Plasma lipid trajectories improve prediction of future Alzheimer's disease.

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.

Duke Scholars

Author Sheng Luo Biostatistics & Bioinformatics, Division of Biostatistics