Plasma lipid trajectories improve prediction of future Alzheimer's disease
Year: 2026
Type: article
Abstract: BackgroundEarly identification of individuals at elevated risk for Alzheimer's disease (AD) is critical for prevention. Blood-based biomarkers offer scalable alternatives to cerebrospinal fluid and imaging, but the prognostic value of longitudinal plasma lipid trajectories remains unclear.ObjectiveTo evaluate whether multi-year plasma lipid trajectories improve prediction of AD conversion beyond demographic, clinical, genetic, and neuropsychological measures.MethodsWe studied 1150 cognitively normal or mildly impaired Alzheimer's Disease Neuroimaging Initiative participants; 329 progressed to AD dementia over a mean follow-up of 2.3 years. Plasma lipidomics quantified 749 lipid species by high-resolution LC-MS. Trajectories were summarized using functional principal component analysis and related to time to conversion using covariate-adjusted Cox proportional hazards models. Predictive performance was assessed by concordance index and likelihood-ratio tests.ResultsCross-sectional lipid profiles modestly improved prediction beyond demographic and clinical covariates, and longitudinal lipid trajectories yielded small additional gains. Ether-linked triglycerides showed the strongest longitudinal associations with conversion, with TG(O-50:1) [NL-18:1] exhibiting the most robust signal. Neuropsychological measures provided substantially stronger discrimination, and lipid trajectories added limited value once cognitive information was included. Nevertheless, longitudinal lipid changes contributed consistent improvements in models without neuropsychological predictors, supporting their role as complementary blood-based markers in settings where standardized cognitive assessments are unavailable or impractical.ConclusionsPlasma lipid trajectories capture biologically relevant metabolic change associated with AD progression and provide complementary predictive information. Longitudinal lipidomic profiling may support early risk stratification and cohort enrichment when cognitive assessments are unavailable.
Source: Journal of Alzheimer s Disease
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Metabolic Networks and Pathways Predictive of Sex Differences in AD Risk and Responsiveness to Treatment, Integrative modeling and dynamic prediction of Alzheimer's disease, Gut Liver Brain Biochemical Axis in Alzheimer's Disease, Alzheimer's Gut Microbiome Project, Metabolic Signatures Underlying Vascular Risk Factors for Alzheimer-type Dementias, Integrative modeling and dynamic prediction of Alzheimer's disease, Alzheimer's Disease Neuroimaging Initiative, Metabolomic Signatures for Disease Sub-classification and Target Prioritization in AMP-AD, Metabolic Networks and Pathways in Alzheimer's Disease, G8063974084, Metabolic age to define influences of the lipidome on brain aging in Alzheimer's disease, Duke/UNC Alzheimer's Disease Research Center
OpenAlex ID: W7150874042