ArticleMetabolites2023
Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women.
Article in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 12 citations in OpenAlex.
- Determinants of the plasma metabolome: cross-sectional and longitudinal associations over six years in the NESDA cohort.EBioMedicine · 2026Article
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Manner of death prediction: A machine learning approach to classify suicide and non-suicide using blood metabolomics.Forensic science international. Synergy · 2025Article
- Recommendations for sample selection, collection and preparation for NMR-based metabolomics studies of blood.Metabolomics : Official journal of the Metabolomic Society · 2025Review
- Metabolomic heterogeneity of ageing with ethnic diversity: a step closer to healthy ageing.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Article
- Untargeted metabolomics reveal signatures of a healthy lifestyle.Scientific reports · 2024Article
- Plasma and serum metabolic analysis of healthy adults shows characteristic profiles by subjects' sex and age.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Harnessing Schistosoma-associated metabolite changes in the human host to identify biomarkers of infection and morbidity: Where are we and what should we do next?PLoS neglected tropical diseases · 2024Review
- Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- NMR-based metabolomics: Where are we now and where are we going?Progress in nuclear magnetic resonance spectroscopyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 9 institutions in 4 countries.
Funding
Abstract
Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women's Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2-0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.