Association of Lipidomics Signatures in Blood with Clinical Progression in Preclinical and Prodromal Alzheimer's Disease.

BACKGROUND: Lipidomics may provide insight into biochemical processes driving Alzheimer's disease (AD) pathogenesis and ensuing clinical trajectories. OBJECTIVE: To identify a peripheral lipidomics signature associated with AD pathology and investigate its potential to predict clinical progression. METHODS: We used Bayesian elastic net regression to select plasma lipid classes associated with the CSF pTau/Aß42 ratio as a biomarker of AD pathology in preclinical and prodromal AD cases from the ADNI cohort. Consensus clustering of the selected lipid classes was used to identify lipidomic endophenotypes and study their association with clinical progression. RESULTS: In the APOE4-adjusted model, ether-glycerophospholipids, lyso-glycerophospholipids, free-fatty acids, cholesterol esters, and complex sphingolipids were found to be associated with the CSF pTau/Aß42 ratio. We found an optimal number of five lipidomic endophenotypes in the prodromal and preclinical cases, respectively. In the prodromal cases, these clusters differed with respect to the risk of clinical progression as measured by clinical dementia rating score conversion. CONCLUSION: Lipid alterations can be captured at the earliest phases of AD. A lipidomic signature in blood may provide a dynamic overview of an individual's metabolic status and may support identifying different risks of clinical progression.

A Robust Blood-based Signature of Cerebrospinal Fluid Aß42 Status.

Early detection of Alzheimer's disease (AD) is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid ß1-42 (Aß42) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aß42 status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aß42 robustly predict CSF Aß42 with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalize well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate enrichment of clinical trials and population-based screening.

In vivo staging of regional amyloid deposition predicts functional conversion in the preclinical and prodromal phases of Alzheimer's disease.

We tested the usefulness of a regional amyloid staging based on amyloid sensitive positron emission tomography to predict conversion to cognitive impairment and dementia in preclinical and prodromal Alzheimer's disease (AD). We analyzed 884 cases, including normal controls, and people with subjective cognitive decline or mild cognitive impairment (MCI), from the Alzheimer's Disease Neuroimaging Initiative with a maximum follow-up of 6 years and 318 cases with subjective memory complaints with a maximum follow-up time of three years from the INveStIGation of AlzHeimer's PredicTors cohort (INSIGHT-preAD study). Cox regression showed a significant association of regional amyloid stages with time to conversion from a cognitively normal to an MCI, and from an MCI to a dementia status. The most advanced amyloid stages identified very-high-risk groups of conversion. All results were robustly replicated across the independent samples. These findings indicate the usefulness of regional amyloid staging for identifying preclinical and prodromal AD cases at very high risk of conversion for future amyloid targeted trials.

Applicability of in vivo staging of regional amyloid burden in a cognitively normal cohort with subjective memory complaints: the INSIGHT-preAD study.

BACKGROUND: Current methods of amyloid PET interpretation based on the binary classification of global amyloid signal fail to identify early phases of amyloid deposition. A recent analysis of 18F-florbetapir PET data from the Alzheimer's disease Neuroimaging Initiative cohort suggested a hierarchical four-stage model of regional amyloid deposition that resembles neuropathologic estimates and can be used to stage an individual's amyloid burden in vivo. Here, we evaluated the validity of this in vivo amyloid staging model in an independent cohort of older people with subjective memory complaints (SMC). We further examined its potential association with subtle cognitive impairments in this population at elevated risk for Alzheimer's disease (AD). METHODS: The monocentric INSIGHT-preAD cohort includes 318 cognitively intact older individuals with SMC. All individuals underwent 18F-florbetapir PET scanning and extensive neuropsychological testing. We projected the regional amyloid uptake signal into the previously proposed hierarchical staging model of in vivo amyloid progression. We determined the adherence to this model across all cases and tested the association between increasing in vivo amyloid stage and cognitive performance using ANCOVA models. RESULTS: In total, 156 participants (49%) showed evidence of regional amyloid deposition, and all but 2 of these (99%) adhered to the hierarchical regional pattern implied by the in vivo amyloid progression model. According to a conventional binary classification based on global signal (SUVRCereb = 1.10), individuals in stages III and IV were classified as amyloid-positive (except one in stage III), but 99% of individuals in stage I and even 28% of individuals in stage II were classified as amyloid-negative. Neither in vivo amyloid stage nor conventional binary amyloid status was significantly associated with cognitive performance in this preclinical cohort. CONCLUSIONS: The proposed hierarchical staging scheme of PET-evidenced amyloid deposition generalizes well to data from an independent cohort of older people at elevated risk for AD. Future studies will determine the prognostic value of the staging approach for predicting longitudinal cognitive decline in older individuals at increased risk for AD.