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The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression. Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and \"asymptomatic Alzheimer's disease\" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples. We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.

The development of plasma biomarkers could facilitate early detection, risk assessment and therapeutic monitoring in Alzheimer's disease (AD). Alterations in ceramides and sphingomyelins have been postulated to play a role in amyloidogensis and inflammatory stress related neuronal apoptosis; however few studies have conducted a comprehensive analysis of the sphingolipidome in AD plasma using analytical platforms with accuracy, sensitivity and reproducibility. We prospectively analyzed plasma from 26 AD patients (mean MMSE 21) and 26 cognitively normal controls in a non-targeted approach using multi-dimensional mass spectrometry-based shotgun lipidomics to determine the levels of over 800 molecular species of lipids. These data were then correlated with diagnosis, apolipoprotein E4 genotype and cognitive performance. Plasma levels of species of sphingolipids were significantly altered in AD. Of the 33 sphingomyelin species tested, 8 molecular species, particularly those containing long aliphatic chains such as 22 and 24 carbon atoms, were significantly lower (p<0.05) in AD compared to controls. Levels of 2 ceramide species (N16:0 and N21:0) were significantly higher in AD (p<0.05) with a similar, but weaker, trend for 5 other species. Ratios of ceramide to sphingomyelin species containing identical fatty acyl chains differed significantly between AD patients and controls. MMSE scores were correlated with altered mass levels of both N20:2 SM and OH-N25:0 ceramides (p<0.004) though lipid abnormalities were observed in mild and moderate AD. Within AD subjects, there were also genotype specific differences. In this prospective study, we used a sensitive multimodality platform to identify and characterize an essentially uniform but opposite pattern of disruption in sphingomyelin and ceramide mass levels in AD plasma. Given the role of brain sphingolipids in neuronal function, our findings provide new insights into the AD sphingolipidome and the potential use of metabolomic signatures as peripheral biomarkers.

The heterogeneity of Alzheimer’s disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.

Sex differences in Alzheimer’s disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer’s and followed up to 10 years in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aβ42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having “high” or “low” likelihood for MCI due to Alzheimer’s. Data were analyzed using mixed-effects models incorporating all follow-ups. The worsening from baseline in Alzheimer’s Disease Assessment Scale-Cognitive score (mean, SD) (9 ± 12) in subjects with high likelihood of MCI due to Alzheimer’s was markedly greater than that in subjects with low likelihood (1 ± 6, p  < 0.0001). Among MCI due to AD subjects, the mean worsening in cognitive score was significantly greater in females (11.58 ± 14) than in males (6.87 ± 11, p  = 0.006). Our findings highlight the need to further investigate these findings in other populations and develop sex specific timelines for Alzheimer’s disease progression.

Results of previous studies have shown associations between PET imaging of amyloid plaques and amyloid-β pathology measured at autopsy. However, these studies were small and not designed to prospectively measure sensitivity or specificity of amyloid PET imaging against a reference standard. We therefore prospectively compared the sensitivity and specificity of amyloid PET imaging with neuropathology at autopsy. This study was an extension of our previous imaging-to-autopsy study of participants recruited at 22 centres in the USA who had a life expectancy of less than 6 months at enrolment. Participants had autopsy within 2 years of PET imaging with florbetapir (18F). For one of the primary analyses, the interpretation of the florbetapir scans (majority interpretation of five nuclear medicine physicians, who classified each scan as amyloid positive or amyloid negative) was compared with amyloid pathology (assessed according to the Consortium to Establish a Registry for Alzheimer's Disease standards, and classed as amyloid positive for moderate or frequent plaques or amyloid negative for no or sparse plaques); correlation of the image analysis results with amyloid burden was tested as a coprimary endpoint. Correlation, sensitivity, and specificity analyses were also done in the subset of participants who had autopsy within 1 year of imaging as secondary endpoints. The study is registered with ClinicalTrials.gov, number NCT 01447719 (original study NCT 00857415). We included 59 participants (aged 47–103 years; cognitive status ranging from normal to advanced dementia). The sensitivity and specificity of florbetapir PET imaging for detection of moderate to frequent plaques were 92% (36 of 39; 95% CI 78–98) and 100% (20 of 20; 80–100%), respectively, in people who had autopsy within 2 years of PET imaging, and 96% (27 of 28; 80–100%) and 100% (18 of 18; 78–100%), respectively, for those who had autopsy within 1 year. Amyloid assessed semiquantitatively with florbetapir PET was correlated with the post-mortem amyloid burden in the participants who had an autopsy within 2 years (Spearman ρ=0·76; p<0·0001) and within 12 months between imaging and autopsy (0·79; p<0·0001). The results of this study validate the binary visual reading method approved in the USA for clinical use with florbetapir and suggest that florbetapir could be used to distinguish individuals with no or sparse amyloid plaques from those with moderate to frequent plaques. Additional research is needed to understand the prognostic implications of moderate to frequent plaque density. Avid Radiopharmaceuticals.

Abstract Introduction This study examined whether, among subjects with mild cognitive impairment (MCI), women progressed at faster rates than men. Methods We examine longitudinal rates of change from baseline in 398 MCI subjects (141 females and 257 males) in the Alzheimer's Disease Neuroimaging Initiative-1, followed for up to 8 years (mean, 4.1 ± 2.5 years) using mixed-effects models incorporating all follow-ups (mean, 8 ± 4 visits). Results Women progressed at faster rates than men on the Alzheimer's disease assessment scale-cognitive subscale (ADAS-Cog; P  = .001) and clinical dementia rating-sum of boxes (CDR-SB; P  = .003). Quadratic fit for change over time was significant for both ADAS-Cog ( P  = .001) and CDR-SB ( P  = .004), and the additional acceleration in women was 100% for ADAS-Cog and 143% for CDR-SB. The variability of change was greater in women. The gender effect was greater in apolipoprotein E ( APOE ) ε4 carriers. Discussion Women with MCI have greater longitudinal rates of cognitive and functional progression than men. Studies to confirm and uncover potential mechanisms appear to be warranted. Trial Registration ADNI ClinicalTrials.gov identifier: NCT00106899.

Topics discussed at the “ Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias ” workshop, held by Duke University and the Alzheimer's Association with support from the National Institute on Aging, are summarized.  Ways in which existing data resources paired with innovative applications of both novel and well‐known methodologies can be used to identify the effects of multi‐level societal, community, and individual determinants of race/ethnicity, sex, and geography‐related health disparities in Alzheimer's disease and related dementia are proposed.  Current literature on the population analyses of these health disparities is summarized with a focus on identifying existing gaps in knowledge, and ways to mitigate these gaps using data/method combinations are discussed at the workshop.  Substantive and methodological directions of future research capable of advancing health disparities research related to aging are formulated.

Previous studies evaluating the association between clinically diagnosed Alzheimer's disease (AD) and age-related macular degeneration (AMD) have generated conflicting results. This study is the first to assess whether AMD prevalence is higher in AD patients than non-AD controls by using histopathology to definitively diagnose AD. This was a retrospective case-control study utilizing diagnostic information extracted from autopsy reports of patients age 75 and above, including 115 with a neuropathological diagnosis of AD and 57 age-matched normal controls. The rate of AMD was not significantly higher in AD cases (53.0%) than in controls (59.6%) (z = 0.820, p = 0.794). AMD severity as determined by Sarks score was similar between AD patients and controls (χ2 = 2.96, p = 0.706). There was also no significant association between Braak stage of AD severity and AMD (χ2 = 4.55, p = 0.602). No significant effect of AD diagnosis or pathologic severity on AMD comorbidity was found, suggesting that any shared mechanisms between AMD and AD may be nondeterministic.

Impaired glucose uptake in the brain is an early presymptomatic manifestation of Alzheimer’s disease (AD), with symptom-free periods of varying duration that likely reflect individual differences in metabolic resilience. We propose a systemic “bioenergetic capacity”, the individual ability to maintain energy homeostasis under pathological conditions. Using fasting serum acylcarnitine profiles from the AD Neuroimaging Initiative as a blood-based readout for this capacity, we identified subgroups with distinct clinical and biomarker presentations of AD. Our data suggests that improving beta-oxidation efficiency can decelerate bioenergetic aging and disease progression. The estimated treatment effects of targeting the bioenergetic capacity were comparable to those of recently approved anti-amyloid therapies, particularly in individuals with specific mitochondrial genotypes linked to succinylcarnitine metabolism. Taken together, our findings provide evidence that therapeutically enhancing bioenergetic health may reduce the risk of symptomatic AD. Furthermore, monitoring the bioenergetic capacity via blood acylcarnitine measurements can be achieved using existing clinical assays. Bioenergetic capacity, the ability to maintain energy balance under stress, can be monitored using blood acylcarnitine profiles. Improving this capacity may slow Alzheimer’s disease progression.