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.

Symbiotic gut microorganisms (microbiome) interact closely with the mammalian host's metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography–mass spectrometry and short‐chain fatty acids in cecum by GC‐FID. Top‐down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro‐conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level. ### Synopsis The symbiotic gut microbiome acts as an extended genome ([Lederberg, 2000][1]) and has evolved to exert control on a number of important mammalian metabolic regulatory functions ([Backhed et al , 2004][2]; [Eckburg et al , 2005][3]; [Holmes and Nicholson, 2005][4]; [Nicholson et al , 2005][5]; [Martin et al , 2006][6]; [Nicholson, 2006][7]; [Turnbaugh et al , 2006][8]). Gut microbial composition is now known to vary significantly in obese animal ([Turnbaugh et al , 2006][8]) and human ([Ley et al , 2006][9]) populations, and recent studies have shown that the exact state of the gut microbial ecology and metabolic activities may be of fundamental importance in the control of calorific absorption ([Xu et al , 2003][10]; [Backhed et al , 2004][2]) and in the development of insulin resistance and non‐alcoholic fatty liver disease ([Dumas et al , 2006][11]). The integrated mammalian microbial co‐metabolism of the bile acid pool is a good example of the complex biochemical interactions between host and resident symbionts, as summarized in [Figure 1][12]. Bile acids are crucial for the absorption of dietary fats and lipid‐soluble vitamins in the intestine ([Staggers et al , 1982][13]) and might have an essential role in regulating obesity or type II diabetes ([Houten et al , 2006][14]; [Watanabe et al , 2006][15]). Many so‐called secondary bile acids (deoxycholic, lithocholic, hyodeoxycholic and ω‐muricholic acids) can be regarded as examples of mammalian–microbiotal co‐metabolism ([Nicholson et al , 2005][5]) and have different metabolic fates via enterohepatic recirculation ([Nicholson et al , 2004][16]). The aim of the study is to assess the effects of the induction of a non‐adapted microflora in a murine model (human baby flora (HBF)) on the host metabolism by comparison with animals colonized with a natural, conventional gut microflora, the result of a long period of co‐evolution. Here, we have integrated the effects of modulation of the microbiome in mouse models on the co‐metabolism of the major bile acids and the general metabolic profiles of the animals. We acquired parallel metabolic profiles of plasma, urine, liver, fecal extracts and ileal flushes using nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry to characterize a broad range of metabolites across these multiple compartments and 14 bile acids, which were also specifically measured in ileal flushes. The application of high‐resolution 1H NMR spectroscopy of biofluids and tissues coupled with multivariate statistical methods is a well‐established tool for differential metabolic pathway profiling ([Nicholson et al , 2002][17]; [Nicholson et al , 2005][5]; [Nicholson and Wilson, 1989][18]; [Wang et al , 2005][19]). Hyphenated chromatographic‐mass spectrometric methods such as ultra‐performance liquid chromatography–mass spectrometry (UPLC–MS)‐based profiling techniques have also been recently used effectively for functional genomic discrimination ([Plumb et al , 2005][20]; [Wilson et al , 2005][21]). We have now developed and enhanced bile acid analysis method using UPLC–MS, which gives superior chromatographic resolution and sensitivity ([Wilson et al , 2005][21]; [Plumb et al , 2006][22]). We show here a significant association of specific metabotypes obtained from urine, plasma, intact liver tissue and ileal flushes with changes of the gut microbiome in mice colonized naturally with a conventional microflora or with a defined population of microbes in a pathogen‐free environment, that is, exposing germ‐free mice to normal environment to be naturally re‐colonized by bacteria or fed with an HBF. Such exhaustive metabolic profiling revealed that re‐conventionalized mice, that is, those germ‐free mice allowed to equilibrate to a conventional flora, tend to converge metabolically and ecologically towards conventional mice and a healthy physiology. We also report that the re‐colonization of germ‐free mice with a non‐adapted microflora (HBF) modifies the physiology of the murine host towards a pre‐pathologic state (compared to conventional animals) and maintains the gut tract and the liver outside a sustainable mouse ecological equilibrium. Using a transgenomic bipartite graph model highlighting functional relationships between fecal bacteria profiles and bile acid profiles, we show that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize fats. The metabolic consequences for the host...

The metabolic basis of Alzheimer disease (AD) pathology and expression of AD symptoms is poorly understood. Omega-3 and -6 fatty acids have previously been linked to both protective and pathogenic effects in AD. However, to date little is known about how the abundance of these species is affected by differing levels of disease pathology in the brain. We performed metabolic profiling on brain tissue samples from 43 individuals ranging in age from 57 to 95 y old who were stratified into three groups: AD (N = 14), controls (N = 14) and “asymptomatic Alzheimer’s disease” (ASYMAD), i.e., individuals with significant AD neuropathology at death but without evidence for cognitive impairment during life (N = 15) from the autopsy sample of the Baltimore Longitudinal Study of Aging (BLSA). We measured 4,897 metabolite features in regions both vulnerable in the middle frontal and inferior temporal gyri (MFG and ITG) and resistant (cerebellum) to classical AD pathology. The levels of six unsaturated fatty acids (UFAs) in whole brain were compared in controls versus AD, and the differences were as follows: linoleic acid (p = 8.8 x 10⁻⁸, FC = 0.52, q = 1.03 x 10⁻⁶), linolenic acid (p = 2.5 x 10⁻⁴, FC = 0.84, q = 4.03 x 10⁻⁴), docosahexaenoic acid (p = 1.7 x 10⁻⁷, FC = 1.45, q = 1.24 x 10⁻⁶), eicosapentaenoic acid (p = 4.4 x 10⁻⁴, FC = 0.16, q = 6.48 x 10⁻⁴), oleic acid (p = 3.3 x 10⁻⁷, FC = 0.34, q = 1.46 x 10⁻⁶), and arachidonic acid (p = 2.98 x 10⁻⁵, FC = 0.75, q = 7.95 x 10⁻⁵). These fatty acids were strongly associated with AD when comparing the groups in the MFG and ITG, respectively: linoleic acid (p < 0.0001, p = 0.0006), linolenic acid (p < 0.0001, p = 0.002), docosahexaenoic acid (p < 0.0001, p = 0.0024), eicosapentaenoic acid (p = 0.0002, p = 0.0008), oleic acid (p < 0.0001, p = 0.0003), and arachidonic acid (p = 0.0001, p = 0.001). Significant associations were also observed between the abundance of these UFAs with neuritic plaque and neurofibrillary tangle burden as well as domain-specific cognitive performance assessed during life. Based on the regional pattern of differences in brain tissue levels of these metabolites, we propose that alterations in UFA metabolism represent both global metabolic perturbations in AD as well as those related to specific features of AD pathology. Within the middle frontal gyrus, decrements in linoleic acid, linolenic acid, and arachidonic acid (control>ASYMAD>AD) and increases in docosahexanoic acid (AD>ASYMAD>control) may represent regionally specific threshold levels of these metabolites beyond which the accumulation of AD pathology triggers the expression of clinical symptoms. The main limitation of this study is the relatively small sample size. There are few cohorts with extensive longitudinal cognitive assessments during life and detailed neuropathological assessments at death, such as the BLSA The findings of this study suggest that unsaturated fatty acid metabolism is significantly dysregulated in the brains of patients with varying degrees of Alzheimer pathology.