Domain-specific associations of metabolomic features with cognition and neural networks among cognitively normal adults
- 1Department of Neurology, Columbia University
- 2Department of Neurology, National and Kapodistrian University of Athens
Abstract
Background: Alterations in systemic metabolism may reflect biological processes relevant to brain function and cognitive aging, yet the extent to which circulating metabolites relate to both cognitive performance and underlying neural network activity remains unclear.
Methods: The current study included 112 cognitively normal participants of the Reference Abilities Neural Network study at Columbia University who had metabolomic, cognitive, and imaging data available at baseline. Cognition was measured using a neuropsychological battery including three tests for each of the four cognitive domains of memory, reasoning, processing speed, and language. Participants underwent fMRI while performing 12 tasks, 3 per domain, to derive domain-specific neural network activation pattern scores such that patterns correlated positively with behavioral performance. Plasma collected from participants were analyzed using an untargeted liquid chromatography coupled high-resolution mass spectrometry-metabolomics assay to generate metabolic profiles. Linear regression was used to explore associations between metabolite features and outcomes including cognition and neural network activation pattern scores.
Results: Participants had a mean age of 56.6 years (standard deviation (SD) = 15.6), 50.0% were female, 71.4% were white, and the mean education level was 15.8 years (SD = 2.2). Of the 4,286 metabolite features analyzed, 8 were significantly associated with neural network activation pattern scores for reasoning, but no other domains after false discovery rate (FDR) adjustment. In contrast, no metabolite features were significantly associated with any cognitive domains of memory, reasoning, processing speed or language after FDR adjustment. Exploratory analyses using nominal p -values revealed minimal overlap in metabolite features associated with domain-specific cognitive performance and neural network measures. Six metabolite features were associated with all neural network domains, whereas only one metabolite feature was associated with all cognitive domains. Pathway-level analyses did not demonstrate consistent enrichment across domains, but suggested involvement of amino acid metabolism, lipid metabolism, energy production, cofactor and vitamin metabolism, and nitrogen handling pathways.
Conclusion: Our findings suggest limited overlap in significant metabolite associations across both cognitive performance and neural network activity, which may reflect some degree of domain specificity. Neural network measures may capture aspects of underlying biological processes that are not fully reflected in cognitive test performance.
Related
- Clinical and neuroimaging features, and associations with early post-stroke cognition in patients with intracerebral hemorrhage in Taiwan
- Body mass index modifies symptom-specific metabolomic associations with depressive symptoms in the Estonian Biobank
- Yield of Low Serum Vitamin B12 Results in General Pediatric Outpatient Practice: Age-Specific Patterns and Associations with Testing Indications and Recorded Clinical Features
- Associations of Distinct Dietary Indices with Global and Domain-Specific Cognition and Age-Related Heterogeneity in Older Chinese Adults
- Neuropathologic features of cognitively defined Alzheimer's disease subgroups in the Adult Changes in Thought study
BibTeX
@article{vollmer2026domainspecific,
title = {{Domain-specific associations of metabolomic features with cognition and neural networks among cognitively normal adults}},
author = {Brandi L. Vollmer and Christian Habeck and Vrinda Kalia and Badri N. Vardarajan and Nikolaos Scarmeas and Gary W. Miller and Yaakov Stern and Yian Gu},
journal = {Frontiers in Neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnins.2026.1898605},
url = {https://www.frontiersin.org/articles/10.3389/fnins.2026.1898605/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Domain-specific associations of metabolomic features with cognition and neural networks among cognitively normal adults
AU - Brandi L. Vollmer
AU - Christian Habeck
AU - Vrinda Kalia
AU - Badri N. Vardarajan
AU - Nikolaos Scarmeas
AU - Gary W. Miller
AU - Yaakov Stern
AU - Yian Gu
PY - 2026
DA - 2026/10/20
JO - Frontiers in Neuroscience
DO - 10.3389/fnins.2026.1898605
UR - https://www.frontiersin.org/articles/10.3389/fnins.2026.1898605/full
AB - Background: Alterations in systemic metabolism may reflect biological processes relevant to brain function and cognitive aging, yet the extent to which circulating metabolites relate to both cognitive performance and underlying neural network activity remains unclear. Methods: The current study included 112 cognitively normal participants of the Reference Abilities Neural Network study at Columbia University who had metabolomic, cognitive, and imaging data available at baseline. Cognition was measured using a neuropsychological battery including three tests for each of the four cognitive domains of memory, reasoning, processing speed, and language. Participants underwent fMRI while performing 12 tasks, 3 per domain, to derive domain-specific neural network activation pattern scores such that patterns correlated positively with behavioral performance. Plasma collected from participants were analyzed using an untargeted liquid chromatography coupled high-resolution mass spectrometry-metabolomics assay to generate metabolic profiles. Linear regression was used to explore associations between metabolite features and outcomes including cognition and neural network activation pattern scores. Results: Participants had a mean age of 56.6 years (standard deviation (SD) = 15.6), 50.0% were female, 71.4% were white, and the mean education level was 15.8 years (SD = 2.2). Of the 4,286 metabolite features analyzed, 8 were significantly associated with neural network activation pattern scores for reasoning, but no other domains after false discovery rate (FDR) adjustment. In contrast, no metabolite features were significantly associated with any cognitive domains of memory, reasoning, processing speed or language after FDR adjustment. Exploratory analyses using nominal p -values revealed minimal overlap in metabolite features associated with domain-specific cognitive performance and neural network measures. Six metabolite features were associated with all neural network domains, whereas only one metabolite feature was associated with all cognitive domains. Pathway-level analyses did not demonstrate consistent enrichment across domains, but suggested involvement of amino acid metabolism, lipid metabolism, energy production, cofactor and vitamin metabolism, and nitrogen handling pathways. Conclusion: Our findings suggest limited overlap in significant metabolite associations across both cognitive performance and neural network activity, which may reflect some degree of domain specificity. Neural network measures may capture aspects of underlying biological processes that are not fully reflected in cognitive test performance.
SN - 1662-453X
SN - 1662-4548
ER -