Bile acid profiles and mRNA abundance of bile acid-related genes in adipose tissue of dairy cows with high versus normal body condition

Besides their lipid-digestive role, bile acids (BA) influence overall energy homeostasis, such as glucose and lipid metabolism. We hypothesized that BA along with their receptors, regulatory enzymes, and transporters are present in subcutaneous adipose tissue (scAT). In addition, we hypothesized that their mRNA abundance varies with the body condition of dairy cows around calving. Therefore, we analyzed BA in serum and scAT as well as the mRNA abundance of BA -related enzymes, transporters, and receptors in scAT during the transition period in cows with different body conditions around calving. In a previously established animal model, 38 German Holstein cows were divided into either a high (HBCS; n = 19) or normal BCS (NBCS; n = 19) group based on their body condition score (BCS) and back fat thickness (BFT). Cows were fed different diets to achieve the targeted differences in BCS and BFT (NBCS: BCS <3.5, BFT <1.2 cm; HBCS: BCS >3.75, BFT >1.4 cm) until dry-off at 7 wk ante partum. During the dry period and subsequent lactation, both groups were fed the same diets regarding their demands. Using a targeted metabolomics approach via LC-ESI-MS /MS, BA were analyzed in serum and scAT at wk -7, 1, 3, and 12 relative to parturition. In serum, 15 BA (cholic acid (CA), chenodeoxycholic acid (CDCA), glycocholic acid (GCA), taurocholic acid (TCA), glycochenodeoxycholic acid (GCDCA), taurochenodeoxycholic acid (TCDCA), deoxycholic acid (DCA), lithocholic acid (LCA), glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), taurodeoxycholic acid (TDCA), taurolithocholic acid (TLCA), β-muricholic acid (MCA(b)), tauromuricholic acid (sum of α and β) (TMCA (a+b)), glycoursodeoxycholic acid (GUDCA)) were observed, whereas in scAT 7 BA (CA, GCA, TCA, GCDCA, TCDCA, GDCA, TDCA) were detected. In serum and scAT samples, the primary BA CA and its conjugate GCA were predominantly detected. Increasing serum concentrations of CA, CDCA, TCA, GCA, GCDCA, DCA, and MCA(b) with the onset of lactation might be related to the increasing DMI after parturition. Furthermore, serum concentrations of CA, CDCA, GCA, DCA, GCDCA, TCA, LCA, and GDCA were lower in HBCS cows compared with NBCS cows, concomitant with increased lipolysis in HBCS cows. The correlation between CA in serum and scAT may point to the transport of CA across cell membranes. Overall, the findings of the present study suggest a potential role of BA in lipid metabolism depending on the body condition of periparturient dairy cows.

Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study

INTRODUCTION

Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites.

RESEARCH DESIGN AND METHODS

This study is based on 1124 participants aged 61-82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin.

RESULTS

Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1-4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models.

CONCLUSIONS

Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.

Association between pre-diagnostic circulating lipid metabolites and colorectal cancer risk: a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC)

BACKGROUND

Altered lipid metabolism is a hallmark of cancer development. However, the role of specific lipid metabolites in colorectal cancer development is uncertain.

METHODS

In a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined associations between pre-diagnostic circulating concentrations of 97 lipid metabolites (acylcarnitines, glycerophospholipids and sphingolipids) and colorectal cancer risk. Circulating lipids were measured using targeted mass spectrometry in 1591 incident colorectal cancer cases (55% women) and 1591 matched controls. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between concentrations of individual lipid metabolites and metabolite patterns with colorectal cancer risk.

FINDINGS

Of the 97 assayed lipids, 24 were inversely associated (nominally p < 0.05) with colorectal cancer risk. Hydroxysphingomyelin (SM (OH)) C22:2 (ORper doubling 0.60, 95% CI 0.47-0.77) and acylakyl-phosphatidylcholine (PC ae) C34:3 (ORper doubling 0.71, 95% CI 0.59-0.87) remained associated after multiple comparisons correction. These associations were unaltered after excluding the first 5 years of follow-up after blood collection and were consistent according to sex, age at diagnosis, BMI, and colorectal subsite. Two lipid patterns, one including 26 phosphatidylcholines and all sphingolipids, and another 30 phosphatidylcholines, were weakly inversely associated with colorectal cancer.

INTERPRETATION

Elevated pre-diagnostic circulating levels of SM (OH) C22:2 and PC ae C34:3 and lipid patterns including phosphatidylcholines and sphingolipids were associated with lower colorectal cancer risk. This study may provide insight into potential links between specific lipids and colorectal cancer development. Additional prospective studies are needed to validate the observed associations.

FUNDING

World Cancer Research Fund (reference: 2013/1002); European Commission (FP7: BBMRI-LPC; reference: 313010).

Metabolomic associations of impaired awareness of hypoglycaemia in type 1 diabetes

This study investigates impaired awareness of hypoglycaemia (IAH), a complication of insulin therapy affecting 20-40% of individuals with type 1 diabetes. The exact pathophysiology is unclear, therefore we sought to identify metabolic signatures in IAH to elucidate potential pathophysiological pathways. Plasma samples from 578 individuals of the Dutch type 1 diabetes biomarker cohort, 67 with IAH and 108 without IAH (NAH) were analysed using the targeted metabolomics Biocrates AbsoluteIDQ p180 assay. Eleven metabolites were significantly associated with IAH. Genome-wide association studies of these 11 metabolites identified significant single nucleotide polymorphisms (SNPs) in C22:1-OH and phosphatidylcholine diacyl C36:6. After adjusting for the SNPs, 11 sphingomyelins and phosphatidylcholines were significantly higher in the IAH group in comparison to NAH. These metabolites are important components of the cell membrane and have been implicated to play a role in cell signalling in diabetes. These findings demonstrate the potential role of phosphatidylcholine and sphingomyelins in IAH.

Association of sex hormones and sex hormone-binding globulin with liver fat in men and women: an observational and Mendelian randomization study

Background

Sex hormones and sex hormone-binding globulin (SHBG) may play a role in fatty liver development. We sought to examine the association of various endogenous sex hormones, including testosterone (T), and SHBG with liver fat using complementary observational and Mendelian randomization (MR) analyses.

Methods

The observational analysis included a total of 2,239 participants (mean age 60 years; 35% postmenopausal women) from the population-based KORA study (average follow-up time: 6.5 years). We conducted linear regression analysis to investigate the sex-specific associations of sex hormones and SHBG with liver fat, estimated by fatty liver index (FLI). For MR analyses, we selected genetic variants associated with sex hormones and SHBG and extracted their associations with magnetic resonance imaging measured liver fat from the largest up to date European genome-wide associations studies.

Results

In the observational analysis, T, dihydrotestosterone (DHT), progesterone and 17α-hydroxyprogesterone (17-OHP) were inversely associated with FLI in men, with beta estimates ranging from -4.23 to -2.30 [p-value <0.001 to 0.003]. Whereas in women, a positive association of free T with FLI (β = 4.17, 95%CI: 1.35, 6.98) was observed. SHBG was inversely associated with FLI across sexes [men: -3.45 (-5.13, -1.78); women: -9.23 (-12.19, -6.28)]. No causal association was found between genetically determined sex hormones and liver fat, but higher genetically determined SHBG was associated with lower liver fat in women (β = -0.36, 95% CI: -0.61, -0.12).

Conclusion

Our results provide suggestive evidence for a causal association between SHBG and liver fat in women, implicating the protective role of SHBG against liver fat accumulation.

Circulating metabolites improve the prediction of renal impairment in patients with type 2 diabetes

INTRODUCTION

Low glomerular filtration rate (GFR) is a leading cause of reduced lifespan in type 2 diabetes. Unravelling biomarkers capable to identify high-risk patients can help tackle this burden. We investigated the association between 188 serum metabolites and kidney function in type 2 diabetes and then whether the associated metabolites improve two established clinical models for predicting GFR decline in these patients.

RESEARCH DESIGN AND METHODS

Two cohorts comprising 849 individuals with type 2 diabetes (discovery and validation samples) and a follow-up study of 575 patients with estimated GFR (eGFR) decline were analyzed.

RESULTS

Ten metabolites were independently associated with low eGFR in the discovery sample, with nine of them being confirmed also in the validation sample (ORs range 1.3-2.4 per 1SD, p values range 1.9×10-2-2.5×10-9). Of these, five metabolites were also associated with eGFR decline (ie, tiglylcarnitine, decadienylcarnitine, total dimethylarginine, decenoylcarnitine and kynurenine) (β range -0.11 to -0.19, p values range 4.8×10-2 to 3.0×10-3). Indeed, tiglylcarnitine and kynurenine, which captured all the information of the other three markers, improved discrimination and reclassification (all p<0.01) of two clinical prediction models of GFR decline in people with diabetes.

CONCLUSIONS

Further studies are needed to validate our findings in larger cohorts of different clinical, environmental and genetic background.

The HuMet Repository: Watching human metabolism at work

The human metabolism constantly responds to stimuli such as food intake, fasting, exercise, and stress, triggering adaptive biochemical processes across multiple metabolic pathways. To understand the role of these processes and disruptions thereof in health and disease, detailed documentation of healthy metabolic responses is needed but still scarce on a time-resolved metabolome-wide level. Here, we present the HuMet Repository, a web-based resource for exploring dynamic metabolic responses to six physiological challenges (exercise, 36 h fasting, oral glucose and lipid loads, mixed meal, cold stress) in healthy subjects. For building this resource, we integrated existing and newly derived metabolomics data measured in blood, urine, and breath samples of 15 young healthy men at up to 56 time points during the six highly standardized challenge tests conducted over four days. The data comprise 1.1 million data points acquired on multiple platforms with temporal profiles of 2,656 metabolites from a broad range of biochemical pathways. By embedding the dataset into an interactive web application, we enable users to easily access, search, filter, analyze, and visualize the time-resolved metabolomic readouts and derived results. Users can put metabolites into their larger context by identifying metabolites with similar trajectories or by visualizing metabolites within holistic metabolic networks to pinpoint pathways of interest. In three showcases, we outline the value of the repository for gaining biological insights and generating hypotheses by analyzing the wash-out of dietary markers, the complementarity of metabolomics platforms in dynamic versus cross-sectional data, and similarities and differences in systemic metabolic responses across challenges. With its comprehensive collection of time-resolved metabolomics data, the HuMet Repository, freely accessible at https://humet.org/, is a reference for normal, healthy responses to metabolic challenges in young males. It will enable researchers with and without computational expertise, to flexibly query the data for their own research into the dynamics of human metabolism.

Maternal hyperglycemia induces alterations in hepatic amino acid, glucose and lipid metabolism of neonatal offspring: Multi-omics insights from a diabetic pig model

OBJECTIVE

To gain mechanistic insights into adverse effects of maternal hyperglycemia on the liver of neonates, we performed a multi-omics analysis of liver tissue from piglets developed in genetically diabetic (mutant INS gene induced diabetes of youth; MIDY) or wild-type (WT) pigs.

METHODS

Proteome, metabolome and lipidome profiles of liver and clinical parameters of serum samples from 3-day-old WT piglets (n=9) born to MIDY mothers (PHG) were compared with those of WT piglets (n=10) born to normoglycemic mothers (PNG). Furthermore, protein-protein interaction network analysis was used to reveal highly interacting proteins that participate in the same molecular mechanisms and to relate these mechanisms with human pathology.

RESULTS

Hepatocytes of PHG displayed pronounced lipid droplet accumulation, although the abundances of central lipogenic enzymes such as fatty acid-synthase (FASN) were decreased. Additionally, circulating triglyceride (TG) levels were reduced as a trend. Serum levels of non-esterified free fatty acids (NEFA) were elevated in PHG, potentially stimulating hepatic gluconeogenesis. This is supported by elevated hepatic phosphoenolpyruvate carboxykinase (PCK1) and circulating alanine transaminase (ALT) levels. Even though targeted metabolomics showed strongly elevated phosphatidylcholine (PC) levels, the abundances of multiple key enzymes involved in major PC synthesis pathways - most prominently those from the Kennedy pathway - were paradoxically reduced in PHG liver. Conversely, enzymes involved in PC excretion and breakdown such as PC-specific translocase ATP-binding cassette 4 (ABCB4) and phospholipase A2 were increased in abundance.

CONCLUSIONS

Our study indicates that maternal hyperglycemia without confounding obesity induces profound molecular changes in the liver of neonatal offspring. In particular, we found evidence for stimulated gluconeogenesis and hepatic lipid accumulation independent of de novo lipogenesis. Reduced levels of PC biosynthesis enzymes and increased levels of proteins involved in PC translocation or breakdown may represent counter-regulatory mechanisms to maternally elevated PC levels. Our comprehensive multi-omics dataset provides a valuable resource for future meta-analysis studies focusing on liver metabolism in newborns from diabetic mothers.

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts

BACKGROUND

Amino acid metabolism is dysregulated in colorectal cancer patients; however, it is not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk of colorectal cancer. We investigated circulating levels of amino acids in relation to colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts.

METHODS

Concentrations of 13-21 amino acids were determined in baseline fasting plasma or serum samples in 654 incident colorectal cancer cases and 654 matched controls in EPIC. Amino acids associated with colorectal cancer risk following adjustment for the false discovery rate (FDR) were then tested for associations in the UK Biobank, for which measurements of 9 amino acids were available in 111,323 participants, of which 1221 were incident colorectal cancer cases.

RESULTS

Histidine levels were inversely associated with colorectal cancer risk in EPIC (odds ratio [OR] 0.80 per standard deviation [SD], 95% confidence interval [CI] 0.69-0.92, FDR P-value=0.03) and in UK Biobank (HR 0.93 per SD, 95% CI 0.87-0.99, P-value=0.03). Glutamine levels were borderline inversely associated with colorectal cancer risk in EPIC (OR 0.85 per SD, 95% CI 0.75-0.97, FDR P-value=0.08) and similarly in UK Biobank (HR 0.95, 95% CI 0.89-1.01, P=0.09) In both cohorts, associations changed only minimally when cases diagnosed within 2 or 5 years of follow-up were excluded.

CONCLUSIONS

Higher circulating levels of histidine were associated with a lower risk of colorectal cancer in two large prospective cohorts. Further research to ascertain the role of histidine metabolism and potentially that of glutamine in colorectal cancer development is warranted.

Metabolic Signatures Elucidate the Effect of Body Mass Index on Type 2 Diabetes

Obesity plays an important role in the development of insulin resistance and diabetes, but the molecular mechanism that links obesity and diabetes is still not completely understood. Here, we used 146 targeted metabolomic profiles from the German KORA FF4 cohort consisting of 1715 participants and associated them with obesity and type 2 diabetes. In the basic model, 83 and 51 metabolites were significantly associated with body mass index (BMI) and T2D, respectively. Those metabolites are branched-chain amino acids, acylcarnitines, lysophospholipids, or phosphatidylcholines. In the full model, 42 and 3 metabolites were significantly associated with BMI and T2D, respectively, and replicate findings in the previous studies. Sobel mediation testing suggests that the effect of BMI on T2D might be mediated via lipids such as sphingomyelin (SM) C16:1, SM C18:1 and diacylphosphatidylcholine (PC aa) C38:3. Moreover, mendelian randomization suggests a causal relationship that BMI causes the change of SM C16:1 and PC aa C38:3, and the change of SM C16:1, SM C18:1, and PC aa C38:3 contribute to T2D incident. Biological pathway analysis in combination with genetics and mice experiments indicate that downregulation of sphingolipid or upregulation of phosphatidylcholine metabolism is a causal factor in early-stage T2D pathophysiology. Our findings indicate that metabolites like SM C16:1, SM C18:1, and PC aa C38:3 mediate the effect of BMI on T2D and elucidate their role in obesity related T2D pathologies.

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study

Background

Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter.

Methods

This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score.

Findings

Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers.

Interpretation

We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment.

Funding

European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).

Dynamic patterns of postprandial metabolic responses to three dietary challenges

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.

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.

Metabolic Signatures of Healthy Lifestyle Patterns and Colorectal Cancer Risk in a European Cohort

BACKGROUND & AIMS

Colorectal cancer risk can be lowered by adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines. We derived metabolic signatures of adherence to these guidelines and tested their associations with colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort.

METHODS

Scores reflecting adherence to the WCRF/AICR recommendations (scale, 1-5) were calculated from participant data on weight maintenance, physical activity, diet, and alcohol among a discovery set of 5738 cancer-free European Prospective Investigation into Cancer and Nutrition participants with metabolomics data. Partial least-squares regression was used to derive fatty acid and endogenous metabolite signatures of the WCRF/AICR score in this group. In an independent set of 1608 colorectal cancer cases and matched controls, odds ratios (ORs) and 95% CIs were calculated for colorectal cancer risk per unit increase in WCRF/AICR score and per the corresponding change in metabolic signatures using multivariable conditional logistic regression.

RESULTS

Higher WCRF/AICR scores were characterized by metabolic signatures of increased odd-chain fatty acids, serine, glycine, and specific phosphatidylcholines. Signatures were inversely associated more strongly with colorectal cancer risk (fatty acids: OR, 0.51 per unit increase; 95% CI, 0.29-0.90; endogenous metabolites: OR, 0.62 per unit change; 95% CI, 0.50-0.78) than the WCRF/AICR score (OR, 0.93 per unit change; 95% CI, 0.86-1.00) overall. Signature associations were stronger in male compared with female participants.

CONCLUSIONS

Metabolite profiles reflecting adherence to WCRF/AICR guidelines and additional lifestyle or biological risk factors were associated with colorectal cancer. Measuring a specific panel of metabolites representative of a healthy or unhealthy lifestyle may identify strata of the population at higher risk of colorectal cancer.

Bezafibrate Reduces Elevated Hepatic Fumarate in Insulin-Deficient Mice

Glucotoxic metabolites and pathways play a crucial role in diabetic complications, and new treatment options which improve glucotoxicity are highly warranted. In this study, we analyzed bezafibrate (BEZ) treated, streptozotocin (STZ) injected mice, which showed an improved glucose metabolism compared to untreated STZ animals. In order to identify key molecules and pathways which participate in the beneficial effects of BEZ, we studied plasma, skeletal muscle, white adipose tissue (WAT) and liver samples using non-targeted metabolomics (NMR spectroscopy), targeted metabolomics (mass spectrometry), microarrays and mitochondrial enzyme activity measurements, with a particular focus on the liver. The analysis of muscle and WAT demonstrated that STZ treatment elevated inflammatory pathways and reduced insulin signaling and lipid pathways, whereas BEZ decreased inflammatory pathways and increased insulin signaling and lipid pathways, which can partly explain the beneficial effects of BEZ on glucose metabolism. Furthermore, lysophosphatidylcholine levels were lower in the liver and skeletal muscle of STZ mice, which were reverted in BEZ-treated animals. BEZ also improved circulating and hepatic glucose levels as well as lipid profiles. In the liver, BEZ treatment reduced elevated fumarate levels in STZ mice, which was probably due to a decreased expression of urea cycle genes. Since fumarate has been shown to participate in glucotoxic pathways, our data suggests that BEZ treatment attenuates the urea cycle in the liver, decreases fumarate levels and, in turn, ameliorates glucotoxicity and reduces insulin resistance in STZ mice.

Blood and adipose tissue steroid metabolomics and mRNA expression of steroidogenic enzymes in periparturient dairy cows differing in body condition

In high-yielding dairy cows, the rapidly increasing milk production after parturition can result in a negative nutrient balance, since feed intake is insufficient to cover the needs for lactation. Mobilizing body reserves, mainly adipose tissue (AT), might affect steroid metabolism. We hypothesized, that cows differing in the extent of periparturient lipomobilization, will have divergent steroid profiles measured in serum and subcutaneous (sc)AT by a targeted metabolomics approach and steroidogenic enzyme profiles in scAT and liver. Fifteen weeks antepartum, 38 multiparous Holstein cows were allocated to a high (HBCS) or normal body condition (NBCS) group fed differently until week 7 antepartum to either increase (HBCS BCS: 3.8 ± 0.1 and BFT: 2.0 ± 0.1 cm; mean ± SEM) or maintain BCS (NBCS BCS: 3.0 ± 0.1 and BFT: 0.9 ± 0.1 cm). Blood samples, liver, and scAT biopsies were collected at week -7, 1, 3, and 12 relative to parturition. Greater serum concentrations of progesterone, androsterone, and aldosterone in HBCS compared to NBCS cows after parturition, might be attributed to the increased mobilization of AT. Greater glucocorticoid concentrations in scAT after parturition in NBCS cows might either influence local lipogenesis by differentiation of preadipocytes into mature adipocytes and/or inflammatory response.

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments.

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture

Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.

Untargeted and Targeted Circadian Metabolomics Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and Flow Injection-Electrospray Ionization-Tandem Mass Spectrometry (FIA-ESI-MS/MS)

A diverse array of 24-h oscillating hormones and metabolites direct and reflect circadian clock function. Circadian metabolomics uses advanced high-throughput analytical chemistry techniques to comprehensively profile these small molecules (<1.5 kDa) across 24 h in cells, media, body fluids, breath, tissues, and subcellular compartments. The goals of circadian metabolomics experiments are often multifaceted. These include identifying and tracking rhythmic metabolic inputs and outputs of central and peripheral circadian clocks, quantifying endogenous free-running period, monitoring relative phase alignment between clocks, and mapping pathophysiological consequences of clock disruption or misalignment. Depending on the particular experimental question, samples are collected under free-running or entrained conditions. Here we describe both untargeted and targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) and flow injection-electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) based assays we have used for circadian metabolomics studies. We discuss tissue homogenization, chemical derivatization, measurement, and tips for data processing, normalization, scaling, how to handle outliers, and imputation of missing values.

Pre- versus post-operative untargeted plasma nuclear magnetic resonance spectroscopy metabolomics of pheochromocytoma and paraganglioma

PURPOSE

Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors.

DESIGN

A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics.

METHODS

Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods.

RESULTS

Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected.

CONCLUSIONS

Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.

Associations of endogenous androgens and sex hormone-binding globulin with kidney function and chronic kidney disease

INTRODUCTION

The role of endogenous androgens in kidney function and disease has not been extensively explored in men and women.

RESEARCH DESIGN AND METHODS

We analyzed data from the observational KORA F4 study and its follow-up examination KORA FF4 (median follow-up time 6.5 years) including 1293 men and 650 peri- and postmenopausal women, not using exogenous sex hormones. We examined the associations between endogenous androgens (testosterone [T], dihydrotestosterone [DHT], free T [fT], free DHT [fDHT], and T/DHT), with estimated glomerular filtration rate (eGFR) at baseline and follow-up, prevalent, and incident chronic kidney disease (CKD) adjusting for common CKD risk factors.

RESULTS

At baseline, 73 men (5.7%) and 54 women (8.4%) had prevalent CKD. Cross-sectionally, no significant associations between androgens and kidney function were observed among men. In women, elevated T (β=-1.305, [95% CI -2.290; -0.320]) and fT (β=-1.423, [95% CI -2.449; -0.397]) were associated with lower eGFR. Prospectively, 81 men (8.8%) and 60 women (15.2%) developed incident CKD. In women, a reverse J-shaped associations was observed between DHT and incident CKD (P_non-linear=0.029), while higher fDHT was associated with lower incident CKD risk (odds ratio per 1 standard deviation=0.613, [95% CI 0.369; 0.971]. Among men, T/DHT (β=-0.819, [95% CI -1.413; -0.226]) and SHBG (P_non-linear=0.011) were associated with eGFR at follow-up but not with incident CKD. Some associations appeared to be modified by type 2 diabetes (T2D).

CONCLUSION

Suggestive associations are observed of androgens and SHBG with kidney impairment among men and women. However, larger well-phenotyped prospective studies are required to further elucidate the potential of androgens, SHBG, and T2D as modifiable risk factors for kidney function and CKD.

The Effect of Dietary Protein Imbalance during Pregnancy on the Growth, Metabolism and Circulatory Metabolome of Neonatal and Weaned Juvenile Porcine Offspring

Protein imbalance during pregnancy affects women in underdeveloped and developing countries and is associated with compromised offspring growth and an increased risk of metabolic diseases in later life. We studied in a porcine model the glucose and urea metabolism, and circulatory hormone and metabolite profile of offspring exposed during gestation, to maternal isoenergetic low-high (LP-HC), high-low (HP-LC) or adequate (AP) protein-carbohydrate ratio diets. At birth, LP-HC were lighter and the plasma acetylcarnitine to free carnitine ratios at 1 day of life was lower compared to AP offspring. Plasma urea concentrations were lower in 1 day old LP-HC offspring than HP-LC. In the juvenile period, increased insulin concentrations were observed in LP-HC and HP-LC offspring compared to AP, as was body weight from HP-LC compared to LP-HC. Plasma triglyceride concentrations were lower in 80 than 1 day old HP-LC offspring, and glucagon concentrations lower in 80 than 1 day old AP and HP-LC offspring. Plasma urea and the ratio of glucagon to insulin were lower in all 80 than 1 day old offspring. Aminoacyl-tRNA, arginine and phenylalanine, tyrosine and tryptophan metabolism, histidine and beta-alanine metabolism differed between 1 and 80 day old AP and HP-LC offspring. Maternal protein imbalance throughout pregnancy did not result in significant consequences in offspring metabolism compared to AP, indicating enormous plasticity by the placenta and developing offspring.

The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium

BACKGROUND

Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI).

METHODS AND FINDINGS

We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds.

CONCLUSIONS

This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism

Objective

Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce.

Methods

We present here a fully unsupervised and versatile correlation-based method – termed Correlation guided Network Integration (CoNI) – to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways.

Results

By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR).

Conclusion

Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.

Physiological extremes of the human blood metabolome: A metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes

Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.

Targeted Metabolomics as a Tool in Discriminating Endocrine From Primary Hypertension

CONTEXT

Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension.

OBJECTIVE

Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability.

METHODS

Retrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a "classical approach" (CA) (performing a series of univariate and multivariate analyses) and a "machine learning approach" (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively.

RESULTS

From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83).

CONCLUSION

TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

Targeted assessment of the metabolome in skeletal muscle and in serum of dairy cows supplemented with conjugated linoleic acid during early lactation

In the dairy cow, late gestation and early lactation are characterized by a complexity of metabolic processes required for the homeorhetic adaptation to the needs of fetal growth and milk production. Skeletal muscle plays an important role in this adaptation. The objective of this study was to characterize the metabolome in skeletal muscle (semitendinosus muscle) and in serum of dairy cows in the context of the physiological changes occurring in early lactation and to test the effects of dietary supplementation (from d 1 in milk onwards) with conjugated linoleic acids (sCLA; 100 g/d; supplying 7.6 g of cis-9,trans-11 CLA and 7.6 g of trans-10,cis-12 CLA per cow/d; n = 11) compared with control fat-supplemented cows (CTR; n = 10). The metabolome was characterized in skeletal muscle samples collected on d 21 and 70 after calving in conjunction with their serum counterpart using a targeted metabolomics approach (AbsoluteIDQ p180 kit; Biocrates Life Sciences AG, Innsbruck, Austria). Thereby 188 metabolites from 6 different compound classes (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, and hexoses) were quantified in both sample types. In both groups, dry matter intake increased after calving. It was lower in sCLA than in CTR on d 21, which resulted in reduced calculated net energy and metabolizable protein balances. On d 21, the concentrations of dopamine, Ala, and hexoses in the skeletal muscle were higher in sCLA than in CTR. On d 21, the changed metabolites in serum were mainly long-chain (>C24) diacyl phosphatidylcholine PC (PC-aa) and acyl-alkyl phosphatidylcholine (PC-ae), along with lysophosphatidylcholine acyl (lysoPC-a) C26:1 that were all lower in sCLA than in CTR. Supplementation with CLA affected the muscle concentrations of 22 metabolites on d 70 including 10 long-chain (>C22) sphingomyelin (SM), hydroxysphingomyelin [SM(OH)], PC-aa, and PC-ae along with 9 long-chain (>C16) lysoPC-a and 3 metabolites related to amino acids (spermine, citrulline, and Asp). On d 70, the concentrations of lysoPC-a C18:2 and C26:0 in serum were higher in the sCLA cows than in the CTR cows. Regardless of treatment, the concentrations of Ile, Leu, Phe, Lys, His, Met, Trp, and hydroxybutyrylcarnitine (C4-OH) decreased, whereas those of ornithine, Gln, and trans-4-hydroxyproline (t4-OH-Pro) increased from d 21 to 70 in muscle. The significantly changed metabolites in serum with time of lactation were 28 long-chain (>C30) PC-ae and PC-aa, 7 long-chain (>C16) SM and SM(OH), along with lysoPC-a C20:3 that were all increased. In conclusion, in addition to other significantly changed metabolites, CLA supplementation mainly led to changes in muscle and serum concentrations of glycerophospholipids and sphingolipids that might reflect the phospholipid compositional changes in muscle. The metabolome changes observed in sCLA on d 21 seem to be, at least in part, due to the lower DMI in these cows. The changes in the muscle concentrations of AA from d 21 to 70, which coincided with the steady energy and MP balances, might reflect a shift of protein synthesis/degradation balance toward synthesis.

Posterior subcapsular cataracts are a late effect after acute exposure to 0.5 Gy ionizing radiation in mice

PURPOSE

The long-term effect of low and moderate doses of ionizing radiation on the lens is still a matter of debate and needs to be evaluated in more detail.

MATERIAL AND METHODS

We conducted a detailed histological analysis of eyes from B6C3F1 mice cohorts after acute gamma irradiation (⁶⁰Co source; 0.063 Gy/min) at young adult age of 10 weeks with doses of 0.063, 0.125, and 0.5 Gy. Sham irradiated (0 Gy) mice were used as controls. To test for genetic susceptibility heterozygous Ercc2 mutant mice were used and compared to wild-type mice of the same strain background. Mice of both sexes were included in all cohorts. Eyes were collected 4 h, 12, 18 and 24 months after irradiation. For a better understanding of the underlying mechanisms, metabolomics analyses were performed in lenses and plasma samples of the same mouse cohorts at 4 and 12 h as well as 12, 18 and 24 months after irradiation. For this purpose, a targeted analysis was chosen.

RESULTS

This analysis revealed histological changes particularly in the posterior part of the lens that rarely can be observed by using Scheimpflug imaging, as we reported previously. We detected a significant increase of posterior subcapsular cataracts (PSCs) 18 and 24 months after irradiation with 0.5 Gy (odds ratio 9.3; 95% confidence interval 2.1-41.3) independent of sex and genotype. Doses below 0.5 Gy (i.e. 0.063 and 0.125 Gy) did not significantly increase the frequency of PSCs at any time point. In lenses, we observed a clear effect of sex and aging but not of irradiation or genotype. While metabolomics analyses of plasma from the same mice showed only a sex effect.

CONCLUSIONS

This article demonstrates a significant radiation-induced increase in the incidence of PSCs, which could not be identified using Scheimpflug imaging as the only diagnostic tool.

The liver-alpha cell axis associates with liver fat and insulin resistance: a validation study in women with non-steatotic liver fat levels

AIMS/HYPOTHESIS

Many individuals who develop type 2 diabetes also display increased glucagon levels (hyperglucagonaemia), which we have previously found to be associated with the metabolic syndrome. The concept of a liver-alpha cell axis provides a possible link between hyperglucagonaemia and elevated liver fat content, a typical finding in the metabolic syndrome. However, this association has only been studied in individuals with non-alcoholic fatty liver disease. Hence, we searched for a link between the liver and the alpha cells in individuals with non-steatotic levels of liver fat content. We hypothesised that the glucagon-alanine index, an indicator of the functional integrity of the liver-alpha cell axis, would associate with liver fat and insulin resistance in our cohort of women with low levels of liver fat.

METHODS

We analysed data from 79 individuals participating in the Prediction, Prevention and Subclassification of Type 2 Diabetes (PPSDiab) study, a prospective observational study of young women at low to high risk for the development of type 2 diabetes. Liver fat content was determined by MRI. Insulin resistance was calculated as HOMA-IR. We conducted Spearman correlation analyses of liver fat content and HOMA-IR with the glucagon-alanine index (the product of fasting plasma levels of glucagon and alanine). The prediction of the glucagon-alanine index by liver fat or HOMA-IR was tested in multivariate linear regression analyses in the whole cohort as well as after stratification for liver fat content ≤0.5% (n = 39) or >0.5% (n = 40).

RESULTS

The glucagon-alanine index significantly correlated with liver fat and HOMA-IR in the entire cohort (ρ = 0.484, p < 0.001 and ρ = 0.417, p < 0.001, respectively). These associations resulted from significant correlations in participants with a liver fat content >0.5% (liver fat, ρ = 0.550, p < 0.001; HOMA-IR, ρ = 0.429, p = 0.006). In linear regression analyses, the association of the glucagon-alanine index with liver fat remained significant after adjustment for age and HOMA-IR in all participants and in those with liver fat >0.5% (β = 0.246, p = 0.0.23 and β = 0.430, p = 0.007, respectively) but not in participants with liver fat ≤0.5% (β = -0.184, p = 0.286).

CONCLUSIONS/INTERPRETATION

We reproduced the previously reported association of liver fat content and HOMA-IR with the glucagon-alanine index in an independent study cohort of young women with low to high risk for type 2 diabetes. Furthermore, our data indicates an insulin-resistance-independent association of liver fat content with the glucagon-alanine index. In summary, our study supports the concept that even lower levels of liver fat (from 0.5%) are connected to relative hyperglucagonaemia, reflecting an imminent impairment of the liver-alpha cell axis.

Validation of Candidate Phospholipid Biomarkers of Chronic Kidney Disease in Hyperglycemic Individuals and Their Organ-Specific Exploration in Leptin Receptor-Deficient db/db Mouse

Biological exploration of early biomarkers for chronic kidney disease (CKD) in (pre)diabetic individuals is crucial for personalized management of diabetes. Here, we evaluated two candidate biomarkers of incident CKD (sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0) concerning kidney function in hyperglycemic participants of the Cooperative Health Research in the Region of Augsburg (KORA) cohort, and in two biofluids and six organs of leptin receptor-deficient (db/db) mice and wild type controls. Higher serum concentrations of SM C18:1 and PC aa C38:0 in hyperglycemic individuals were found to be associated with lower estimated glomerular filtration rate (eGFR) and higher odds of CKD. In db/db mice, both metabolites had a significantly lower concentration in urine and adipose tissue, but higher in the lungs. Additionally, db/db mice had significantly higher SM C18:1 levels in plasma and liver, and PC aa C38:0 in adrenal glands. This cross-sectional human study confirms that SM C18:1 and PC aa C38:0 associate with kidney dysfunction in pre(diabetic) individuals, and the animal study suggests a potential implication of liver, lungs, adrenal glands, and visceral fat in their systemic regulation. Our results support further validation of the two phospholipids as early biomarkers of renal disease in patients with (pre)diabetes.

Cross-sectional and prospective relationships of endogenous progestogens and estrogens with glucose metabolism in men and women: a KORA F4/FF4 Study

INTRODUCTION

Relationships between endogenous female sex hormones and glycemic traits remain understudied, especially in men. We examined whether endogenous 17α-hydroxyprogesterone (17-OHP), progesterone, estradiol (E2), and free estradiol (fE2) were associated with glycemic traits and glycemic deterioration.

RESEARCH DESIGN AND METHODS

921 mainly middle-aged and elderly men and 390 perimenopausal/postmenopausal women from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) F4/FF4 cohort study were followed up for a median of 6.4 years. Sex hormones were measured at baseline using mass spectrometry. We calculated regression coefficients (β) and ORs with 95% CIs using multivariable-adjusted linear and logistic regression models for Z-standardized hormones and glycemic traits or glycemic deterioration (ie, worsening of categorized glucose tolerance status), respectively.

RESULTS

In the cross-sectional analysis (n=1222 men and n=594 women), in men, 17-OHP was inversely associated with 2h-glucose (2hG) (β=-0.067, 95% CI -0.120 to -0.013) and fasting insulin (β=-0.074, 95% CI -0.118 to -0.030), and positively associated with Quantitative Insulin Sensitivity Check Index (QUICKI) (β=0.061, 95% CI 0.018 to 0.105). Progesterone was inversely associated with fasting insulin (β=-0.047, 95% CI -0.088 to -0.006) and positively associated with QUICKI (β=0.041, 95% CI 0.001 to 0.082). E2 was inversely associated with fasting insulin (β=-0.068, 95% CI -0.116 to -0.020) and positively associated with QUICKI (β=0.059, 95% CI 0.012 to 0.107). fE2 was positively associated with glycated hemoglobin (HbA_1c) (β=0.079, 95% CI 0.027 to 0.132). In women, 17-OHP was positively associated with fasting glucose (FG) (β=0.068, 95% CI 0.014 to 0.123). fE2 was positively associated with FG (β=0.080, 95% CI 0.020 to 0.141) and HbA_1c (β=0.121, 95% CI 0.062 to 0.180). In the sensitivity analyses restricted to postmenopausal women, we observed a positive association between 17-OHP and glycemic deterioration (OR=1.518, 95% CI 1.033 to 2.264).

CONCLUSIONS

Inter-relations exist between female sex hormones and glucose-related traits among perimenopausal/postmenopausal women and insulin-related traits among men. Endogenous progestogens and estrogens appear to be involved in glucose homeostasis not only in women but in men as well. Further well-powered studies assessing causal associations between endogenous female sex hormones and glycemic traits are warranted.

Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet.

OBJECTIVE

Evaluation of quantitative metabolomics as a diagnostic tool for PPGL.

DESIGN

Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study.

PATIENTS

Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded.

RESULTS

Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling.

CONCLUSIONS

The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.

Mendelian Randomization Study on Amino Acid Metabolism Suggests Tyrosine as Causal Trait for Type 2 Diabetes

Circulating levels of branched-chain amino acids, glycine, or aromatic amino acids have been associated with risk of type 2 diabetes. However, whether those associations reflect causal relationships or are rather driven by early processes of disease development is unclear. We selected diabetes-related amino acid ratios based on metabolic network structures and investigated causal effects of these ratios and single amino acids on the risk of type 2 diabetes in two-sample Mendelian randomization studies. Selection of genetic instruments for amino acid traits relied on genome-wide association studies in a representative sub-cohort (up to 2265 participants) of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study and public data from genome-wide association studies on single amino acids. For the selected instruments, outcome associations were drawn from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis, 74,124 cases and 824,006 controls) consortium. Mendelian randomization results indicate an inverse association for a per standard deviation increase in ln-transformed tyrosine/methionine ratio with type 2 diabetes (OR = 0.87 (0.81-0.93)). Multivariable Mendelian randomization revealed inverse association for higher log10-transformed tyrosine levels with type 2 diabetes (OR = 0.19 (0.04-0.88)), independent of other amino acids. Tyrosine might be a causal trait for type 2 diabetes independent of other diabetes-associated amino acids.

Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring

Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show similar metabolomic patterns several years postpartum. Targeted metabolomics (including 137 metabolites) was performed in plasma samples obtained during an oral glucose tolerance test from 48 mothers with GDM and their offspring at a cross-sectional study visit 8 years after delivery. Partial Pearson's correlations between the area under the curve (AUC) of maternal and offspring metabolites were calculated, yielding so-called Gaussian graphical models. Spearman's correlations were applied to investigate correlations of body mass index (BMI), Matsuda insulin sensitivity index (ISI-M), dietary intake, and physical activity between generations, and correlations of metabolite AUCs with lifestyle variables. This study revealed that BMI, ISI-M, and the AUC of six metabolites (carnitine, taurine, proline, SM(-OH) C14:1, creatinine, and PC ae C34:3) were significantly correlated between mothers and offspring several years postpartum. Intergenerational metabolite correlations were independent of shared BMI, ISI-M, age, sex, and all other metabolites. Furthermore, creatinine was correlated with physical activity in mothers. This study suggests that there is long-term metabolic programming in the offspring of mothers with GDM and informs us about targets that could be addressed by future intervention studies.

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes

Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C18:1 and phosphatidylcholine diacyl C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

Mouse Age Matters: How Age Affects the Murine Plasma Metabolome

A large part of metabolomics research relies on experiments involving mouse models, which are usually 6 to 20 weeks of age. However, in this age range mice undergo dramatic developmental changes. Even small age differences may lead to different metabolomes, which in turn could increase inter-sample variability and impair the reproducibility and comparability of metabolomics results. In order to learn more about the variability of the murine plasma metabolome, we analyzed male and female C57BL/6J, C57BL/6NTac, 129S1/SvImJ, and C3HeB/FeJ mice at 6, 10, 14, and 20 weeks of age, using targeted metabolomics (BIOCRATES AbsoluteIDQ™ p150 Kit). Our analysis revealed high variability of the murine plasma metabolome during adolescence and early adulthood. A general age range with minimal variability, and thus a stable metabolome, could not be identified. Age-related metabolomic changes as well as the metabolite profiles at specific ages differed markedly between mouse strains. This observation illustrates the fact that the developmental timing in mice is strain specific. We therefore stress the importance of deliberate strain choice, as well as consistency and precise documentation of animal age, in metabolomics studies.

Physiological relevance of the neuronal isoform of inositol-1,4,5-trisphosphate 3-kinases in mice

Inositol-1,4,5-trisphosphate 3-kinase-A (ITPKA) is the neuronal isoform of ITPKs and exhibits both actin bundling and InsP₃kinase activity. In addition to neurons, ITPKA is ectopically expressed in tumor cells, where its oncogenic activity increases tumor cell malignancy. In order to analyze the physiological relevance of ITPKA, here we performed a broad phenotypic screening of itpka deficient mice. Our data show that among the neurobehavioral tests analyzed, itpka deficient mice reacted faster to a hotplate, prepulse inhibition was impaired and the accelerating rotarod test showed decreased latency of itpka deficient mice to fall. These data indicate that ITPKA is involved in the regulation of nociceptive pathways, sensorimotor gating and motor learning. Analysis of extracerebral functions in control and itpka deficient mice revealed significantly reduced glucose, lactate, and triglyceride plasma concentrations in itpka deficient mice. Based on this finding, expression of ITPKA was analyzed in extracerebral tissues and the highest level was found in the small intestine. However, functional studies on CaCo-2 control and ITPKA depleted cells showed that glucose, as well as triglyceride uptake, were not significantly different between the cell lines. Altogether, these data show that ITPKA exhibits distinct functions in the central nervous system and reveal an involvement of ITPKA in energy metabolism.

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

BACKGROUND

Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.

METHODS

We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (T_pred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous T_pred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.

FINDINGS

A higher T_pred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=-0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher T_pred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the T_pred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher T_pred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2.

INTERPRETATION

Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health.

FUNDING

This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.

SUN-LB97 Targeted Metabolomics as a Screening Tool in the Diagnosis of Endocrine Hypertension

Arterial hypertension [HT] is a global epidemic that requires adequate treatment to reduce cardiovascular morbidity and mortality. Secondary causes of HT and specifically endocrine hypertension [EHT] (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL] and Cushing syndrome [CS]) can potentially be cured by surgery or treated by targeted medication. However, diagnosis of EHT requires expertise in test selection and interpretation of test results. The availability of experts outnumbers its demand. Thus, preselecting tools are necessary to identify patients who require further referral to an expert. Since targeted metabolomics [TM] is a new method showing promising results in profiling cardiovascular diseases and endocrine conditions associated with HT, we tested the ability of TM in discriminating primary hypertension [PHT] from EHT cases. The study included 282 adult patients (52% female; mean age 49 years) from the European multicentre consortium ENSAT-HT (www.ensat-ht.eu). Of these, 59 were diagnosed with PHT and 223 with EHT (40 CS, 107 PA and 76 PPGL). TM was performed on stored blood samples with a mass spectrometry based approach using the AbsoluteIDQ^TM p180 Kit (BIOCRATES Life Sciences, Austria). In total, 188 metabolites were determined, of which 155 were eligible for statistical analyses according to established selection criteria. To identify relevant discriminating metabolites, a series of univariate and multivariate analyses were applied. Since the distribution of the patients between the clinical entities was different according to sex (p<0.001) and age (p=0.001), analyses were also performed separately for each sex and age group (cut-off 50 years). Thereby, we identified 4 common metabolites (C18:1, C18:2, spermidine, ornithine) from the comparison of PHT with each endocrine hypertension subgroup (CS, PA, PPGL) separately. The ROC curve for discrimination between PHT and EHT built upon these 4 metabolites had an area under the curve (AUC) of 0.79 (95%CI 0.73-0.85). In the comparison of PHT and EHT as a common group 38 metabolites were identified. Using the top 15 metabolites from the latter comparison (C3-DC, C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC20:4, PCaaC38:6, PCaaC40:6, PCaaC42:1) the AUC was 0.86 (95%CI 0.81-0.91). We conclude that TM is associated with distinct metabolic pattern in PHT and EHT and is a promising pre-screening tool for identifying EHT patients.

Impact of maternal smoking associated lyso-phosphatidylcholine 20:3 on offspring brain development

Maternal smoking during pregnancy affects fetal neurological development. Metabolomic studies in the general population suggest that smoking is associated with characteristic metabolic alterations. We investigated the association between the maternal smoking status, the fetal metabolome and head circumference at birth, as a surrogate parameter of brain development. 320 mother/newborn pairs of the Berlin Birth Cohort were investigated. Anthropometric parameters, including head circumference, of newborns of smoking mothers, former smoking mothers, and never smoking mothers were compared to assess the impact of maternal smoking behavior. Associations between maternal smoking behavior and 163 cord blood metabolites and associations between newborn head circumference and concentrations of smoking behavior related metabolites were analysed. Male newborns of smoking mothers had a reduced head circumference when compared with newborns from former smoking and never smoking mothers (p < 0.05). Using linear regression models corrected for established confounding factors, maternal smoking during pregnancy showed an independent association with head circumference (95% CI: -0.75～-0.41 cm, p = 2.45×10^-11). In a stepwise linear regression model corrected for known confounding factors of brain growth lyso-phosphatidylcholine 20:3 (95% CI: 6.68～39.88 cm, p = 4.62×10^-4) was associated with head circumference in male offspring only. None of the metabolites were associated with head circumference of female newborns. In conclusion, maternal smoking during pregnancy impacted on male offspring's development including brain development. The smoking related metabolite lyso-phosphatidylcholine 20:3 was associated with head circumference of male offspring.

Sex hormone-binding globulin, androgens and mortality: the KORA-F4 cohort study

OBJECTIVE

Sex hormone-binding globulin (SHBG) and androgens have been associated with mortality in women and men, but controversy still exists. Our objective was to investigate associations of SHBG and androgens with all-cause and cause-specific mortality in men and women.

DESIGN

1006 men and 709 peri- and postmenopausal women (age range: 45-82 years) from the German population-based KORA F4 cohort study were followed-up for a median of 8.7 years.

METHODS

SHBG was measured with an immunoassay, total testosterone (TT) and dihydrotestosterone (DHT) with mass-spectrometry in serum samples and we calculated free testosterone (cFT). To assess associations between SHBG and androgen levels and mortality, we calculated hazard ratios (HRs) with 95% CIs using Cox proportional-hazards models.

RESULTS

In the cohort, 128 men (12.7%) and 70 women (9.9%) died. In women, we observed positive associations of SHBG with all-cause (HR: 1.54, 95% CI: 1.16-2.04) and with other disease-related mortality (HR: 1.86, 95% CI: 1.08-3.20) and for DHT with all-cause mortality (HR: 1.32, 95% CI: 1.00-1.73). In men, we found a positive association of SHBG (HR: 1.24 95% CI: 1.00-1.54) and inverse associations of TT (HR: 0.87, 95% CI: 0.77-0.97) and cFT (HR: 0.84, 95% CI: 0.73-0.97) with all-cause mortality. No other associations were found for cause-specific mortality.

CONCLUSIONS

Higher SHBG levels were associated with increased risk of all-cause mortality in men and women. Lower TT and cFT levels in men and higher DHT levels in women were associated with increased risk of all-cause mortality. Future, well-powered population-based studies should further investigate cause-specific mortality risk.

Alterations of the acylcarnitine profiles in blood serum and in muscle from periparturient cows with normal or elevated body condition

The objective of the current study was to characterize muscle and blood serum acylcarnitine (AcylCN) profiles and to determine the mRNA abundance of muscle carnitine acyltransferases in periparturient dairy cows with high (HBCS) and normal body condition (NBCS). Fifteen weeks antepartum, 38 pregnant multiparous Holstein cows were assigned to 2 groups that were fed differently to reach the targeted BCS and backfat thickness (BFT) until dry-off at -49 d before calving (HBCS: BCS >3.75 and BFT >1.4 cm; NBCS: <3.5 and <1.2 cm). Thereafter, both groups were fed identical diets. Blood samples and biopsies from the semitendinosus muscle were collected on d -49, 3, 21, and 84 relative to calving. Actual BCS at d -49 were 3.02 ± 0.24 and 3.82 ± 0.33 (mean ± SD) for NBCS and HBCS, respectively. In both groups, serum profiles showed marked changes during the periparturient period, with decreasing concentrations of free carnitine and increasing concentrations of long-chain AcylCN. Compared with NBCS, HBCS had greater serum long-chain AcylCN in early lactation, which may point to an insufficient adaptation of their metabolism in response to the metabolic load of fatty acids around parturition. The muscle concentrations of C5-, C9-, C18:1-, and C18:2-AcylCN were lower and those of C14:2-AcylCN were greater in HBCS than in NBCS cows. The mRNA abundance of carnitine palmitoyltransferase (CPT)1, muscle isoform (CPT1b) and CPT2 increased from d -49 to early lactation (d 3, d 21), followed by a decline to nearly antepartum values by d 84; this change was not affected by group. In conclusion, over-conditioning around calving seems to be associated with mitochondrial overload, which can result in incomplete fatty acid oxidation in dairy cows.

Induction of the nicotinamide riboside kinase NAD+ salvage pathway in a model of sarcoplasmic reticulum dysfunction

Background

Hexose-6-Phosphate Dehydrogenase (H6PD) is a generator of NADPH in the Endoplasmic/Sarcoplasmic Reticulum (ER/SR). Interaction of H6PD with 11β-hydroxysteroid dehydrogenase type 1 provides NADPH to support oxo-reduction of inactive to active glucocorticoids, but the wider understanding of H6PD in ER/SR NAD(P)(H) homeostasis is incomplete. Lack of H6PD results in a deteriorating skeletal myopathy, altered glucose homeostasis, ER stress and activation of the unfolded protein response. Here we further assess muscle responses to H6PD deficiency to delineate pathways that may underpin myopathy and link SR redox status to muscle wide metabolic adaptation.

Methods

We analysed skeletal muscle from H6PD knockout (H6PDKO), H6PD and NRK2 double knockout (DKO) and wild-type (WT) mice. H6PDKO mice were supplemented with the NAD⁺ precursor nicotinamide riboside. Skeletal muscle samples were subjected to biochemical analysis including NAD(H) measurement, LC-MS based metabolomics, Western blotting, and high resolution mitochondrial respirometry. Genetic and supplement models were assessed for degree of myopathy compared to H6PDKO.

Results

H6PDKO skeletal muscle showed adaptations in the routes regulating nicotinamide and NAD⁺ biosynthesis, with significant activation of the Nicotinamide Riboside Kinase 2 (NRK2) pathway. Associated with changes in NAD⁺ biosynthesis, H6PDKO muscle had impaired mitochondrial respiratory capacity with altered mitochondrial acylcarnitine and acetyl-CoA metabolism. Boosting NAD⁺ levels through the NRK2 pathway using the precursor nicotinamide riboside elevated NAD⁺/NADH but had no effect to mitigate ER stress and dysfunctional mitochondrial respiratory capacity or acetyl-CoA metabolism. Similarly, H6PDKO/NRK2 double KO mice did not display an exaggerated timing or severity of myopathy or overt change in mitochondrial metabolism despite depression of NAD⁺ availability.

Conclusions

These findings suggest a complex metabolic response to changes in muscle SR NADP(H) redox status that result in impaired mitochondrial energy metabolism and activation of cellular NAD⁺ salvage pathways. It is possible that SR can sense and signal perturbation in NAD(P)(H) that cannot be rectified in the absence of H6PD. Whether NRK2 pathway activation is a direct response to changes in SR NAD(P)(H) availability or adaptation to deficits in metabolic energy availability remains to be resolved.

Metabolome profiling in skeletal muscle to characterize metabolic alterations in over-conditioned cows during the periparturient period

The transition from late gestation to early lactation is associated with extensive changes in metabolic, endocrine, and immune functions in dairy cows. Skeletal muscle plays an important role in maintaining the homeorhetic adaptation to the metabolic needs of lactation. The objective of this study was to characterize the skeletal muscle metabolome in the context of the metabolic changes that occur during the transition period in dairy cows with high (HBCS) versus normal body condition (NBCS). Fifteen weeks antepartum, 38 pregnant multiparous Holstein cows were assigned to 1 of 2 groups, which were fed differently to reach the targeted BCS and back fat thickness (BFT) until dry-off at -49 d before calving (HBCS: >3.75 and >1.4 cm; NBCS: <3.5 and <1.2 cm). During the dry period and the subsequent lactation, both groups were fed identical diets. The differences in both BCS and BFT were maintained throughout the study. The metabolome was characterized in skeletal muscle samples (semitendinosus muscle) collected on d -49, 3, 21, and 84 relative to calving using a targeted metabolomics approach (AbsoluteIDQ p180 kit; Biocrates Life Sciences AG, Innsbruck, Austria), which allowed for the quantification of up to 188 metabolites from 6 different compound classes (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, and hexoses). On d -49, the concentrations of citrulline and hydroxytetradecadienyl-l-carnitine in muscle were higher in HBCS cows than in NBCS cows, but those of carnosine were lower. Over-conditioning did not affect the muscle concentrations of any of the metabolites on d 3. On d 21, the concentrations of phenylethylamine and linoleylcarnitine in muscle were lower in HBCS cows than in NBCS cows, and the opposite was true for lysophosphatidylcholine acyl C20:4. On d 84, the significantly changed metabolites were mainly long-chain (>C32) acyl-alkyl phosphatidylcholine and di-acyl phosphatidylcholine, along with 3 long-chain (>C16) sphingomyelin that were all lower in HBCS cows than in NBCS cows. These data contribute to a better understanding of the metabolic adaptation in skeletal muscle of dairy cows during the transition period, although the physiological significance and underlying molecular mechanisms responsible for the regulation of citrulline, hydroxytetradecadienyl-l-carnitine, carnosine, and phenylethylamine associated with over-conditioning are still elusive and warrant further investigation. The changes observed in muscle lysophosphatidylcholine and phosphatidylcholine concentrations may point to an alteration in phosphatidylcholine metabolism, probably resulting in an increase in membrane stiffness, which may lead to abnormalities in insulin signaling in the muscle of over-conditioned cows.

Proteasome activity and expression of mammalian target of rapamycin signaling factors in skeletal muscle of dairy cows supplemented with conjugated linoleic acids during early lactation

The mammalian target of rapamycin (mTOR) is a major regulator of protein synthesis via its main downstream effectors, ribosomal protein S6 kinase (S6K1) and eukaryotic initiation factor 4E binding protein (4EBP1). The ubiquitin-proteasome system (UPS) is the main proteolytic pathway in muscle, and the muscle-specific ligases tripartite motif containing 63 (TRIM63; also called muscle-specific ring-finger protein 1, MuRF-1) and F-box only protein 32 (FBXO32; also called atrogin-1) are important components of the UPS. We investigated 20S proteasome activity and mRNA expression of key components of mTOR signaling and UPS in skeletal muscle of dairy cows during late gestation and early lactation and tested the effects of dietary supplementation (from d 1 in milk) with conjugated linoleic acids (sCLA; 100 g/d; n = 11) compared with control fat-supplemented cows (CTR; n = 10). Blood and muscle tissue (semitendinosus) samples were collected on d -21, 1, 21, and 70 relative to parturition. Dry matter intake increased with time of lactation in both groups. It was lower in sCLA than in CTR on d 21, which resulted in a reduced calculated metabolizable protein balance. Most serum and muscle concentrations of AA followed time-related changes but were unaffected by CLA supplementation. In both groups, serum and muscle 3-methylhistidine (3-MH) concentrations and the ratio of 3-MH:creatinine increased from d -21 to d 1, followed by a decline on d 21. The mRNA abundance of MTOR on d 21 and 70 was greater in sCLA than in CTR. The abundance of 4EBP1 mRNA did not differ between groups but was upregulated in both on d 1. The mRNA abundance of S6K1 on d 70 was greater in CTR than in sCLA, but remained unchanged over time in both groups. The mRNA abundance of FBXO32 (encoding atrogin-1) on d 21 was greater in sCLA than in CTR. The mRNA abundance of TRIM63 (also known as MuRF1) showed a similar pattern as FBXO32 in both groups: an increase from d -21 to d 1, followed by a decline. The mRNA for the α (BCKDHA) and β (BCKDHB) polypeptide of branched-chain α-keto acid dehydrogenase was elevated in sCLA and CTR cows on d 21, respectively, suggesting a role of CLA in determining the metabolic fate of branched-chain AA. For the mTOR protein, no group differences were observed. The abundance of S6K1 protein was greater across all time points in sCLA versus CTR. The antepartum 20S proteasome activity in muscle was elevated in both groups compared with postpartum, probably reflecting the start of protein mobilization before parturition. Plasma insulin concentrations decreased in both groups postpartum but to a greater extent in CTR than in sCLA, resulting in greater insulin concentrations in sCLA than in CTR. Thus, the greater abundance of MTOR mRNA and S6K1 protein in sCLA compared with CTR might be mediated by the greater plasma insulin postpartum. The upregulation of MTOR mRNA in sCLA cows on d 21, despite greater FBXO32 mRNA abundance, may reflect a simultaneous activation of both anabolic and catabolic signaling pathways, likely resulting in greater protein turnover.

Altered metabolism distinguishes high-risk from stable carotid atherosclerotic plaques

Aims

Identification and treatment of the rupture prone atherosclerotic plaque remains a challenge for reducing the burden of cardiovascular disease. The interconnection of metabolic and inflammatory processes in rupture prone plaques is poorly understood. Herein, we investigate associations between metabolite profiles, inflammatory mediators and vulnerability in carotid atherosclerotic plaques.

Methods and results

We collected 159 carotid plaques from patients undergoing endarterectomy and measured 165 different metabolites in a targeted metabolomics approach. We identified a metabolite profile in carotid plaques that associated with histologically evaluated vulnerability and inflammatory mediators, as well as presence of symptoms in patients. The distinct metabolite profiles identified in high-risk and stable plaques were in line with different transcription levels of metabolic enzymes in the two groups, suggesting an altered metabolism in high-risk plaques. The altered metabolic signature in high-risk plaques was consistent with a change to increased glycolysis, elevated amino acid utilization and decreased fatty acid oxidation, similar to what is found in activated leucocytes and cancer cells.

Conclusion

These results highlight a possible key role of cellular metabolism to support inflammation and a high-risk phenotype of atherosclerotic plaques. Targeting the metabolism of atherosclerotic plaques with novel metabolic radiotracers or inhibitors might therefore be valid future approaches to identify and treat the high-risk atherosclerotic plaque.