associated metabolites are prospectively associated with incident CKD.

METHODS

We used data from 3751 Black and White men and women (aged 45-64 years) in the Atherosclerosis Risk in Communities study. Dietary intake was assessed using a semiquantitative 66-item food frequency questionnaire, and ultra-processed food was classified using the NOVA classification system. Multivariable linear regression models were used to identify the association between 359 metabolites and ultra-processed food consumption. Cox proportional hazards models were used to investigate the prospective association of ultra-processed food-associated metabolites with incident CKD.

RESULTS

Twelve metabolites (saccharine, homostachydrine, stachydrine, N2, N2-dimethylguanosine, catechol sulfate, caffeine, 3-methyl-2-oxovalerate, theobromine, docosahexaenoate, glucose, mannose, and bradykinin) were significantly associated with ultra-processed food consumption after controlling for false discovery rate <0.05 and adjusting for sociodemographic factors, health behaviors, eGFR, and total energy intake. The 12 ultra-processed food-related metabolites significantly improved the prediction of ultra-processed food consumption (difference in C statistics: 0.069, P <1×10 -16 ). Higher levels of mannose, glucose, and N2, N2-dimethylguanosine were associated with higher risk of incident CKD after a median follow-up of 23 years.

CONCLUSIONS

We identified 12 serum metabolites associated with ultra-processed food consumption and three of them were positively associated with incident CKD. Mannose and N2, N2-dimethylguanosine are novel markers of CKD that may explain observed associations between ultra-processed food and CKD.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_03_08_CJN08480722.mp3.

The emergent role of mitochondrial RNA modifications in metabolic alterations

Mitochondrial epitranscriptomics refers to the modifications occurring in all the different RNA types of mitochondria. Although the number of mitochondrial RNA modifications is less than those in cytoplasm, substantial evidence indicates that they play a critical role in accurate protein synthesis. Recent evidence supported those modifications in mitochondrial RNAs also have crucial implications in mitochondrial-related diseases. In the light of current knowledge about the involvement, the association between mitochondrial RNA modifications and diseases arises from studies focusing on mutations in both mitochondrial and nuclear DNA genes encoding enzymes involved in such modifications. Here, we review the current evidence available for mitochondrial RNA modifications and their role in metabolic disorders, and we also explore the possibility of using them as promising targets for prevention and early detection. Finally, we discuss future directions of mitochondrial epitranscriptomics in these metabolic alterations, and how these RNA modifications may offer a new diagnostic and theragnostic avenue for preventive purposes. This article is categorized under: RNA Processing > RNA Editing and Modification.

Bilirubin Levels Are Negatively Correlated with Adiposity in Obese Men and Women, and Its Catabolized Product, Urobilin, Is Positively Associated with Insulin Resistance

Bilirubin levels in obese humans and rodents have been shown to be lower than in their lean counterparts. Some studies have proposed that the glucuronyl UGT1A1 enzyme that clears bilirubin from the blood increases in the liver with obesity. UGT1A1 clearance of bilirubin allows more conjugated bilirubin to enter the intestine, where it is catabolized into urobilin, which can be then absorbed via the hepatic portal vein. We hypothesized that when bilirubin levels are decreased, the urobilin increases in the plasma of obese humans, as compared to lean humans. To test this, we measured plasma levels of bilirubin and urobilin, body mass index (BMI), adiposity, blood glucose and insulin, and HOMA IR in a small cohort of obese and lean men and women. We found that bilirubin levels negatively correlated with BMI and adiposity in obese men and women, as compared to their lean counterparts. Contrarily, urobilin levels were positively associated with adiposity and BMI. Only obese women were found to be insulin resistant based on significantly higher HOMA IR, as compared to lean women. The urobilin levels were positively associated with HOMA IR in both groups, but women had a stronger linear correlation. These studies indicate that plasma urobilin levels are associated with obesity and its comorbidities, such as insulin resistance.

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.

Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling.

RESULTS

In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, β-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (β-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction.

CONCLUSIONS

This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.

A novel 6-metabolite signature for prediction of clinical outcomes in type 2 diabetic patients undergoing percutaneous coronary intervention

Background

Outcome prediction tools for patients with type 2 diabetes mellitus (T2DM) undergoing percutaneous coronary intervention (PCI) are lacking. Here, we developed a machine learning-based metabolite classifier for predicting 1-year major adverse cardiovascular events (MACEs) after PCI among patients with T2DM.

Methods

Serum metabolomic profiling was performed in a nested case–control study of 108 matched pairs of patients with T2DM occurring and not occurring MACEs at 1 year after PCI, then the matched pairs were 1:1 assigned into the discovery and internal validation sets. External validation was conducted using targeted metabolite analyses in an independent prospective cohort of 301 patients with T2DM receiving PCI. The function of candidate metabolites was explored in high glucose-cultured human aortic smooth muscle cells (HASMCs).

Results

Overall, serum metabolome profiles differed between diabetic patients with and without 1-year MACEs after PCI. Through VSURF, a machine learning approach for feature selection, we identified the 6 most important metabolic predictors, which mainly targeted the nicotinamide adenine dinucleotide (NAD⁺) metabolism. The 6-metabolite model based on random forest and XGBoost algorithms yielded an area under the curve (AUC) of ≥ 0.90 for predicting MACEs in both discovery and internal validation sets. External validation of the 6-metabolite classifier also showed good accuracy in predicting MACEs (AUC 0.94, 95% CI 0.91–0.97) and target lesion failure (AUC 0.89, 95% CI 0.83–0.95). In vitro, there were significant impacts of altering NAD⁺ biosynthesis on bioenergetic profiles, inflammation and proliferation of HASMCs.

Conclusion

The 6-metabolite model may help for noninvasive prediction of 1-year MACEs following PCI among patients with T2DM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01561-1.

Circulating Metabolites Associate With and Improve the Prediction of All-Cause Mortality in Type 2 Diabetes

Death rate is increased in type 2 diabetes. Unraveling biomarkers of novel pathogenic pathways capable to identify high-risk patients is instrumental to tackle this burden. We investigated the association between serum metabolites and all-cause mortality in type 2 diabetes and then whether the associated metabolites mediate the effect of inflammation on mortality risk and improve ENFORCE (EstimatioN oF mORtality risk in type2 diabetic patiEnts) and RECODe (Risk Equation for Complications Of type 2 Diabetes), two well-established all-cause mortality prediction models in diabetes. Two cohorts comprising 856 individuals (279 all-cause deaths) were analyzed. Serum metabolites (n = 188) and pro- and anti-inflammatory cytokines (n = 7) were measured. In the pooled analysis, hexanoylcarnitine, kynurenine, and tryptophan were significantly and independently associated with mortality (hazard ratio [HR] 1.60 [95% CI 1.43-1.80]; 1.53 [1.37-1.71]; and 0.71 [0.62-0.80] per 1 SD). The kynurenine-to-tryptophan ratio (KTR), a proxy of indoleamine-2,3-dioxygenase, which degrades tryptophan to kynurenine and contributes to a proinflammatory status, mediated 42% of the significant association between the antiatherogenic interleukin (IL) 13 and mortality. Adding the three metabolites improved discrimination and reclassification (all P < 0.01) of both mortality prediction models. In type 2 diabetes, hexanoylcarnitine, tryptophan, and kynurenine are associated to and improve the prediction of all-cause mortality. Further studies are needed to investigate whether interventions aimed at reducing KTR also reduce the risk of death, especially in patients with low IL-13.

The Kynurenine Pathway in Obese Middle-Aged Women with Normoglycemia and Type 2 Diabetes

We examined the relationships of tryptophan (Trp) and the metabolites of the kynurenine pathway (KP) to the occurrence of type 2 diabetes (T2D) and metabolic risk factors in obese middle-aged women. The study included 128 obese women divided into two subgroups: a normoglycemic group (NG, n = 65) and a T2D group (n = 63). The concentrations of serum tryptophan (Trp), kynurenine (Kyn), 3-hydroxykynurenine (3HKyn), quinolinic acid (QA), and kynurenic acid (Kyna) were analyzed using ultra-high-performance liquid chromatography coupled with electrospray ionization/triple quadrupole mass spectrometry. Blood biochemical parameters and anthropometric parameters were measured. The women with T2D had significantly higher Trp, Kyna, Kyna/QA ratio, and Kyna/3HKyn ratio values than the NG women. Logistic regression analysis showed that the concentrations of Trp and Kyna and the values of the Kyna/3HKyn ratio were most strongly associated with T2D occurrence, even after controlling for confounding factors. The model with Trp level and Kyna/3HKyn ratio accounted for 20% of the variation in the presence of T2D. We also showed a different pattern of correlations between kynurenines and metabolic factors in the NG and T2D women, which was mostly reflected in the stronger relationship between BMI and KP metabolites in the NG obese women. An increase in Trp and Kyna levels with an accompanying increase in Kyna/3HKyn ratio value is associated with the occurrence of T2D in obese middle-aged women.

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.

Using primary teeth and archived dried spots for exposomic studies in children: Exploring new paths in the environmental epidemiology of pediatric cancer

It is estimated that 300,000 children 0-14 years of age are diagnosed with cancer worldwide each year. While the absolute risk of cancer in children is low, it is the leading cause of death due to disease in children in high-income countries. In spite of this, the etiologies of pediatric cancer are largely unknown. Environmental exposures have long been thought to play an etiologic role. However, to date, there are few well-established environmental risk factors for pediatric malignancies, likely due to technical barriers in collecting biological samples prospectively in pediatric populations for direct measurements. In this review, we propose the use of novel or underutilized biospecimens (dried blood spots and teeth) and molecular approaches for exposure assessment (epigenetics, metabolomics, and somatic mutational profiles). Future epidemiologic studies of pediatric cancer should incorporate novel exposure assessment methodologies, data on molecular features of tumors, and a more complete assessment of gene-environment interactions.

In vivo toxicometabolomics reveals multi-organ and urine metabolic changes in mice upon acute exposure to human-relevant doses of 3,4-methylenedioxypyrovalerone (MDPV)

3,4-Methylenedioxypyrovalerone (MDPV) is consumed worldwide, despite its potential to cause toxicity in several organs and even death. There is a recognized need to clarify the biological pathways through which MDPV elicits general and target-organ toxicity. In this work, a comprehensive untargeted GC-MS-based metabolomics analysis was performed, aiming to detect metabolic changes in putative target organs (brain, heart, kidneys and liver) but also in urine of mice after acute exposure to human-relevant doses of MDPV. Male CD-1 mice received binge intraperitoneal administrations of saline or MDPV (2.5 mg/kg or 5 mg/kg) every 2 h, for a total of three injections. Twenty-four hours after the first administration, target organs, urine and blood samples were collected for metabolomics, biochemical and histological analysis. Hepatic and renal tissues of MDPV-treated mice showed moderate histopathological changes but no significant differences were found in plasma and tissue biochemical markers of organ injury. In contrast, the multivariate analysis significantly discriminated the organs and urine of MDPV-treated mice from the control (except for the lowest dose in the brain), allowing the identification of a panoply of metabolites. Those levels were significantly deviated in relation to physiological conditions and showed an organ specific response towards the drug. Kidneys and liver showed the greatest metabolic changes. Metabolites related with energetic metabolism, antioxidant defenses and inflammatory response were significantly changed in the liver of MDPV-dosed animals, while the kidneys seem to have developed an adaptive response against oxidative stress caused by MDPV. On the other hand, the dysregulation of metabolites that contribute to metabolic acidosis was also observed in this organ. The heart showed an increase of fatty acid biosynthesis, possibly as an adaptation to maintain the cardiac energy homeostasis. In the brain, changes in 3-hydroxybutyric acid levels may reflect the activation of a neurotoxic pathway. However, the increase in metabolites with neuroprotective properties seems to counteract this change. Metabolic profiling of urine from MDPV-treated mice suggested that glutathione-dependent antioxidant pathways may be particularly involved in the compensatory mechanism to counteract oxidative stress induced by MDPV. Overall, this study reports, for the first time, the metabolic profile of liver, kidneys, heart, brain, and urine of MDPV-dosed mice, providing unique insights into the biological pathways of toxicity. Our findings also underline the value of toxicometabolomics as a robust and sensitive tool for detecting adaptive/toxic cellular responses upon exposure to a physiologically relevant dose of a toxic agent, earlier than conventional toxicity tests.