Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Streptococcus Species Abundance in the Gut Is Linked to Subclinical Coronary Atherosclerosis in 8973 Participants From the SCAPIS Cohort.

BACKGROUND: Gut microbiota have been implicated in atherosclerotic disease, but their relation with subclinical coronary atherosclerosis is unclear. This study aimed to identify associations between the gut microbiome and computed tomography-based measures of coronary atherosclerosis and to explore relevant clinical correlates. METHODS: We conducted a cross-sectional study of 8973 participants (50 to 65 years of age) without overt atherosclerotic disease from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study). Coronary atherosclerosis was measured using coronary artery calcium score and coronary computed tomography angiography. Gut microbiota species abundance and functional potential were assessed with shotgun metagenomics sequencing of fecal samples, and associations with coronary atherosclerosis were evaluated with multivariable regression models adjusted for cardiovascular risk factors. Associated species were evaluated for association with inflammatory markers, metabolites, and corresponding species in saliva. RESULTS: The mean age of the study sample was 57.4 years, and 53.7% were female. Coronary artery calcification was detected in 40.3%, and 5.4% had at least 1 stenosis with >50% occlusion. Sixty-four species were associated with coronary artery calcium score independent of cardiovascular risk factors, with the strongest associations observed for Streptococcus anginosus and Streptococcus oralis subsp oralis (P<1×10-5). Associations were largely similar across coronary computed tomography angiography-based measurements. Out of the 64 species, 19 species, including streptococci and other species commonly found in the oral cavity, were associated with high-sensitivity C-reactive protein plasma concentrations, and 16 with neutrophil counts. Gut microbial species that are commonly found in the oral cavity were negatively associated with plasma indole propionate and positively associated with plasma secondary bile acids and imidazole propionate. Five species, including 3 streptococci, correlated with the same species in saliva and were associated with worse dental health in the Malmö Offspring Dental Study. Microbial functional potential of dissimilatory nitrate reduction, anaerobic fatty acid ß-oxidation, and amino acid degradation were associated with coronary artery calcium score. CONCLUSIONS: This study provides evidence of an association of a gut microbiota composition characterized by increased abundance of Streptococcus spp and other species commonly found in the oral cavity with coronary atherosclerosis and systemic inflammation markers. Further longitudinal and experimental studies are warranted to explore the potential implications of a bacterial component in atherogenesis.

Unraveling the metabolomic architecture of autism in a large Danish population-based cohort.

BACKGROUND: The prevalence of autism in Denmark has been increasing, reaching 1.65% among 10-year-old children, and similar trends are seen elsewhere. Although there are several factors associated with autism, including genetic, environmental, and prenatal factors, the molecular etiology of autism is largely unknown. Here, we use untargeted metabolomics to characterize the neonatal metabolome from dried blood spots collected shortly after birth. METHODS: We analyze the metabolomic profiles of a subset of a large Danish population-based cohort (iPSYCH2015) consisting of over 1400 newborns, who later are diagnosed with autism and matching controls and in two Swedish population-based cohorts comprising over 7000 adult participants. Mass spectrometry analysis was performed by a timsTOF Pro operated in QTOF mode, using data-dependent acquisition. By applying an untargeted metabolomics approach, we could reproducibly measure over 800 metabolite features. RESULTS: We detected underlying molecular perturbations across several metabolite classes that precede autism. In particular, the cyclic dipeptide cyclo-leucine-proline (FDR-adjusted p = 0.003) and the carnitine-related 5-aminovaleric acid betaine (5-AVAB) (FDR-adjusted p = 0.03), were associated with an increased probability for autism, independently of known prenatal and genetic risk factors. Analysis of genetic and dietary data in adults revealed that 5-AVAB was associated with increased habitual dietary intake of dairy (FDR-adjusted p < 0.05) and with variants near SLC22A4 and SLC22A5 (p < 5.0e - 8), coding for a transmembrane carnitine transporter protein involved in controlling intracellular carnitine levels. CONCLUSIONS: Cyclo-leucine-proline and 5-AVAB are associated with future diagnosis of autism in Danish neonates, both representing novel early biomarkers for autism. 5-AVAB is potentially modifiable and may influence carnitine homeostasis.

High level of immunoglobulin G targeting mycoplasma or cytomegalovirus in the newborn increases risk of ADHD.

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder typically detected in childhood. Although ADHD has been demonstrated to have a strong genetic component, environmental risk factors, such as maternal infections during pregnancy, may also play a role. We therefore measured the immunological response to 5 abundant microorganisms (Toxoplasmosis Gondii, cytomegalovirus (CMV), Herpes Simplex Virus 1, Epstein Barr Virus and mycoplasma pneumoniae) in newborn heel prick samples of 1679 ADHD cases and 2948 matching controls as part of the iPSYCH Danish case-cohort study. We found an association between high anti-CMV (OR 1.30, 95 % CI [1.09,1.55], p = 0.015) and anti-mycoplasma (OR 1.30, 95 % CI [1.07,1.59], p = 0.037) signal and those newborns later being diagnosed with ADHD. The risk estimate remained increased when controlling for ADHD polygenic risk score as well as penicillin prescriptions. We saw a dose-response association with the amount of positive anti-microorganism titers increasing the risk of being diagnosed with ADHD later in life (p = 0.01 for the trend), suggesting that the more activated the immune system is prior to or at birth, the higher the risk is for a later diagnosis with ADHD. If the associations are causal, they emphasize the importance of a healthy life style during pregnancy to reduce the risk of infections when pregnant and the associated risks for the child.

A healthy dietary metabolic signature is associated with a lower risk for type 2 diabetes and coronary artery disease.

BACKGROUND: The global burden of cardiovascular disease and type 2 diabetes could be decreased by improving dietary factors, but identification of groups suitable for interventional approaches can be difficult. Reporting of dietary intake is prone to errors, and measuring of metabolites has shown promise in determining habitual dietary intake. Our aim is to create a metabolic signature that is associated with healthy eating and test if it associates with type 2 diabetes and coronary artery disease risk. METHODS: Using plasma metabolite data consisting of 111 metabolites, partial least square (PLS) regression was used to identify a metabolic signature associated with a health conscious food pattern in the Malmö Offspring Study (MOS, n = 1538). The metabolic signature's association with dietary intake was validated in the Malmö Diet and Cancer study (MDC, n = 2521). The associations between the diet-associated metabolic signature and incident type 2 diabetes and coronary artery disease (CAD) were tested using Cox regression in MDC and logistic regression in Malmö Preventive Project (MPP, n = 1083). Modelling was conducted unadjusted (model 1), adjusted for potential confounders (model 2) and additionally for potential mediators (model 3). RESULTS: The metabolic signature was associated with lower risk for type 2 diabetes in both MDC (hazard ratio: 0.58, 95% CI 0.52-0.66, per 1 SD increment of the metabolic signature) and MPP (odds ratio: 0.54, 95% CI 0.44-0.65 per 1 SD increment of the metabolic signature) in model 2. The results were attenuated but remained significant in model 3 in both MDC (hazard ratio 0.73, 95% CI 0.63-0.83) and MPP (odds ratio 0.70, 95% CI 0.55-0.88). The diet-associated metabolic signature was also inversely associated with lower risk of CAD in both MDC and MPP in model 1, but the association was non-significant in model 3. CONCLUSIONS: In this proof-of-concept study, we identified a healthy diet-associated metabolic signature, which was inversely associated with future risk for type 2 diabetes and coronary artery disease in two different cohorts. The association with diabetes was independent of traditional risk factors and might illustrate an effect of health conscious dietary intake on cardiometabolic health.

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

BACKGROUND: Metabolite profiles provide snapshots of the overall effect of numerous exposures accumulated over life courses, which may lead to health outcomes in the future. OBJECTIVE: We hypothesized that the risk of all-cause mortality is linked to alterations in metabolism earlier in life, which are reflected in plasma metabolite profiles. We aimed to identify plasma metabolites associated with future risk of all-cause mortality. METHODS: Through metabolomics, 110 metabolites were measured in 3833 individuals from the Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC). A total of 1574 deaths occurred within an average follow-up time of 22.2 years. Metabolites that were significantly associated with all-cause mortality in MDC-CC were replicated in 1500 individuals from Malmö Preventive Project re-examination (MPP), among whom 715 deaths occurred within an average follow-up time of 11.3 years. RESULTS: Twenty two metabolites were significantly associated with all-cause mortality in MDC-CC, of which 13 were replicated in MPP. Levels of trigonelline, glutamate, dimethylglycine, C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, and 1-methyladenosine were associated with an increased risk, while levels of valine, tryptophan, lysine, leucine, histidine, and 2-aminoisobutyrate were associated with a decreased risk of all-cause mortality. CONCLUSION: We used metabolomics in two Swedish prospective cohorts and identified replicable associations between 13 metabolites and future risk of all-cause mortality. Novel associations between five metabolites-C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, trigonelline, and 2-aminoisobutyrate-and all-cause mortality were discovered. These findings suggest potential new biomarkers for the prediction of mortality and provide insights for understanding the biochemical pathways that lead to mortality.

The inverse association between a fish consumption biomarker and gingival inflammation and periodontitis: A population-based study.

AIM: The metabolite 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF) is a fatty fish-intake biomarker. We investigated the association between plasma levels of CMPF in relation to gingival inflammation and periodontitis case definition, as well as the extent and severity variables. MATERIALS AND METHODS: The Malmö Offspring Study is a population-based study, and the Malmö Offspring Dental Study (MODS) is its dental arm, including periodontal charting. Plasma CMPF was measured using liquid chromatography-mass spectrometry and studied in relation to periodontal diagnosis and parameters using multivariable linear or logistic regression modelling adjusting for age, sex, education, body mass index, fasting glucose, and smoking. RESULTS: Metabolite data were available for 922 MODS participants. Higher CMPF levels were associated with less gingival inflammation (ß = -2.12, p = .002) and lower odds of severe periodontitis (odds ratio [OR] = 0.74, 95% confidence interval [CI]: 0.56 to 0.98). Higher CMPF levels were also associated with more teeth (ß = 0.19, p = .001), lower number of periodontal pockets (≥4 mm) (ß = -1.07, p = .007), and lower odds of having two or more periodontal pockets of ≥6 mm (OR = 0.80, 95% CI: 0.65 to 0.98) in fully adjusted models. CONCLUSIONS: CMPF, a validated biomarker of fatty fish consumption, is associated with less periodontal inflammation and periodontitis. Residual confounding cannot be ruled out, and future studies are warranted.

Replication study reveals miR-483-5p as an important target in prevention of cardiometabolic disease.

BACKGROUND: Alterations in levels of circulating micro-RNAs might reflect within organ signaling or subclinical tissue injury that is linked to risk of diabetes and cardiovascular risk. We previously found that serum levels of miR-483-5p is correlated with cardiometabolic risk factors and incidence of cardiometabolic disease in a case-control sample from the populations-based Malmö Diet and Cancer Study Cardiovascular Cohort (MDC-CC). We here aimed at replicating these findings and to test for association with carotid atherosclerosis. METHODS: We measured miR-483-5p in fasting serum of 1223 healthy subjects from the baseline examination of the population-based, prospective cohort study Malmö Offspring Study (MOS) and correlated miR-483-5p to cardiometabolic risk factors and to incidence of diabetes mellitus and coronary artery disease (CAD) during 3.7 (± 1.3) years of follow-up using logistic regression. In both MOS and MDC-CC we related mir-483-5p to carotid atherosclerosis measured with ultrasound. RESULTS: In cross-sectional analysis miR-483-5p was correlated with BMI, waist circumference, HDL, and sex. After adjustment for age and sex, the association remained significant for all risk factors except for HDL. Logistic regression analysis showed significant associations between miR-483-5p and new-onset diabetes (OR = 1.94, 95% CI 1.06-3.56, p = 0.032) and cardiovascular disease (OR = 1.99, 95% CI 1.06-3.75, p = 0.033) during 3.7 (± 1.3) years of follow-up. Furthermore, miR-483-5p was significantly related with maximum intima-media thickness of the carotid bulb in MDC-CC (p = 0.001), but not in MOS, whereas it was associated with increasing number of plaques in MOS (p = 0.007). CONCLUSION: miR-483-5p is related to an unfavorable cardiometabolic risk factor profile and predicts diabetes and CAD, possibly through an effect on atherosclerosis. Our results encourage further studies of possible underlying mechanisms and means of modifying miR-483-5p as a possible interventional target in prevention of cardiometabolic disease.

The association between water intake and future cardiometabolic disease outcomes in the Malmö Diet and Cancer cardiovascular cohort.

The aim of this study was to explore the longitudinal association between reported baseline water intake and incidence of coronary artery disease (CAD) and type 2 diabetes in the Malmö Diet and Cancer Cohort (n = 25,369). Using cox proportional hazards models, we separately modelled the effect of plain and total (all water, including from food) water on CAD and type 2 diabetes risk, whilst adjusting for age, sex, diet collection method, season, smoking status, alcohol intake, physical activity, education level, energy intake, energy misreporting, body mass index, hypertension, lipid lowering medication, apolipoprotein A, apolipoprotein B, and dietary variables. Sensitivity analyses were run to assess validity. After adjustment, no association was found between tertiles of plain or total water intake and type 2 diabetes risk. For CAD, no association was found comparing moderate to low intake tertiles from plain or total water, however, risk of CAD increased by 12% (95% CI 1.03, 1.21) when comparing high to low intake tertiles of plain water, and by 17% (95% CI 1.07, 1.27) for high versus low tertiles of total water. Sensitivity analyses were largely in agreement. Overall, baseline water intake was not associated with future type 2 diabetes risk, whilst CAD risk was higher with higher water intakes. Our findings are discordant with prevailing literature suggesting higher water intakes should reduce cardiometabolic risk. These findings may be an artefact of limitations within the study, but future research is needed to understand if there is a causal underpinning.

Urbanisation-associated shifts in the avian metabolome within the annual cycle.

While organisms have evolved to cope with predictable changes in the environment, the rapid rate of current global change presents numerous novel and unpredictable stressors to which organisms have had less time to adapt. To persist in the urban environment, organisms must modify their physiology, morphology and behaviour accordingly. Metabolomics offers great potential for characterising organismal responses to natural and anthropogenic stressors at the systems level and can be applied to any species, even without genomic knowledge. Using metabolomic profiling of blood, we investigated how two closely related species of passerine bird respond to the urban environment. Great tits Parus major and blue tits Cyanistes caeruleus residing in urban and forest habitats were sampled during the breeding (spring) and non-breeding (winter) seasons across replicated sites in southern Sweden. During breeding, differences in the plasma metabolome between urban and forest birds were characterised by higher levels of amino acids in urban-dwelling tits and higher levels of fatty acyls in forest-dwelling tits. The suggested higher rates of fatty acid oxidation in forest tits could be driven by habitat-associated differences in diet and could explain the higher reproductive investment and success of forest tits. High levels of amino acids in breeding urban tits could reflect the lack of lipid-rich caterpillars in the urban environment and a dietary switch to protein-rich spiders, which could be of benefit for tackling inflammation and oxidative stress associated with pollution. In winter, metabolomic profiles indicated lower overall levels of amino acids and fatty acyls in urban tits, which could reflect relaxed energetic demands in the urban environment. Our metabolomic profiling of two urban-adapted species suggests that their metabolism is modified by urban living, though whether these changes represent adaptative or non-adaptive mechanisms to cope with anthropogenic challenges remains to be determined.

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

BACKGROUND: Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality. METHODS AND RESULTS: We included 6865 individuals from 2 Swedish population-based cohorts. Untargeted plasma metabolomics was performed by liquid-chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid-femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite-predicted aortic stiffness and risk of new-onset cardiovascular disease, cardiovascular mortality, and all-cause mortality were calculated. Metabolite-predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R2=0.27 and R2=0.39, respectively). Metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly correlated with age, sex, systolic blood pressure, body mass index, and low-density lipoprotein. After 23 years of follow-up, metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly associated with increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality. CONCLUSIONS: Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality after more than 23 years of follow-up in the general population.

Proteomic biomarkers and pathway analysis for progression to heart failure in three epidemiological representative cohorts.

AIMS: Biomarkers associated with asymptomatic ventricular dysfunction might improve risk stratification and identify pathways leading to heart failure (HF). We explored the association between proteomic biomarkers and left ventricular hypertrophy (LVH), diastolic dysfunction (DD) and incident HF in three population-based cohorts. METHODS AND RESULTS: A chip was used to measure 92 protein biomarkers in blood samples from >1500 Malmö Preventive Project (MPP) participants, of whom 514 had LVH (34%), 462 had DD (32.4%) and, over a median follow-up of 13 (11-14) years, 130 developed HF (7.7%). Findings were confirmed in the STANISLAS (n > 1500, 238 participants with LVH, 76 with DD) and HOMAGE case-control (562 cases of incident HF, 871 controls) cohorts. In multivariable logistic or Cox regression analyses adjusted for age, sex and cardiovascular risk factors, N-terminal pro-B-type natriuretic peptide (NT-proBNP) was associated with LVH, DD and incident HF in all cohorts: MPP (LVH odds ratio [OR] [95% confidence interval] 1.48 [1.28-1.71]; DD OR 1.71 [1.53-1.92]; HF HR 1.98 [1.66-2.36]); STANISLAS (LVH OR 1.20 [1.02-1.41]; DD OR 1.46 [1.12-1.90]); HOMAGE (HF HR 1.85 [1.62-2.12]). Galectin-4, growth differentiation factor 15 and suppression of tumorigenicity-2 were associated with incident HF in MPP and HOMAGE. A pathway enrichment analysis suggested that inflammation and viral infection were related to incident HF. CONCLUSION: In conclusion, our study reinforces the role of NT-proBNP as a key biomarker for asymptomatic cardiac dysfunction and incident HF, consistent with its established use in clinical practice. This underscores the value of NT-proBNP for identifying patients at high risk for HF, and provides insights into pathways leading to HF and potential therapeutic targets.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Effects of Long-Term Storage on the Biobanked Neonatal Dried Blood Spot Metabolome.

Over 2.5 million neonatal dried blood spots (DBS) are stored at the Danish National Biobank. These samples offer extraordinary possibilities for metabolomics research, including prediction of disease and understanding of underlying molecular mechanisms of disease development. Nevertheless, Danish neonatal DBS have been little explored in metabolomics studies. One question that remains underinvestigated is the long-term stability of the large number of metabolites typically assessed in untargeted metabolomics over long time periods of storage. Here, we investigate temporal trends of metabolites measured in 200 neonatal DBS collected over a time course of 10 years, using an untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) based metabolomics protocol. We found that a majority (71%) of the metabolome was stable during 10 years of storage at -20 °C. However, we found decreasing trends for lipid-related metabolites, such as glycerophosphocholines and acylcarnitines. A few metabolites, including glutathione and methionine, may be strongly influenced by storage, with changes in metabolite levels up to 0.1-0.2 standard deviation units per year. Our findings indicate that untargeted metabolomics of DBS samples, with long-term storage in biobanks, is suitable for retrospective epidemiological studies. We identify metabolites whose stability in DBS should be closely monitored in future studies of DBS samples with long-term storage.

Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study.

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and 1H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.

Multi-omic analyses of triptan-treated migraine attacks gives insight into molecular mechanisms.

Migraine is a common, polygenic disorder that is characterized by moderate to severe headache attacks. Migraine attacks are commonly treated with triptans, i.e. serotonin receptor agonists. However, triptans are effective in ~ 60% of the population, and the mechanisms of triptans are debated. Here, we aim to expose the mechanisms of triptan using metabolomics and transcriptomics in spontaneous migraine attacks. We collected temporal multi-omics profiles on 24 migraine patients, using samples collected at a migraine attack, 2 h after treatment with a triptan, when headache-free, and after a cold-pressor test. Differential metabolomic analysis was performed to find metabolites associated with treatment. Their effect was further investigated using correlation analysis and a machine learning approach. We found three differential metabolites: cortisol, sumatriptan and glutamine. The change in sumatriptan levels correlated with a change in GNAI1 and VIPR2 gene expression, both known to regulate cAMP levels. Furthermore, we found fatty acid oxidation to be affected, a mechanism known to be involved in migraine but not previously found in relation to triptans. In conclusion, using an integrative approach we find evidence for a role of glutamine, cAMP regulation, and fatty acid oxidation in the molecular mechanisms of migraine and/or the effect of triptans.

Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population.

BACKGROUND: Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease. METHODS: We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999). RESULTS: Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism. CONCLUSIONS: Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09070822.mp3.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes.

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

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

OBJECTIVE: Obesity is a key risk factor for type 2 diabetes; however, up to 20% of patients are normal weight. Our aim was to identify metabolite patterns reproducibly predictive of BMI and subsequently to test whether lean individuals who carry an obese metabolome are at hidden high risk of obesity-related diseases, such as type 2 diabetes. RESEARCH DESIGN AND METHODS: Levels of 108 metabolites were measured in plasma samples of 7,663 individuals from two Swedish and one Italian population-based cohort. Ridge regression was used to predict BMI using the metabolites. Individuals with a predicted BMI either >5 kg/m2 higher (overestimated) or lower (underestimated) than their actual BMI were characterized as outliers and further investigated for obesity-related risk factors and future risk of type 2 diabetes and mortality. RESULTS: The metabolome could predict BMI in all cohorts (r2 = 0.48, 0.26, and 0.19). The overestimated group had a BMI similar to individuals correctly predicted as normal weight, had a similar waist circumference, were not more likely to change weight over time, but had a two times higher risk of future type 2 diabetes and an 80% increased risk of all-cause mortality. These associations remained after adjustments for obesity-related risk factors and lifestyle parameters. CONCLUSIONS: We found that lean individuals with an obesity-related metabolome have an increased risk for type 2 diabetes and all-cause mortality compared with lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals to initiate lifestyle and pharmacological interventions.

An online atlas of human plasma metabolite signatures of gut microbiome composition.

Human gut microbiota produce a variety of molecules, some of which enter the bloodstream and impact health. Conversely, dietary or pharmacological compounds may affect the microbiota before entering the circulation. Characterization of these interactions is an important step towards understanding the effects of the gut microbiota on health. In this cross-sectional study, we used deep metagenomic sequencing and ultra-high-performance liquid chromatography linked to mass spectrometry for a detailed characterization of the gut microbiota and plasma metabolome, respectively, of 8583 participants invited at age 50 to 64 from the population-based Swedish CArdioPulmonary bioImage Study. Here, we find that the gut microbiota explain up to 58% of the variance of individual plasma metabolites and we present 997 associations between alpha diversity and plasma metabolites and 546,819 associations between specific gut metagenomic species and plasma metabolites in an online atlas ( https://gutsyatlas.serve.scilifelab.se/ ). We exemplify the potential of this resource by presenting novel associations between dietary factors and oral medication with the gut microbiome, and microbial species strongly associated with the uremic toxin p-cresol sulfate. This resource can be used as the basis for targeted studies of perturbation of specific metabolites and for identification of candidate plasma biomarkers of gut microbiota composition.