The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer's disease and mild cognitive impairment

Systems healthcare: a holistic paradigm for tomorrow

Systems healthcare is a holistic approach to health premised on systems biology and medicine. The approach integrates data from molecules, cells, organs, the individual, families, communities, and the natural and man-made environment. Both extrinsic and intrinsic influences constantly challenge the biological networks associated with wellness. Such influences may dysregulate networks and allow pathobiology to evolve, resulting in early clinical presentation that requires astute assessment and timely intervention for successful mitigation. Herein, we describe the components of relevant biological systems and the nature of progression from at-risk to manifest disease. We illustrate the systems approach by examining two relevant clinical examples: Alzheimer's and cardiovascular diseases. The implications of systems healthcare management are examined through the lens of economics, ethics, policy and the law. Finally, we propose the need to develop new educational paradigms to enhance the training of the health professional in an era of systems medicine.

Bile acid quantification of 20 plasma metabolites identifies lithocholic acid as a putative biomarker in Alzheimer's disease

INTRODUCTION

There is still a clear need for a widely available, inexpensive and reliable method to diagnose Alzheimer's disease (AD) and monitor disease progression. Liquid chromatography-mass spectrometry (LC-MS) is a powerful analytic technique with a very high sensitivity and specificity.

OBJECTIVES

The aim of the present study is to measure concentrations of 20 bile acids using the novel Kit from Biocrates Life Sciences based on LC-MS technique.

METHODS

Twenty bile acid metabolites were quantitatively measured in plasma of 30 cognitively healthy subjects, 20 patients with mild cognitive impairment (MCI) and 30 patients suffering from AD.

RESULTS

Levels of lithocholic acid were significantly enhanced in plasma of AD patients (50 ± 6 nM, p = 0.004) compared to healthy controls (32 ± 3 nM). Lithocholic acid plasma levels of MCI patients (41 ± 4 nM) were not significantly different from healthy subjects or AD patients. Levels of glycochenodeoxycholic acid, glycodeoxycholic acid and glycolithocholic acid were significantly higher in AD patients compared to MCI patients (p < 0.05). All other cholic acid metabolites were not significantly different between healthy subjects, MCI patients and AD patients. ROC analysis shows an overall accuracy of about 66%. Discriminant analysis was used to classify patients and we found that 15/23 were correctly diagnosed. We further showed that LCA levels increased by about 3.2 fold when healthy subjects converted to AD patients within a 8-9 year follow up period. Pathway analysis linked these changes to a putative toxic cholesterol pathway.

CONCLUSION

In conclusion, 4 bile acids may be useful to diagnose AD in plasma samples despite limitations in diagnostic accuracy.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Metabolic network failures in Alzheimer's disease: A biochemical road map

INTRODUCTION

The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

METHODS

Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.

RESULTS

Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.

DISCUSSION

Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Neuroprotective Actions of Dietary Choline

Choline is an essential nutrient for humans. It is a precursor of membrane phospholipids (e.g., phosphatidylcholine (PC)), the neurotransmitter acetylcholine, and via betaine, the methyl group donor S-adenosylmethionine. High choline intake during gestation and early postnatal development in rat and mouse models improves cognitive function in adulthood, prevents age-related memory decline, and protects the brain from the neuropathological changes associated with Alzheimer’s disease (AD), and neurological damage associated with epilepsy, fetal alcohol syndrome, and inherited conditions such as Down and Rett syndromes. These effects of choline are correlated with modifications in histone and DNA methylation in brain, and with alterations in the expression of genes that encode proteins important for learning and memory processing, suggesting a possible epigenomic mechanism of action. Dietary choline intake in the adult may also influence cognitive function via an effect on PC containing eicosapentaenoic and docosahexaenoic acids; polyunsaturated species of PC whose levels are reduced in brains from AD patients, and is associated with higher memory performance, and resistance to cognitive decline.

Metabolomics and cognition in African American adults in midlife: the atherosclerosis risk in communities study

Clinical studies have shown alterations in metabolic profiles when patients with mild cognitive impairment and Alzheimer's disease dementia were compared to cognitively normal subjects. Associations between 204 serum metabolites measured at baseline (1987-1989) and cognitive change were investigated in 1035 middle-aged community-dwelling African American participants in the biracial Atherosclerosis Risk in Communities (ARIC) Study. Cognition was evaluated using the Delayed Word Recall Test (DWRT; verbal memory), the Digit Symbol Substitution Test (DSST; processing speed) and the Word Fluency Test (WFT; verbal fluency) at visits 2 (1990-1992) and 4 (1996-1998). In addition, Cox regression was used to analyze the metabolites as predictors of incident hospitalized dementia between baseline and 2011. There were 141 cases among 1534 participants over a median 17.1-year follow-up period. After adjustment for established risk factors, one standard deviation increase in N-acetyl-1-methylhistidine was significantly associated with greater 6-year change in DWRT scores (β=-0.66 words; P=3.65 × 10-4). Two metabolites (one unnamed and a long-chain omega-6 polyunsaturated fatty acid found in vegetable oils (docosapentaenoate (DPA, 22:5 n-6)) were significantly associated with less decline on the DSST (DPA: β=1.25 digit-symbol pairs, P=9.47 × 10-5). Two unnamed compounds and three sex steroid hormones were associated with an increased risk of dementia (all P<3.9 × 10-4). The association of 4-androstene-3beta, 17beta-diol disulfate 1 with dementia was replicated in European Americans. These results demonstrate that screening the metabolome in midlife can detect biologically plausible biomarkers that may improve risk stratification for cognitive impairment at older ages.

Dysregulation of lipids in Alzheimer's disease and their role as potential biomarkers

The brain is highly enriched in lipids, and an intensive study of these lipids may be informative, not only of normal brain function but also of changes with age and in disease. In recent years, the development of highly sensitive mass spectrometry platforms and other high-throughput technologies has enabled the discovery of complex changes in the entire lipidome. This lipidomics approach promises to be a particularly useful tool for identifying diagnostic biomarkers for early detection of age-related neurodegenerative disease, such as Alzheimer's disease (AD), which has till recently been limited to protein- and gene-centric approaches. This review highlights known lipid changes affecting the AD brain and presents an update on the progress of lipid biomarker research in AD. Important considerations for designing large-scale lipidomics experiments are discussed to help standardize findings across different laboratories, as well as challenges associated with moving toward clinical application.

Amino Acid Catabolism in Alzheimer's Disease Brain: Friend or Foe?

There is a dire need to discover new targets for Alzheimer's disease (AD) drug development. Decreased neuronal glucose metabolism that occurs in AD brain could play a central role in disease progression. Little is known about the compensatory neuronal changes that occur to attempt to maintain energy homeostasis. In this review using the PubMed literature database, we summarize evidence that amino acid oxidation can temporarily compensate for the decreased glucose metabolism, but eventually altered amino acid and amino acid catabolite levels likely lead to toxicities contributing to AD progression. Because amino acids are involved in so many cellular metabolic and signaling pathways, the effects of altered amino acid metabolism in AD brain are far-reaching. Possible pathological results from changes in the levels of several important amino acids are discussed. Urea cycle function may be induced in endothelial cells of AD patient brains, possibly to remove excess ammonia produced from increased amino acid catabolism. Studying AD from a metabolic perspective provides new insights into AD pathogenesis and may lead to the discovery of dietary metabolite supplements that can partially compensate for alterations of enzymatic function to delay AD or alleviate some of the suffering caused by the disease.

Prospective associations of plasma phospholipids and mild cognitive impairment/dementia among African Americans in the ARIC Neurocognitive Study

Introduction

The objective of this study was to investigate whether 10 phospholipids/metabolites previously identified as prospectively predictive of mild cognitive impairment (MCI) or dementia in whites would also be predictive in a mostly African-American cohort.

Methods

We repeatedly measured 188 phospholipids/metabolites in plasma samples of 221 participants of the Atherosclerosis Risk in Communities study, 97% African American, who were followed between 2004–2006 and 2011–2013.

Results

After a mean follow-up of 7.3 years, 77 were classified as having MCI and 18 as having dementia. Our study failed to replicate previous findings in this mostly African American cohort, in that the 10 phospholipids/metabolites only achieved a C statistic/AUC of 0.609 in predicting development of MCI or dementia (compared to 0.96) and 0.607 in distinguishing normal from MCI or dementia at the follow-up visit.

Conclusion

A panel of 10 phospholipids/metabolites previously associated with incident dementia was not predictive of MCI or dementia in an independent cohort.

Plasma phospholipids and prevalence of mild cognitive impairment and/or dementia in the ARIC Neurocognitive Study (ARIC-NCS)

Introduction

Phospholipids are altered in brains of patients with dementia and some studies suggest their plasma levels may be useful in the detection of mild cognitive impairment (MCI) and dementia.

Methods

We measured 188 plasma metabolites in participants who underwent a detailed neuropsychological assessment and classified as normal (n = 153), MCI (n = 145), or dementia (n = 143) by expert adjudication.

Results

Among 10 phospholipids recently implicated as altered in dementia, higher concentration of PC aa C36:6 was significantly associated with decreased prevalence of dementia (odds ratio = 0.71, 95% confidence interval = 0.50–1.00 per 1−SD increase). Adding these phospholipids to a model including multiple predictors of dementia led to only minimal improvement in detection (C statistic changed from 0.702 to 0.71).

Discussion

Some phospholipids and metabolites were altered in MCI and dementia but cross-sectional association was relatively weak and did not improve detection of MCI and dementia beyond information provided by clinical variables.

The Brain Metabolome of Male Rats across the Lifespan

Comprehensive and accurate characterization of brain metabolome is fundamental to brain science, but has been hindered by technical limitations. We profiled the brain metabolome in male Wistar rats at different ages (day 1 to week 111) using high-sensitivity and high-resolution mass spectrometry. Totally 380 metabolites were identified and 232 of them were quantitated. Compared with anatomical regions, age had a greater effect on variations in the brain metabolome. Lipids, fatty acids and amino acids accounted for the largest proportions of the brain metabolome, and their concentrations varied across the lifespan. The levels of polyunsaturated fatty acids were higher in infancy (week 1 to week 3) compared with later ages, and the ratio of omega-6 to omega-3 fatty acids increased in the aged brain (week 56 to week 111). Importantly, a panel of 20 bile acids were quantitatively measured, most of which have not previously been documented in the brain metabolome. This study extends the breadth of the mammalian brain metabolome as well as our knowledge of functional brain development, both of which are critically important to move the brain science forward.

Gut Microbiota and Nonalcoholic Fatty Liver Disease: Insights on Mechanism and Application of Metabolomics

Gut microbiota are intricately involved in the development of obesity-related metabolic diseases such as nonalcoholic fatty liver disease (NAFLD), type 2 diabetes, and insulin resistance. In the current review, we discuss the role of gut microbiota in the development of NAFLD by focusing on the mechanisms of gut microbiota-mediated host energy metabolism, insulin resistance, regulation of bile acids and choline metabolism, as well as gut microbiota-targeted therapy. We also discuss the application of a metabolomic approach to characterize gut microbial metabotypes in NAFLD.