ArticleNeurology2026
Metabolomic Profiles of Inflammation Associated With Incident Ischemic Stroke Risk in Women.
Article in Neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
BACKGROUND AND
objectivesIschemic stroke (IS) accounts for 87% of all strokes and is a leading cause of disability worldwide. Women face higher lifetime IS risk and worse functional outcomes, yet predictive biomarkers remain limited. Moreover, inflammation is increasingly recognized as a contributor to IS pathogenesis, with inflammatory markers such as C-reactive protein (CRP) positively associated with IS. Yet, the metabolic pathways linking chronic inflammation to IS risk are poorly understood. We aimed to identify a metabolomic signature reflecting systemic inflammation and evaluate its association with incident IS in women.
methodsThis study used nested case-control designs within the Nurses' Health Study (NHS), a prospective cohort of US female registered nurses aged 30-55 at enrollment. Using elastic net regression in a derivation cohort with inflammatory biomarker (high-sensitive CRP, interleukin 6, tumor necrosis factor receptor 2, adiponectin) and metabolomic data, we developed a metabolomic signature index of inflammation (i-MSI). The i-MSI's association with incident IS was examined in an independent NHS nested case-control study using conditional logistic regression, adjusting for cardiovascular risk factors. Generalizability to atherosclerotic disease was evaluated in a coronary heart disease (CHD) nested case-control study from the Women's Health Initiative (WHI).
resultsThe derivation cohort included 1,699 women (mean age 58 years, 94% White). The i-MSI comprised 102 metabolites, with lysophosphatidylcholine species-promoters of endothelial activation, vascular inflammation, and plaque instability-contributing most significantly. In the independent IS case-control study (454 cases, 454 controls; mean age 66 years), women in the highest compared with lowest i-MSI quartile had a multivariable-adjusted odds ratio (OR) of 1.76 (95% CI 1.02-3.03) for IS, whereas each 1-SD increase in the i-MSI was associated with an OR of 1.35 (95% CI 1.09-1.67). In the WHI study (793 cases, 795 controls; mean age 67 years), each SD increase in the i-MSI was associated with an OR of 1.20 (95% CI 1.05-1.37) for CHD. DISCUSSION: An inflammatory metabolomic signature was associated with higher IS risk, independent of traditional cardiovascular disease risk factors, with consistent findings for CHD. Future studies should replicate these findings in other populations and evaluate whether these metabolites can improve risk stratification and serve as biomarkers for atherosclerotic cardiovascular diseases.
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.