A new pathway-level analysis links diet and physical activity to blood metabolic patterns associated with mobility, cognition, independence, and frailty across the cognitive aging spectrum.
Study: Lifestyle-associated blood metabolic pathways and functional performance in cognitive aging. Image Credit: Lightspring / Shutterstock
In a recent article in press in the journal Scientific Reports, researchers identified lifestyle-associated blood metabolic pathways with coordinated cross-domain patterns linked to functional performance across the cognitive aging spectrum.
Alzheimer’s disease (AD) is increasingly recognized as a systemic disorder, characterized by progressive cognitive decline as well as deterioration in resilience, function, and independence. Lifestyle factors, especially diet and physical activity, are associated with functional and cognitive outcomes in older individuals. Higher dietary quality is associated with lower functional decline and dementia risk.
Regular physical activity is associated with preserved muscle strength, mobility, and cognition. Yet, the metabolic pathways that may accompany diet quality, physical activity, and functional outcomes across cognitive stages are poorly understood. Nutrition and physical activity influence amino acid utilization, mitochondrial energy synthesis, oxidative stress, inflammation, and lipid oxidation, all of which are implicated in functional decline and neurodegeneration.
In addition, metabolic alterations in AD are often reported inconsistently, partly because metabolites are analyzed in isolation and metabolomics data are high-dimensional. Therefore, a pathway-level approach, which can aggregate metabolite signals into biologically annotated units, could reduce dimensionality, increase interpretability, and identify coordinated metabolic patterns associated with complex phenotypes like functional performance.
About the study
In the present study, researchers examined lifestyle-associated metabolic patterns linked to functional performance across the cognitive aging continuum. This study analyzed human phenotypic and metabolomic data from the AD Neuroimaging Initiative (ADNI), including metabolomics/lipidomics datasets generated by the AD Metabolomics Consortium (ADMC), the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP), and the Australian Imaging, Biomarkers, and Lifestyle Study of Aging (AIBL).
Cognitive status across datasets was harmonized and classified as cognitively normal (CN), mild cognitive impairment (MCI), or AD. Diet quality was assessed using a Food Frequency Questionnaire (FFQ), while physical activity was quantified using the International Physical Activity Questionnaire (IPAQ). Functional outcomes included gait speed, activities of daily living (ADL), grip strength, global cognition, composite function, and frailty.
Fasting blood metabolomics data were obtained from the datasets. Metabolites were mapped to curated metabolic pathways, and pathway activity scores were estimated by aggregating metabolite signals in each pathway. Metabolic pathways were categorized into five broad domains: amino acid metabolism, energy metabolism, microbiome-related metabolism, lipid metabolism, and oxidative stress and inflammation-related metabolism.
Linear regression models assessed associations between lifestyle exposures, functional outcomes, and pathway activity scores. Models were adjusted for body mass index (BMI), age, sex, education, hypertension, statin use, diabetes, and apolipoprotein E (APOE) ε4 carrier status. A lifestyle-modulated metabolic pathway score (LMPS) was derived via elastic net regression to summarize diet-quality-and physical-activity-associated pathway patterns jointly, with cross-validation, out-of-fold score estimation, and bootstrap stability assessment used to assess internal robustness.
Findings
The study included 360 participants: 150 with CN, 120 with MCI, and 90 with AD. Individuals with AD were older and had lower levels of education than CN individuals. The prevalence of APOE ε4 carriers increased from CN to MCI and AD groups. Dietary quality and physical activity declined progressively across the CN, MCI, and AD groups.
Moreover, LMPS differed across groups, with higher scores in CN individuals and lower scores in MCI and AD groups. Higher LMPS values were associated with better ADL, global cognition, composite function, and gait speed, as well as lower frailty; the associations were stronger in CN participants than in AD and MCI groups. Analyzing differences in pathway activity across cognitive groups revealed structured differences between CN and AD groups.
Pathways related to amino acid and energy metabolism exhibited modest shifts between the AD and CN groups, whereas lipid metabolism pathways demonstrated greater heterogeneity. Oxidative stress- and inflammation-related pathways showed greater dispersion and separation in the AD group, whereas microbiome-related pathways showed mixed patterns.
Multi-layer convergence mapping revealed that pathway-level signals were distributed across metabolic domains. Further, a clustered dot heatmap revealed several pathway clusters with aligned association patterns across lifestyle exposures and functional outcomes. Pathways in these clusters, positively associated with lifestyle exposures, were also positively associated with ADL, cognition, and gait speed and inversely associated with frailty. However, these visualizations were intended to summarize association patterns and prioritize hypotheses rather than independently validate biological mechanisms.
Conclusions
In sum, pathway-level metabolomics identified lifestyle-associated metabolic patterns linked to functional outcomes in cognitive aging. The lifestyle-metabolism-function relationships were organized across metabolic domains and were not confined to a single pathway class. LMPS was associated with cognitive, physical, and functional outcomes across cognitive groups, albeit further validation is needed to evaluate its reproducibility, relevance, and translational utility.
Because the study was cross-sectional and lacked an independent external validation cohort, LMPS should be interpreted as a hypothesis-generating research measure rather than a validated biomarker, mediator, or clinical tool. The untargeted metabolomics data also reflected relative signal intensities, rather than absolute metabolite concentrations.
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