Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Plasma metabolites to profile pathways in noncommunicable disease multimorbidity.

Multimorbidity, the simultaneous presence of multiple chronic conditions, is an increasing global health problem and research into its determinants is of high priority. We used baseline untargeted plasma metabolomics profiling covering >1,000 metabolites as a comprehensive readout of human physiology to characterize pathways associated with and across 27 incident noncommunicable diseases (NCDs) assessed using electronic health record hospitalization and cancer registry data from over 11,000 participants (219,415 person years). We identified 420 metabolites shared between at least 2 NCDs, representing 65.5% of all 640 significant metabolite-disease associations. We integrated baseline data on over 50 diverse clinical risk factors and characteristics to identify actionable shared pathways represented by those metabolites. Our study highlights liver and kidney function, lipid and glucose metabolism, low-grade inflammation, surrogates of gut microbial diversity and specific health-related behaviors as antecedents of common NCD multimorbidity with potential for early prevention. We integrated results into an open-access webserver ( https://omicscience.org/apps/mwasdisease/ ) to facilitate future research and meta-analyses.

Dynamic modelling of an ACADS genotype in fatty acid oxidation - Application of cellular models for the analysis of common genetic variants.

BACKGROUND: Genome-wide association studies of common diseases or metabolite quantitative traits often identify common variants of small effect size, which may contribute to phenotypes by modulation of gene expression. Thus, there is growing demand for cellular models enabling to assess the impact of gene regulatory variants with moderate effects on gene expression. Mitochondrial fatty acid oxidation is an important energy metabolism pathway. Common noncoding acyl-CoA dehydrogenase short chain (ACADS) gene variants are associated with plasma C4-acylcarnitine levels and allele-specific modulation of ACADS expression may contribute to the observed phenotype. METHODS AND FINDINGS: We assessed ACADS expression and intracellular acylcarnitine levels in human lymphoblastoid cell lines (LCL) genotyped for a common ACADS variant associated with plasma C4-acylcarnitine and found a significant genotype-dependent decrease of ACADS mRNA and protein. Next, we modelled gradual decrease of ACADS expression using a tetracycline-regulated shRNA-knockdown of ACADS in Huh7 hepatocytes, a cell line with high fatty acid oxidation-(FAO)-capacity. Assessing acylcarnitine flux in both models, we found increased C4-acylcarnitine levels with decreased ACADS expression levels. Moreover, assessing time-dependent changes of acylcarnitine levels in shRNA-hepatocytes with altered ACADS expression levels revealed an unexpected effect on long- and medium-chain fatty acid intermediates. CONCLUSIONS: Both, genotyped LCL and regulated shRNA-knockdown are valuable tools to model moderate, gradual gene-regulatory effects of common variants on cellular phenotypes. Decreasing ACADS expression levels modulate short and surprisingly also long/medium chain acylcarnitines, and may contribute to increased plasma acylcarnitine levels.

Molecular Fingerprints of Iron Parameters among a Population-Based Sample.

Iron deficiency is the most frequent deficiency disease and parameters of iron metabolism appear to be linked to major metabolic and cardiovascular diseases. We screened a large set of small molecules in plasma for associations with iron status among apparently healthy subjects to elucidate subclinical profiles which may provide a link between iron status and onset of diseases. Based on mass spectrometry and nuclear magnetic resonance spectroscopy we determined 613 plasma metabolites and lipoprotein subfractions among 820 apparently healthy individuals. Associations between ferritin, transferrin, haemoglobin and myoglobin and metabolite levels were tested by sex-specific linear regression analyses controlling for common confounders. Far more significant associations in women (82 out of 102) compared to men became obvious. The majority of the metabolites associated with serum ferritin and haemoglobin in women comprising fatty acid species, branched-chain amino acid catabolites and catabolites of heme. The latter was also obvious among men. Positive associations between serum transferrin and VLDL and IDL particle measures seen in women were observed in men with respect to serum ferritin. We observed a sexual-dimorphic fingerprint of surrogates of iron metabolism which may provide a link for the associations between those parameters and major metabolic and cardiovascular disease.

Accelerated lipid catabolism and autophagy are cancer survival mechanisms under inhibited glutaminolysis.

Suppressing glutaminolysis does not always induce cancer cell death in glutamine dependent tumors because cells may switch to alternative energy sources. To reveal compensatory metabolic pathways, we investigated the metabolome-wide cellular response to inhibited glutaminolysis in cancer cells. Glutaminolysis inhibition with C.968 suppressed cell proliferation but was insufficient to induce cancer cell death. We found that lipid catabolism was activated as a compensation for glutaminolysis inhibition. Accelerated lipid catabolism, together with oxidative stress induced by glutaminolysis inhibition, triggered autophagy. Simultaneously inhibiting glutaminolysis and either beta oxidation with trimetazidine or autophagy with chloroquine both induced cancer cell death. Here we identified metabolic escape mechanisms contributing to cancer cell survival under treatment and we suggest potentially translational strategy for combined cancer therapy, given that chloroquine is an FDA approved drug. Our findings are first to show efficiency of combined inhibition of glutaminolysis and beta oxidation as potential anti-cancer strategy as well as add to the evidence that combined inhibition of glutaminolysis and autophagy may be effective in glutamine-addicted cancers.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation.

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

Activated macrophages control human adipocyte mitochondrial bioenergetics via secreted factors.

OBJECTIVE: Obesity-associated WAT inflammation is characterized by the accumulation and local activation of macrophages (MΦs), and recent data from mouse studies suggest that macrophages are modifiers of adipocyte energy metabolism and mitochondrial function. As mitochondrial dysfunction has been associated with obesity and the metabolic syndrome in humans, herein we aimed to delineate how human macrophages may affect energy metabolism of white adipocytes. METHODS: Human adipose tissue gene expression analysis for markers of macrophage activation and tissue inflammation (CD11c, CD40, CD163, CD206, CD80, MCP1, TNFα) in relationship to mitochondrial complex I (NDUFB8) and complex III (UQCRC2) was performed on subcutaneous WAT of 24 women (BMI 20-61 kg/m2). Guided by these results, the impact of secreted factors of LPS/IFNγ- and IL10/TGFß-activated human macrophages (THP1, primary blood-derived) on mitochondrial function in human subcutaneous white adipocytes (SGBS, primary) was determined by extracellular flux analysis (Seahorse technology) and gene/protein expression. RESULTS: Stepwise regression analysis of human WAT gene expression data revealed that a linear combination of CD40 and CD163 was the strongest predictor for mitochondrial complex I (NDUFB8) and complex III (UQCRC2) levels, independent of BMI. IL10/TGFß-activated MΦs displayed high CD163 and low CD40 expression and secreted factors that decreased UQCRC2 gene/protein expression and ATP-linked respiration in human white adipocytes. In contrast, LPS/IFNγ-activated MΦs showed high CD40 and low CD163 expression and secreted factors that enhanced adipocyte mitochondrial activity resulting in a total difference of 37% in ATP-linked respiration of white adipocytes (p = 0.0024) when comparing the effect of LPS/IFNγ- vs IL10/TGFß-activated MΦs. CONCLUSION: Our data demonstrate that macrophages modulate human adipocyte energy metabolism via an activation-dependent paracrine mechanism.

Comprehensive metabolic characterization of serum osteocalcin action in a large non-diabetic sample.

Recent research suggested a metabolic implication of osteocalcin (OCN) in e.g. insulin sensitivity or steroid production. We used an untargeted metabolomics approach by analyzing plasma and urine samples of 931 participants using mass spectrometry to reveal further metabolic actions of OCN. Several detected relations between OCN and metabolites were strongly linked to renal function, however, a number of associations remained significant after adjustment for renal function. Intermediates of proline catabolism were associated with OCN reflecting the implication in bone metabolism. The association to kynurenine points towards a pro-inflammatory state with increasing OCN. Inverse relations with intermediates of branch-chained amino acid metabolism suggest a link to energy metabolism. Finally, urinary surrogate markers of smoking highlight its adverse effect on OCN metabolism. In conclusion, the present study provides a read-out of metabolic actions of OCN. However, most of the associations were weak arguing for a limited role of OCN in whole-body metabolism.

Metabolomic Profiling of Long-Term Weight Change: Role of Oxidative Stress and Urate Levels in Weight Gain.

OBJECTIVE: To investigate the association between long-term weight change and blood metabolites. METHODS: Change in BMI over 8.6 ± 3.79 years was assessed in 3,176 females from the TwinsUK cohort (age range: 18.3-79.6, baseline BMI: 25.11 ± 4.35) measured for 280 metabolites at follow-up. Statistically significant metabolites (adjusting for covariates) were included in a multivariable least absolute shrinkage and selection operator (LASSO) model. Findings were replicated in the Cooperative Health Research in the Region of Augsburg (KORA) study (n = 1,760; age range: 25-70, baseline BMI: 27.72 ± 4.53). The study examined whether the metabolites identified could prospectively predict weight change in KORA and in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) study (n = 471; age range: 55-74, baseline BMI: 27.24 ± 5.37). RESULTS: Thirty metabolites were significantly associated with change in BMI per year in TwinsUK using Bonferroni correction. Four were independently associated with weight change in the multivariable LASSO model and replicated in KORA: namely, urate (meta-analysis ß [95% CI] = 0.05 [0.040 to 0.063]; P = 1.37 × 10-19 ), gamma-glutamyl valine (ß [95% CI] = 0.06 [0.046 to 0.070]; P = 1.23 × 10-20 ), butyrylcarnitine (ß [95% CI] = 0.04 [0.028 to 0.051]; P = 6.72 × 10-12 ), and 3-phenylpropionate (ß [95% CI] = -0.03 [-0.041 to -0.019]; P = 9.8 × 10-8 ), all involved in oxidative stress. Higher levels of urate at baseline were associated with weight gain in KORA and PLCO. CONCLUSIONS: Metabolites linked to higher oxidative stress are associated with increased long-term weight gain.

Sex-specific metabolic profiles of androgens and its main binding protein SHBG in a middle aged population without diabetes.

The role of androgens in metabolism with respect to sex-specific disease associations is poorly understood. Therefore, we aimed to provide molecular signatures in plasma and urine of androgen action in a sex-specific manner using state-of-the-art metabolomics techniques. Our study population consisted of 430 men and 343 women, aged 20-80 years, who were recruited for the cross-sectional population-based Study of Health in Pomerania (SHIP-TREND), Germany. We used linear regression models to identify associations between testosterone, androstenedione and dehydroepiandrosterone-sulfate (DHEAS) as well as sex hormone-binding globulin and plasma or urine metabolites measured by mass spectrometry. The analyses revealed major sex-specific differences in androgen-associated metabolites, particularly for levels of urate, lipids and metabolic surrogates of lifestyle factors, like cotinine or piperine. In women, in particular in the postmenopausal state, androgens showed a greater impact on the metabolome than in men (especially DHEAS and lipids were highly related in women). We observed a novel association of androstenedione on the metabolism of biogenic amines and only a small sex-overlap of associations within steroid metabolism. The present study yields new insights in the interaction between androgens and metabolism, especially about their implication in female metabolism.

Short-term NO2 exposure is associated with long-chain fatty acids in prospective cohorts from Augsburg, Germany: results from an analysis of 138 metabolites and three exposures.

BACKGROUND: Short-term exposure to air pollution is associated with morbidity and mortality. Metabolites are intermediaries in biochemical processes, and associations between air pollution and metabolites can yield unique mechanistic insights. METHODS: We used independent cross-sectional samples with targeted metabolomics (138 metabolites across five metabolite classes) from three cohort studies, each a part of the Cooperative Health Research in the Region of Augsburg (KORA). The KORA cohorts are numbered (1 to 4) according to which survey they belong to, and lettered S or F according to whether the survey was a baseline or follow-up survey. KORA F4 (N = 3044) served as our discovery cohort, with KORA S4 (N = 485) serving as the primary replication cohort. KORA F4 and KORA S4 were primarily fasting cohorts. We used the non-fasting KORA F3 (N = 377) cohort to evaluate replicated associations in non-fasting individuals, and we performed a random effects meta-analysis of all three cohorts. Associations between the 0-4-day lags and the 5-day average of particulate matter (PM)2.5, NO2 and ozone were modelled via generalized additive models. All air pollution exposures were scaled to the interquartile range, and effect estimates presented as percent changes relative to the geometric mean of the metabolite concentration (ΔGM). RESULTS: There were 10 discovery cohort associations, of which seven were lysophosphatidylcholines (LPCs); NO2 was the most ubiquitous exposure (5/10). The 5-day average NO2-LPC(28:0) association was associated at a Bonferroni corrected P-value threshold (P < 1.2x10-4) in KORA F4 [ΔGM = 11.5%; 95% confidence interval (CI) = 6.60, 16.3], and replicated (P < 0.05) in KORA S4 (ΔGM = 21.0%; CI = 4.56, 37.5). This association was not observed in the non-fasting KORA F3 cohort (ΔGM = -5.96%; CI = -26.3, 14.3), but remained in the random effects meta-analysis (ΔGM = 10.6%; CI = 0.16, 21). CONCLUSIONS: LPCs are associated with short-term exposure to air pollutants, in particular NO2 Further research is needed to understand the effect of nutritional/fasting status on these associations and the causal mechanisms linking air pollution exposure and metabolite profiles.

Cardiovascular Risk Factors Associated With Blood Metabolite Concentrations and Their Alterations During a 4-Year Period in a Population-Based Cohort.

BACKGROUND: The effects of lifestyle risk factors considered collectively on the human metabolism are to date unknown. We aim to investigate the association of these risk factors with metabolites and their changes during 4 years. METHODS AND RESULTS: One hundred and sixty-three metabolites were measured in serum samples with the AbsoluteIDQ kit p150 (Biocrates) following a targeted metabolomics approach, in a population-based cohort of 1030 individuals, aged 45 to 83 years at baseline. We evaluated associations between metabolite concentrations (28 acylcarnitines, 14 amino acids, 9 lysophosphocholines, 72 phosphocholines, 10 sphingomyelins and sum of hexoses) and 5 lifestyle risk factors (body mass index [BMI], alcohol consumption, smoking, diet, and exercise). Multilevel or simple linear regression modeling adjusted for relevant covariates was used for the evaluation of cross-sectional or longitudinal associations, respectively; multiple testing correction was based on false discovery rate. BMI, alcohol consumption, and smoking were associated with lipid metabolism (reduced lyso- and acyl-alkyl-phosphatidylcholines and increased diacylphosphatidylcholines concentrations). Smoking showed positive associations with acylcarnitines, and BMI correlated inversely with nonessential amino acids. Fewer metabolites showed relative changes that were associated with baseline risk factors: increases in 5 different acyl-alkyl phosphatidylcholines were associated with lower alcohol consumption and BMI and with a healthier diet. Increased levels of tyrosine were associated with BMI. Sex-specific effects of smoking and BMI were found specifically related to acylcarnitine metabolism: in women higher BMI and in men more pack-years were associated with increases in acylcarnitines. CONCLUSIONS: This study showed sex-specific effects of lifestyle risks factors on human metabolism and highlighted their long-term metabolic consequences.

Metabolomics enables precision medicine: "A White Paper, Community Perspective".

INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. OBJECTIVES OF WHITE PAPER­EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

The Pharmacogenetic Footprint of ACE Inhibition: A Population-Based Metabolomics Study.

Angiotensin-I-converting enzyme (ACE) inhibitors are an important class of antihypertensives whose action on the human organism is still not fully understood. Although it is known that ACE especially cleaves COOH-terminal dipeptides from active polypeptides, the whole range of substrates and products is still unknown. When analyzing the action of ACE inhibitors, effects of genetic variation on metabolism need to be considered since genetic variance in the ACE gene locus was found to be associated with ACE-concentration in blood as well as with changes in the metabolic profiles of a general population. To investigate the interactions between genetic variance at the ACE-locus and the influence of ACE-therapy on the metabolic status we analyzed 517 metabolites in 1,361 participants from the KORA F4 study. We replicated our results in 1,964 individuals from TwinsUK. We observed differences in the concentration of five dipeptides and three ratios of di- and oligopeptides between ACE inhibitor users and non-users that were genotype dependent. Such changes in the concentration affected major homozygotes, and to a lesser extent heterozygotes, while minor homozygotes showed no or only small changes in the metabolite status. Two of these resulting dipeptides, namely aspartylphenylalanine and phenylalanylserine, showed significant associations with blood pressure which qualifies them-and perhaps also the other dipeptides-as readouts of ACE-activity. Since so far ACE activity measurement is substrate specific due to the usage of only one oligopeptide, taking several dipeptides as potential products of ACE into account may provide a broader picture of the ACE activity.

A Metabolome-Wide Association Study of Kidney Function and Disease in the General Population.

Small molecules are extensively metabolized and cleared by the kidney. Changes in serum metabolite concentrations may result from impaired kidney function and can be used to estimate filtration (e.g., the established marker creatinine) or may precede and potentially contribute to CKD development. Here, we applied a nontargeted metabolomics approach using gas and liquid chromatography coupled to mass spectrometry to quantify 493 small molecules in human serum. The associations of these molecules with GFR estimated on the basis of creatinine (eGFRcr) and cystatin C levels were assessed in ≤1735 participants in the KORA F4 study, followed by replication in 1164 individuals in the TwinsUK registry. After correction for multiple testing, 54 replicated metabolites significantly associated with eGFRcr, and six of these showed pairwise correlation (r≥0.50) with established kidney function measures: C-mannosyltryptophan, pseudouridine, N-acetylalanine, erythronate, myo-inositol, and N-acetylcarnosine. Higher C-mannosyltryptophan, pseudouridine, and O-sulfo-L-tyrosine concentrations associated with incident CKD (eGFRcr <60 ml/min per 1.73 m(2)) in the KORA F4 study. In contrast with serum creatinine, C-mannosyltryptophan and pseudouridine concentrations showed little dependence on sex. Furthermore, correlation with measured GFR in 200 participants in the AASK study was 0.78 for both C-mannosyltryptophan and pseudouridine concentration, and highly significant associations of both metabolites with incident ESRD disappeared upon adjustment for measured GFR. Thus, these molecules may be alternative or complementary markers of kidney function. In conclusion, our study provides a comprehensive list of kidney function-associated metabolites and highlights potential novel filtration markers that may help to improve the estimation of GFR.

Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse.

Estrogens are suggested to lower the risk of developing metabolic syndrome in both sexes. In this study, we investigated how the increased circulating estrogen-to-androgen ratio (E/A) alters liver lipid metabolism in males. The cytochrome P450 aromatase (P450arom) is an enzyme converting androgens to estrogens. Male mice overexpressing human aromatase enzyme (AROM+ mice), and thus have high circulating E/A, were used as a model in this study. Proteomics and gene expression analyses indicated an increase in the peroxisomal ß-oxidation in the liver of AROM+ mice as compared with their wild type littermates. Correspondingly, metabolomic analysis revealed a decrease in the amount of phosphatidylcholines with long-chain fatty acids in the plasma. With interest we noted that the expression of Cyp4a12a enzyme, which specifically metabolizes arachidonic acid (AA) to 20-hydroxy AA, was dramatically decreased in the AROM+ liver. As a consequence, increased amounts of phospholipids having AA as a fatty acid tail were detected in the plasma of the AROM+ mice. Overall, these observations demonstrate that high circulating E/A in males is linked to indicators of higher peroxisomal ß-oxidation and lower AA metabolism in the liver. Furthermore, the plasma phospholipid profile reflects the changes in the liver lipid metabolism.

Urine Metabolite Profiles Predictive of Human Kidney Allograft Status.

Noninvasive diagnosis and prognostication of acute cellular rejection in the kidney allograft may help realize the full benefits of kidney transplantation. To investigate whether urine metabolites predict kidney allograft status, we determined levels of 749 metabolites in 1516 urine samples from 241 kidney graft recipients enrolled in the prospective multicenter Clinical Trials in Organ Transplantation-04 study. A metabolite signature of the ratio of 3-sialyllactose to xanthosine in biopsy specimen-matched urine supernatants best discriminated acute cellular rejection biopsy specimens from specimens without rejection. For clinical application, we developed a high-throughput mass spectrometry-based assay that enabled absolute and rapid quantification of the 3-sialyllactose-to-xanthosine ratio in urine samples. A composite signature of ratios of 3-sialyllactose to xanthosine and quinolinate to X-16397 and our previously reported urinary cell mRNA signature of 18S ribosomal RNA, CD3ε mRNA, and interferon-inducible protein-10 mRNA outperformed the metabolite signatures and the mRNA signature. The area under the receiver operating characteristics curve for the composite metabolite-mRNA signature was 0.93, and the signature was diagnostic of acute cellular rejection with a specificity of 84% and a sensitivity of 90%. The composite signature, developed using solely biopsy specimen-matched urine samples, predicted future acute cellular rejection when applied to pristine samples taken days to weeks before biopsy. We conclude that metabolite profiling of urine offers a noninvasive means of diagnosing and prognosticating acute cellular rejection in the human kidney allograft, and that the combined metabolite and mRNA signature is diagnostic and prognostic of acute cellular rejection with very high accuracy.

Integration of 'omics' data in aging research: from biomarkers to systems biology.

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

Vitamin E supplementation is associated with lower levels of C-reactive protein only in higher dosages and combined with other antioxidants: The Cooperative Health Research in the Region of Augsburg (KORA) F4 study.

The aim of the present study was to examine the association between intake of five common antioxidative nutrients from supplements and medications (vitamin E, vitamin C, carotenoids, Se, and Zn) and levels of high-sensitivity C-reactive protein (hs-CRP) in the general population. For this purpose, a total of 2924 participants of the population-based Cooperative Health Research in the Region of Augsburg (KORA) F4 study (2006-8) were investigated cross-sectionally. Intake of dietary supplements and medication during the last 7 d was recorded in a personal interview, when participants were asked to show product packages of ingested preparations. Linear regression models were calculated; first, the exposure to regular nutrient intake was treated with a binary response (yes/no); then regularly ingested amounts were divided into quartiles to examine dose-response relationships. Effect of single v. combined supplementation of antioxidants was assessed through the inclusion of interaction terms into the models. Regular intake of any of the five investigated antioxidants per se was not associated with hs-CRP levels. However, dose-response analyses revealed that participants who regularly ingested more than 78 mg vitamin E/d, which corresponds to the upper quartile, had 22% lower hs-CRP levels (95% CI 0·63, 0·97) compared to those of persons who were not exposed to any vitamin E supplementation. Stratified analyses showed that this association was found only in persons who took vitamin E in combination with other antioxidants. The combined supplementation of vitamin E with other antioxidants could thus be a promising strategy for the prevention of inflammation-related diseases in the general population, if further studies could confirm that the proposed association is causal.

Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits.

Previously, we reported strong influences of genetic variants on metabolic phenotypes, some of them with clinical relevance. Here, we hypothesize that DNA methylation may have an important and potentially independent effect on human metabolism. To test this hypothesis, we conducted what is to the best of our knowledge the first epigenome-wide association study (EWAS) between DNA methylation and metabolic traits (metabotypes) in human blood. We assess 649 blood metabolic traits from 1814 participants of the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) population study for association with methylation of 457 004 CpG sites, determined on the Infinium HumanMethylation450 BeadChip platform. Using the EWAS approach, we identified two types of methylome-metabotype associations. One type is driven by an underlying genetic effect; the other type is independent of genetic variation and potentially driven by common environmental and life-style-dependent factors. We report eight CpG loci at genome-wide significance that have a genetic variant as confounder (P = 3.9 × 10(-20) to 2.0 × 10(-108), r(2) = 0.036 to 0.221). Seven loci display CpG site-specific associations to metabotypes, but do not exhibit any underlying genetic signals (P = 9.2 × 10(-14) to 2.7 × 10(-27), r(2) = 0.008 to 0.107). We further identify several groups of CpG loci that associate with a same metabotype, such as 4-vinylphenol sulfate and 4-androsten-3-beta,17-beta-diol disulfate. In these cases, the association between CpG-methylation and metabotype is likely the result of a common external environmental factor, including smoking. Our study shows that analysis of EWAS with large numbers of metabolic traits in large population cohorts are, in principle, feasible. Taken together, our data suggest that DNA methylation plays an important role in regulating human metabolism.