Heart (British Cardiac Society)
September 22, 2026 · via Europe PMC
Plasma metabolomics uncovers key metabolites and enhances prediction of aortic stenosis: insights from prospective cohort and Mendelian randomisation studies.
Xiang H, Zhang Y, Zhang Y, +6 more, Qin X.
AI summary — for doctors, generated from the abstract only
A large study using advanced blood metabolite testing found that a panel of 12 specific metabolites, many related to a type of cholesterol particle, could meaningfully improve prediction of who will develop aortic valve narrowing beyond standard risk factors, with genetic analysis supporting that several of these metabolites directly contribute to causing the disease.
What was studied
A study combining prospective cohort analysis and genetic (Mendelian randomization) analysis examining 168 blood metabolites measured by nuclear magnetic resonance technology, assessing their association with incident aortic stenosis risk and related interventions/deaths, and testing whether adding a refined metabolite panel to standard clinical risk factors improved prediction, using training, testing, and external validation datasets.
Key findings
- Over a median 13.7-year follow-up, 2,562 aortic stenosis events and 1,039 related interventions or deaths occurred; 77 of 168 metabolites were significantly associated with aortic stenosis risk in observational analysis after adjustment and correction for multiple testing.
- Genetic analysis confirmed 39 metabolites, particularly apolipoprotein B and apolipoprotein B-containing lipoprotein particles, as likely causally associated with aortic stenosis risk, and all 39 were also associated with aortic-stenosis-related interventions or death.
- A refined 12-metabolite panel added to a standard clinical risk factor model significantly improved prediction accuracy, with the C-index (a measure of predictive accuracy) improving from 0.780 to 0.791 in the testing set and from 0.764 to 0.786 in an independent external validation cohort.
Clinical relevance
These findings suggest that measuring specific blood metabolites, particularly those related to apolipoprotein B-containing lipoproteins, could meaningfully improve prediction of future aortic valve narrowing beyond standard clinical risk factors, and the genetic evidence supporting a causal role suggests these lipoprotein pathways could become targets for future preventive therapies, similar to how LDL cholesterol is targeted in coronary artery disease.
Who should care
Limitations
This is an observational and genetic association study, which, despite the Mendelian randomization approach, cannot definitively prove causation with the same certainty as a randomized controlled trial testing an actual intervention; implementing this metabolite panel in routine practice would require further validation of its cost-effectiveness and clinical utility.
A specific panel of 12 blood metabolites, many related to a cholesterol-carrying particle, meaningfully improved prediction of who would develop a serious heart valve narrowing condition, and genetic evidence suggests these metabolites may directly cause the disease, pointing to a potential new treatment target.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Objective
To examine the association between nuclear magnetic resonance (NMR)-based metabolomics and aortic stenosis (AS) risk, and determine whether metabolomic profiling can enhance AS prediction beyond conventional clinical risk factors.
Methods
We included 168 metabolites in our study. The primary outcome of interest was incident AS. The secondary outcome of interest was incident AS-related interventions or deaths. Prospective and two-sample Mendelian randomisation (MR) analyses were used to investigate the relationship between metabolites and AS risk. Harrell's C index, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were employed to assess whether adding metabolites could improve risk discrimination and reclassification for AS prediction beyond clinical risk factors.
Results
During a median follow-up of 13.7 years, 2562 AS events, along with 1039 AS-related interventions or deaths, were recorded. In observational analyses, 77 of 168 metabolites were significantly associated with AS risk after adjusting for covariates and correcting for multiple testing. In MR analyses, 39 metabolites, including apolipoprotein B, apolipoprotein B-containing lipoprotein particles and lipid constituents within lipoprotein subclasses, remained causally associated with AS risk. Besides, all of the 39 metabolites were associated with incident AS-related interventions or deaths. Using LASSO (least absolute shrinkage and selection operator) regression, we identified 12 metabolites in the training set (n=146 252). Adding the 12 metabolites to clinical risk factors-based Cox regression model significantly improved AS risk prediction. The C index improved from 0.780 to 0.791 in the testing set (n=62 678) and from 0.764 to 0.786 in the external validation cohort (n=21 546). The NRI and IDI were also significantly improved.
Conclusions
Our study identified 39 metabolites with causal associations with AS risk, particularly apolipoprotein B-containing lipoproteins. The derived 12-metabolite panel significantly improved AS risk prediction, demonstrating the potential of NMR-based metabolomics to enhance AS risk stratification and targeted prevention.
DOI
10.1136/heartjnl-2025-326161
PMID
40759471
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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