Researchers used data from the community-based Reference Abilities Neural Network study at Columbia University. The analysis included adults aged 20 to 80 who were recruited mainly through random-market mailing in the greater New York area and were free of cognitive impairment, functional impairment and medical or psychiatric conditions that could affect cognition. Participants had a mean age of 56.6 years, with a standard deviation of 15.6 years; half were women, and 71.4% were white. Of 398 cohort members, 112 had the metabolomic, cognitive and functional magnetic resonance imaging data required for the study. These included participants were older than those excluded and had higher reasoning and language cognitive scores and a higher language neural-network score.

Each participant completed a neuropsychological battery containing three tests in each of four domains: episodic memory, reasoning, processing speed and language. Domain scores were derived with principal-axis factor analysis and standardized across the cohort, so that higher values represented better performance. The researchers also used functional MRI acquired while participants performed 12 in-scanner tasks, three for each domain. Memory tasks included logical memory, word-order recognition and paired associates; fluid reasoning included paper folding, matrix reasoning and letter sets; speed tasks included digit symbol, letter comparison and pattern comparison; and language tasks included synonyms, antonyms and picture naming.

The imaging analysis summarized each domain with a scaled subprofile modeling approach, which derived an activation pattern whose subject-level score best predicted performance in that domain. The resulting four network scores were designed to capture patterns of task-related brain activity associated with memory, reasoning, speed and language. In parallel, plasma samples were analyzed by untargeted liquid chromatography coupled to high-resolution mass spectrometry. Two chromatographic columns and positive or negative ionization produced technical replicates, which were processed for peak detection, alignment and batch correction. A feature was defined by its mass-to-charge ratio and retention time. Of 7,327 features detected with the hydrophilic interaction column and 6,806 detected with the C18 column, 4,286 were present in at least 70% of samples and entered the analysis.

Eight separate metabolome-wide association analyses tested the 4,286 features against four neural-network outcomes and four cognitive outcomes. Models for brain networks adjusted for age and sex, while models for cognitive performance also adjusted for education. Benjamini-Hochberg correction at a false discovery rate of 5% was the primary threshold. The analysis identified no metabolite features associated with any cognitive domain after correction, and none linked to the memory, speed or language network scores. The eight surviving associations all involved reasoning-network activation. Five were tentatively annotated: three as creatinine, one as a sphingomyelin, and one as a feature compatible with several phosphatidylcholines and phosphatidylethanolamines. Three creatinine features were inversely associated with the reasoning-network score, while the sphingomyelin and the multi-identity phospholipid feature had positive associations.

Because so few individual associations survived the primary correction, the researchers also conducted exploratory analyses using nominal p-values below 0.05. These analyses associated 1,199 metabolite features with at least one neural-network domain, including six features associated with all four networks. For cognitive performance, 568 features were associated with at least one domain, but only one feature was associated with all four; it was tentatively identified as a group of methylated inositol isomers that the assay could not distinguish. The broad cross-domain cognitive feature was interpreted cautiously because the analysis did not control the false-positive rate at the individual-feature level. The scale of these nominal findings therefore demonstrates how many statistical associations can appear in a high-dimensional metabolomic screen, rather than providing robust evidence of a biomarker.

Pathway analysis pointed toward several broad metabolic themes, including amino acid metabolism, lipid metabolism, energy production, cofactor and vitamin metabolism and nitrogen handling. After false-discovery-rate correction, arginine and proline metabolism was enriched for the memory network, while de novo fatty-acid biosynthesis and fatty-acid oxidation were enriched for the reasoning network. No cognitive-domain pathway remained significant after correction. In the exploratory pathway analysis, memory had the largest variety of enriched pathways and the greatest overlap between neural-network and cognitive results, including beta-alanine metabolism, butanoate metabolism, lysine metabolism, branched-chain amino-acid degradation and urea-cycle or amino-group metabolism. These pathway results describe statistical enrichment, not demonstrated mechanisms of brain change.

The limited correspondence between metabolites associated with brain-network scores and those associated with test performance was itself a central observation. Under the nominal threshold, the same-domain analyses yielded 12 overlapping features for memory, 22 for reasoning, 43 for processing speed and 73 for language. Even among the six features associated with all four neural networks, only two also met the cognitive threshold for speed and language, while two more were associated exclusively with language. The investigators noted that several possible explanations are plausible: blood oxygenation-dependent functional MRI signals may be more closely coupled to circulating metabolism than behavioral test scores; the in-scanner and outside-scanner tasks were not identical; and different aspects of a cognitive domain can be supported by partially distinct biological processes. The overlap data do not show that cognition and functional imaging are measuring unrelated systems.

The study’s main strengths are its simultaneous use of high-resolution metabolomics, standardized domain scores and domain-specific imaging patterns in cognitively normal adults across a broad age range. Untargeted metabolomics allowed the researchers to screen thousands of molecular features without restricting analysis to a preselected metabolic hypothesis. A sensitivity analysis that treated measurements below the detection limit as missing produced beta estimates correlated with those from the primary analysis at 0.66 to 0.83 across outcomes, indicating that handling low-intensity values did not entirely account for the results. Principal-component analysis also found no small set of components dominating the metabolomic data; the first three explained 13%, 8% and 5% of variance, with a gradual scree-plot decline.

Important limitations constrain the findings. The sample of 112 was small for testing thousands of metabolite features, reducing statistical power and the ability to detect modest associations. The selected sample differed from the wider cohort in age and several cognitive and imaging measures, raising concerns about selection bias and generalizability. Domain-level activation scores compress potentially important regional and subnetwork differences, while 31% of analyzed features could not be mapped to a putative metabolite and many of the remaining annotations were uncertain. Plasma compounds also may not closely represent metabolism within the central nervous system, and the models could not rule out residual confounding from diet, kidney function, medication use and other factors. The cross-sectional design cannot determine whether circulating metabolites precede neural changes, accompany them or reflect them.

Overall, the results offer a qualified picture of domain specificity rather than a set of clinically ready metabolic markers. Robust associations were confined to reasoning-network activation, whereas measured cognitive performance showed no metabolite associations after correction for multiple comparisons. The limited overlap across cognitive domains, neural networks and pathways suggests that blood metabolomic profiles may capture aspects of brain physiology that are more proximal, and more selective, than conventional test scores. At the same time, the small sample, exploratory nominal findings, uncertain metabolite identities and potential confounding mean that the eight surviving feature associations should be treated as hypotheses for replication. The authors call for larger, well-characterized longitudinal cohorts, along with finer analyses of individual functional subnetworks and brain regions, to determine whether these metabolic relationships are reproducible, domain-specific and relevant to neurodegeneration.

Subject of Research: Associations between plasma metabolomic features, domain-specific cognition and neural-network activation in cognitively normal adults

Article Title: Domain-specific associations of metabolomic features with cognition and neural networks among cognitively normal adults

Article References: Vollmer, B. L., Habeck, C., Kalia, V., Vardarajan, B. N., Scarmeas, N., Miller, G. W., Stern, Y., & Gu, Y. (2026). Domain-specific associations of metabolomic features with cognition and neural networks among cognitively normal adults. Frontiers in Neuroscience, 20, Article 1898605. https://doi.org/10.3389/fnins.2026.1898605

Image Credits: AI Generated

DOI: 10.3389/fnins.2026.1898605

Keywords: metabolomics, cognition, functional MRI, neural networks, plasma metabolites, reasoning, brain aging, cognitively normal adults, Domain-specific, associations, metabolomic, features