Dataset Information

High-resolution metabolomic profiling of Alzheimer's disease in plasma.

ABSTRACT:

Background

Alzheimer's disease (AD) is a complex neurological disorder with contributions from genetic and environmental factors. High-resolution metabolomics (HRM) has the potential to identify novel endogenous and environmental factors involved in AD. Previous metabolomics studies have identified circulating metabolites linked to AD, but lack of replication and inconsistent diagnostic algorithms have hindered the generalizability of these findings. Here we applied HRM to identify plasma metabolic and environmental factors associated with AD in two study samples, with cerebrospinal fluid (CSF) biomarkers of AD incorporated to achieve high diagnostic accuracy.

Methods

Liquid chromatography-mass spectrometry (LC-MS)-based HRM was used to identify plasma and CSF metabolites associated with AD diagnosis and CSF AD biomarkers in two studies of prevalent AD (Study 1: 43 AD cases, 45 mild cognitive impairment [MCI] cases, 41 controls; Study 2: 50 AD cases, 18 controls). AD-associated metabolites were identified using a metabolome-wide association study (MWAS) framework.

Results

An MWAS meta-analysis identified three non-medication AD-associated metabolites in plasma, including elevated levels of glutamine and an unknown halogenated compound and lower levels of piperine, a dietary alkaloid. The non-medication metabolites were correlated with CSF AD biomarkers, and glutamine and the unknown halogenated compound were also detected in CSF. Furthermore, in Study 1, the unknown compound and piperine were altered in MCI patients in the same direction as AD dementia.

Conclusions

In plasma, AD was reproducibly associated with elevated levels of glutamine and a halogen-containing compound and reduced levels of piperine. These findings provide further evidence that exposures and behavior may modify AD risks.

SUBMITTER: Niedzwiecki MM

PROVIDER: S-EPMC6952314 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High-resolution metabolomic profiling of Alzheimer's disease in plasma.

Niedzwiecki Megan M MM Walker Douglas I DI Howell Jennifer Christina JC Watts Kelly D KD Jones Dean P DP Miller Gary W GW Hu William T WT

Annals of clinical and translational neurology 20191211 1

<h4>Background</h4>Alzheimer's disease (AD) is a complex neurological disorder with contributions from genetic and environmental factors. High-resolution metabolomics (HRM) has the potential to identify novel endogenous and environmental factors involved in AD. Previous metabolomics studies have identified circulating metabolites linked to AD, but lack of replication and inconsistent diagnostic algorithms have hindered the generalizability of these findings. Here we applied HRM to identify plasm ...[more]

PMID: 31828981

Similar Datasets

Project description:BackgroundProliferative diabetic retinopathy (PDR), a sight-threatening retinopathy, is the leading cause of irreversible blindness in adults. Despite strict control of systemic risk factors, a fraction of patients with diabetes develop PDR, suggesting the existence of other potential pathogenic factors underlying PDR. This study aimed to investigate the plasma metabotype of patients with PDR and to identify novel metabolite markers for PDR. Biomarkers identified from this study will provide scientific insight and new strategies for the early diagnosis and intervention of diabetic retinopathy.MethodsA total of 1024 patients with type 2 diabetes were screened. To match clinical parameters between case and control subjects, patients with PDR (PDR, n = 21) or those with a duration of diabetes of ≥10 years but without diabetic retinopathy (NDR, n = 21) were assigned to the present case-control study. Distinct metabolite profiles of serum were examined using liquid chromatography-mass spectrometry (LC-MS).ResultsThe distinct metabolites between PDR and NDR groups were significantly enriched in 9 KEGG pathways (P < 0.05, impact > 0.1), namely, alanine, aspartate and glutamate metabolism, caffeine metabolism, beta-alanine metabolism, purine metabolism, cysteine and methionine metabolism, sulfur metabolism, sphingosine metabolism, and arginine and proline metabolism. A total of 63 altered metabolites played important roles in these pathways. Finally, 4 metabolites were selected as candidate biomarkers for PDR, namely, fumaric acid, uridine, acetic acid, and cytidine. The area under the curve for these biomarkers were 0.96, 0.95, 1.0, and 0.95, respectively.ConclusionsThis study suggested that impairment in the metabolism of pyrimidines, arginine and proline were identified as metabolic dysregulation associated with PDR. And fumaric acid, uridine, acetic acid, and cytidine might be potential biomarkers for PDR. Fumaric acid was firstly reported as a novel metabolite marker with no prior reports of association with diabetes or diabetic retinopathy, which might provide insights into potential new pathogenic pathways for diabetic retinopathy.

Project description:BackgroundRheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA.MethodsUltra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups ("lower", DAS28-CRP ≤ 3.2; and "higher", DAS28-CRP > 3.2) and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups ("low-CRP", CRP ≤ 3.0 mg/L; "high-CRP", CRP > 3.0 mg/L) were investigated.ResultsWe identified 33 metabolites differentially abundant between the lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE] ± SD: 1.51 ± 1.77) compared to a GLM without feature selection (MAE ± SD: 2.02 ± 2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted and actual DAS28-CRP (with feature selection: Spearman's ρ = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearman's ρ = 0.18, 95% CI: [-0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with the CRP patient groups while controlling for potential confounders (P < 0.05).ConclusionsWe demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that reflect disease activity and inflammatory status in RA patients.

Dataset Information

High-resolution metabolomic profiling of Alzheimer's disease in plasma.

Background

Methods

Results

Conclusions

Publications

High-resolution metabolomic profiling of Alzheimer's disease in plasma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets