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METABOLOMICS IN ALZHEIMER’S DISEASE

Zubair, Mohammed

[Thesis]. Manchester, UK: The University of Manchester; 2013.

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Abstract

Metabolites are a potentially useful source of detecting and identifying disease specific biomarkers. This thesis investigates the possibility of using metabolomics applications to detect Alzheimer’s disease associated metabolite peaks in patients and to detect longitudinal changes of the disease. Serum samples and clinical data were collected from 60 healthy controls and 60 Alzheimer’s disease patients (60 at baseline and 60 at 12 month follow-up). The metabolic fingerprinting of serum samples using the FT-IR lacked discriminatory power to discriminate Alzheimer’s disease and non-disease samples due to the similar magnitude of biological and analytical variation. The metabolic profiling of serum samples using the GC-ToF-MS did not reveal any significantly altered metabolite peaks between the Alzheimer’s disease and non-disease groups. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the positive ionisation mode did not reveal any significantly altered metabolite peaks between the disease and non-disease groups. Up to twelve metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the negative ionisation mode did not reveal any significantly altered metabolite peaks between Alzheimer’s disease and non-disease groups. Three metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples with the UPLC-LTQ/Orbitrap-MS may potentially be used to detect disease and disease progression associated metabolite peaks. The metabolite peaks require identification followed by a validation experiment.

Layman's Abstract

Metabolites are a potentially useful source of detecting and identifying disease specific biomarkers. This thesis investigates the possibility of using metabolomics applications to detect Alzheimer’s disease associated metabolite peaks in patients and to detect longitudinal changes of the disease. Serum samples and clinical data were collected from 60 healthy controls and 60 Alzheimer’s disease patients (60 at baseline and 60 at 12 month follow-up). The metabolic fingerprinting of serum samples using the FT-IR lacked discriminatory power to discriminate Alzheimer’s disease and non-disease samples due to the similar magnitude of biological and analytical variation. The metabolic profiling of serum samples using the GC-ToF-MS did not reveal any significantly altered metabolite peaks between the Alzheimer’s disease and non-disease groups. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the positive ionisation mode did not reveal any significantly altered metabolite peaks between the disease and non-disease groups. Up to twelve metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the negative ionisation mode did not reveal any significantly altered metabolite peaks between Alzheimer’s disease and non-disease groups. Three metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples with the UPLC-LTQ/Orbitrap-MS may potentially be used to detect disease and disease progression associated metabolite peaks. The metabolite peaks require identification followed by a validation experiment.

Keyword(s)

METABOLOMICS

Bibliographic metadata

Type of resource:
Content type:
Form of thesis:
Type of submission:
Degree programme:
PhD Medicine (Brain, Behaviour, Mental Health)
Publication date:
Location:
Manchester, UK
Total pages:
290
Abstract:
Metabolites are a potentially useful source of detecting and identifying disease specific biomarkers. This thesis investigates the possibility of using metabolomics applications to detect Alzheimer’s disease associated metabolite peaks in patients and to detect longitudinal changes of the disease. Serum samples and clinical data were collected from 60 healthy controls and 60 Alzheimer’s disease patients (60 at baseline and 60 at 12 month follow-up). The metabolic fingerprinting of serum samples using the FT-IR lacked discriminatory power to discriminate Alzheimer’s disease and non-disease samples due to the similar magnitude of biological and analytical variation. The metabolic profiling of serum samples using the GC-ToF-MS did not reveal any significantly altered metabolite peaks between the Alzheimer’s disease and non-disease groups. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the positive ionisation mode did not reveal any significantly altered metabolite peaks between the disease and non-disease groups. Up to twelve metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the negative ionisation mode did not reveal any significantly altered metabolite peaks between Alzheimer’s disease and non-disease groups. Three metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples with the UPLC-LTQ/Orbitrap-MS may potentially be used to detect disease and disease progression associated metabolite peaks. The metabolite peaks require identification followed by a validation experiment.
Layman's abstract:
Metabolites are a potentially useful source of detecting and identifying disease specific biomarkers. This thesis investigates the possibility of using metabolomics applications to detect Alzheimer’s disease associated metabolite peaks in patients and to detect longitudinal changes of the disease. Serum samples and clinical data were collected from 60 healthy controls and 60 Alzheimer’s disease patients (60 at baseline and 60 at 12 month follow-up). The metabolic fingerprinting of serum samples using the FT-IR lacked discriminatory power to discriminate Alzheimer’s disease and non-disease samples due to the similar magnitude of biological and analytical variation. The metabolic profiling of serum samples using the GC-ToF-MS did not reveal any significantly altered metabolite peaks between the Alzheimer’s disease and non-disease groups. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the positive ionisation mode did not reveal any significantly altered metabolite peaks between the disease and non-disease groups. Up to twelve metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples using the UPLC-LTQ/Orbitrap-MS operated in the negative ionisation mode did not reveal any significantly altered metabolite peaks between Alzheimer’s disease and non-disease groups. Three metabolite peaks were significantly altered in the Alzheimer’s disease baseline and follow-up samples, indicating a potential association with disease progression. Metabolic profiling of serum samples with the UPLC-LTQ/Orbitrap-MS may potentially be used to detect disease and disease progression associated metabolite peaks. The metabolite peaks require identification followed by a validation experiment.
Keyword(s):
Thesis main supervisor(s):
Thesis co-supervisor(s):
Thesis advisor(s):
Language:
en

Institutional metadata

University researcher(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:193483
Created by:
Zubair, Mohammed
Created:
29th April, 2013, 11:24:19
Last modified by:
Zubair, Mohammed
Last modified:
14th June, 2013, 12:43:30

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