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Schematic overview of mediation analysis with lysoPC a C17:0 and hexoses (a) or N-lactoylvaline, lactate, N-lactoylleucine, formiminoglutamate and X-24295 (b) as mediators. Numbers above the red arrows indicate the percentage and significance of mediation effects. T2D, type 2 diabetes
Source publication
Aims/hypothesis
Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes.
Methods
As part of the Innovative Medicines Initiative - Diabetes Research on Patient Str...
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Background
Milk fever (MF), a metabolic disorder in dairy cows characterized by low blood calcium concentrations postpartum, is well‐recognized clinically. However, comprehensive data on the alteration of metabolites associated with this condition remains sparse.
Hypothesis
Delineate serum metabolite profiles and metabolic pathways preceding, coin...
Citations
... N-lactoyltyrosine, which belongs to a class of pseudopeptides, formed by lactic acid and an amino acid (52), was associated with brain health in our study, with higher levels being linked to lower brain volume. N-lactoyl amino acids received some attention in diabetes research recently (53,54); while higher levels of N-lactoyl amino acids (including Nlactoylphenylalanine, N-lactoyltyrosine, and N-lactoylleucine) associate with decreasing is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint ...
Increasing evidence suggests the involvement of metabolic alterations in neurological disorders, including Alzheimer’s disease (AD), and highlights the significance of the peripheral metabolome, influenced by genetic factors and modifiable environmental exposures, for brain health. In this study, we examined 1,387 metabolites in plasma samples from 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study. We assessed the relation of metabolites with general cognition (G-factor) and magnetic resonance imaging (MRI) markers using linear regression and estimated the variance of these metabolites explained by genes, gut microbiome, lifestyle factors, common clinical comorbidities, and medication using gradient boosting decision tree analysis. Twenty-one metabolites and one metabolite were significantly associated with total brain volume and total white matter lesions, respectively. Fourteen metabolites showed significant associations with G-factor, with ergothioneine exhibiting the largest effect (adjusted mean difference = 0.122, P = 4.65x10 ⁻⁷ ). Associations for nine of the 14 metabolites were replicated in an independent, older cohort. The metabolite signature of incident AD in the replication cohort resembled that of cognition in the discovery cohort, emphasizing the potential relevance of the identified metabolites to disease pathogenesis. Lifestyle, clinical variables, and medication were most important in determining these metabolites’ blood levels, with lifestyle, explaining up to 28.6% of the variance. Smoking was associated with ten metabolites linked to G-factor, while diabetes and antidiabetic medication were associated with 13 metabolites linked to MRI markers, including N-lactoyltyrosine. Antacid medication strongly affected ergothioneine levels. Mediation analysis revealed that lower ergothioneine levels may partially mediate negative effects of antacids on cognition (31.5%). Gut microbial factors were more important for the blood levels of metabolites that were more strongly associated with cognition and incident AD in the older replication cohort (beta-cryptoxanthin, imidazole propionate), suggesting they may be involved later in the disease process. The detailed results on how multiple modifiable factors affect blood levels of cognition- and brain imaging-related metabolites in dementia-free participants may help identify new AD prevention strategies.
Background
Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep-related phenotypes and blood metabolites.
Methods
Utilising data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep-related phenotypes, grouped in several domains (sleep disordered breathing (SDB), sleep duration, sleep timing, self-reported insomnia symptoms, excessive daytime sleepiness (EDS), and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualise and interpret the associations between sleep phenotypes and metabolites.
Findings
The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, primary bile acid metabolism showed the highest cumulative percentage of statistically significant associations across all sleep phenotype domains except for SDB and EDS phenotypes. Several metabolites were associated with multiple sleep phenotypes, from a few domains. Glycochenodeoxycholate, vanillyl mandelate (VMA) and 1-stearoyl-2-oleoyl-GPE (18:0/18:1) were associated with the highest number of sleep phenotypes, while pregnenolone sulfate was associated with all sleep phenotype domains except for sleep duration. N-lactoyl amino acids such as N-lactoyl phenylalanine (lac-Phe), were associated with sleep duration, SDB, sleep timing and heart rate during sleep.
Interpretation
This atlas of sleep–metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.
Funding
R01HL161012, R35HL135818, R01AG80598.