Abstract
| Original language | English |
|---|---|
| Article number | 83 |
| Number of pages | 6 |
| Journal | Genome Medicine |
| Volume | 2 |
| DOIs | |
| Publication status | Published - 2010 |
| MoE publication type | A1 Journal article-refereed |
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Systems medicine and the integration of bioinformatic tools for the diagnosis of Alzheimer's disease. / Orešič, Matej (Corresponding Author); Lötjönen, Jyrki; Soininen, Hilkka.
In: Genome Medicine, Vol. 2, 83, 2010.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Systems medicine and the integration of bioinformatic tools for the diagnosis of Alzheimer's disease
AU - Orešič, Matej
AU - Lötjönen, Jyrki
AU - Soininen, Hilkka
PY - 2010
Y1 - 2010
N2 - Because of the changes in demographic structure, the prevalence of Alzheimer's disease is expected to rise dramatically over the next decades. The progression of this degenerative and terminal disease is gradual, with the subclinical stage of illness believed to span several decades. Despite this, no therapy to prevent or cure Alzheimer's disease is currently available. Early disease detection is still important for delaying the onset of the disease with pharmacological treatment and/or lifestyle changes, assessing the efficacy of potential therapeutic agents, or monitoring disease progression more closely using medical imaging. Sensitive cerebrospinal-fluid-derived marker candidates exist, but given the invasiveness of sample collection their use in routine diagnostics may be limited. The pathogenesis of Alzheimer's disease is complex and poorly understood. There is thus a strong case for integrating information across multiple physiological levels, from molecular profiling (metabolomics, lipidomics, proteomics and transcriptomics) and brain imaging to cognitive assessments. To facilitate the integration of heterogeneous data, such as molecular and image data, sophisticated statistical approaches are needed to segment the image data and study their dependencies on molecular changes in the same individuals. Molecular profiling, combined with biophysical modeling of molecular assemblies associated with the disease, offer an opportunity to link the molecular pathway changes with cell- and tissue-level physiology and structure. Given that data acquired at different levels can carry complementary information about early Alzheimer's disease pathology, it is expected that their integration will improve early detection as well as our understanding of the disease.
AB - Because of the changes in demographic structure, the prevalence of Alzheimer's disease is expected to rise dramatically over the next decades. The progression of this degenerative and terminal disease is gradual, with the subclinical stage of illness believed to span several decades. Despite this, no therapy to prevent or cure Alzheimer's disease is currently available. Early disease detection is still important for delaying the onset of the disease with pharmacological treatment and/or lifestyle changes, assessing the efficacy of potential therapeutic agents, or monitoring disease progression more closely using medical imaging. Sensitive cerebrospinal-fluid-derived marker candidates exist, but given the invasiveness of sample collection their use in routine diagnostics may be limited. The pathogenesis of Alzheimer's disease is complex and poorly understood. There is thus a strong case for integrating information across multiple physiological levels, from molecular profiling (metabolomics, lipidomics, proteomics and transcriptomics) and brain imaging to cognitive assessments. To facilitate the integration of heterogeneous data, such as molecular and image data, sophisticated statistical approaches are needed to segment the image data and study their dependencies on molecular changes in the same individuals. Molecular profiling, combined with biophysical modeling of molecular assemblies associated with the disease, offer an opportunity to link the molecular pathway changes with cell- and tissue-level physiology and structure. Given that data acquired at different levels can carry complementary information about early Alzheimer's disease pathology, it is expected that their integration will improve early detection as well as our understanding of the disease.
U2 - 10.1186/gm204
DO - 10.1186/gm204
M3 - Article
VL - 2
JO - Genome Medicine
JF - Genome Medicine
SN - 1756-994X
M1 - 83
ER -