Plasma metabolomic profiling of hypertrophic cardiomyopathy patients before and after surgical myectomy suggests postoperative improvement in metabolic function

Plasma metabolites as potential markers and targets to prevent and treat urolithiasis: a Mendelian randomization study

Background

Studies on the relationships between diseases of the urinary system and human plasma proteomes have identified several potential biomarkers. However, none of these studies have elucidated the causal relationships between plasma proteins and urolithiasis.

Objective

The objective of the study was to investigate the potential risks of plasma metabolites in urolithiasis using a two-sample Mendelian randomization (MR) study.

Methods

A total of 1,400 metabolites were identified in the most comprehensive genome-wide association study (GWAS) of plasma metabolomics in a European population to date, and single-nucleotide polymorphisms (SNPs) were used as the instrumental variables for the plasma metabolites. The European GWAS data for urinary calculi included 482,123 case samples and 6,223 control samples (ebi-a-GCST90018935). The associations between the plasma metabolites and risk of urolithiasis were evaluated by inverse variance weighting (IVW) and supplemented by sensitivity analyses of the MR-Egger and MR-PRESSO tests.

Results

For the first time, we found a causal relationship between two plasma metabolites (p < 1.03 × 10-4) and urolithiasis (p < 0.05). The chemical 4-hydroxychlorothalonil, which is an intermediate product of the pesticide hydroxychlorothalonil, could promote urolithiasis (odds ratio (OR) = 1.12) as a risk factor. Moreover, 1-stearoyl-2-arachidonoyl-GPC, which is an important component of phospholipid metabolism in the human body, can inhibit urolithiasis (OR = 0.94).

Conclusions

Our results suggest that blood metabolites can be used as blood markers and drug targets in the prevention, diagnosis, and treatment of urolithiasis; furthermore, our results can provide a basis for policy makers to formulate prevention and treatment policies for urolithiasis.

OMICs Signatures Linking Persistent Organic Pollutants to Cardiovascular Disease in the Swedish Mammography Cohort

Cardiovascular disease (CVD) development may be linked to persistent organic pollutants (POPs), including organochlorine compounds (OCs) and perfluoroalkyl and polyfluoroalkyl substances (PFAS). To explore underlying mechanisms, we investigated metabolites, proteins, and genes linking POPs with CVD risk. We used data from a nested case-control study on myocardial infarction (MI) and stroke from the Swedish Mammography Cohort - Clinical (n = 657 subjects). OCs, PFAS, and multiomics (9511 liquid chromatography-mass spectrometry (LC-MS) metabolite features; 248 proteins; 8110 gene variants) were measured in baseline plasma. POP-related omics features were selected using random forest followed by Spearman correlation adjusted for confounders. From these, CVD-related omics features were selected using conditional logistic regression. Finally, 29 (for OCs) and 12 (for PFAS) unique features associated with POPs and CVD. One omics subpattern, driven by lipids and inflammatory proteins, associated with MI (OR = 2.03; 95% CI = 1.47; 2.79), OCs, age, and BMI, and correlated negatively with PFAS. Another subpattern, driven by carnitines, associated with stroke (OR = 1.55; 95% CI = 1.16; 2.09), OCs, and age, but not with PFAS. This may imply that OCs and PFAS associate with different omics patterns with opposite effects on CVD risk, but more research is needed to disentangle potential modifications by other factors.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.