The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Recent advances in the application of metabolomics to Alzheimer's Disease
Open accessAbstract
The pathophysiological changes associated with Alzheimer’s Disease (AD) begin decades before the emergence of clinical symptoms. Understanding the early mechanisms associated with AD pathology is, therefore, especially important for identifying disease-modifying therapeutic targets. While the majority of AD clinical trials to date have focused on anti-amyloid-beta (Aβ) treatments, other therapeutic approaches may be necessary. The ability to monitor changes in cellular networks that include both Aβ and non-Aβ pathways is essential to advance our understanding of the etiopathogenesis of AD and subsequent development of cognitive symptoms and dementia. Metabolomics is a powerful tool that detects perturbations in the metabolome, a pool of metabolites that reflects changes downstream of genomic, transcriptomic and proteomic fluctuations, and represents an accurate biochemical profile of the organism in health and disease. The application of metabolomics could help to identify biomarkers for early AD diagnosis, to discover novel therapeutic targets, and to monitor therapeutic response and disease progression. Moreover, given the considerable parallel between mouse and human metabolism, the use of metabolomics provides ready translation of animal research into human studies for accelerated drug design. In this review, we will summarize current progress in the application of metabolomics in both animal models and in humans to further understanding of the mechanisms involved in AD pathogenesis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123
Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., & Trojanowski, J. Q. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70299-6 · Cited by 4,806
Querfurth, H. W., & LaFerla, F. M. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0909142 · Cited by 4,569
Clinical and biomarker changes in dominantly inherited Alzheimer's disease
Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., Marcus, D. S., Cairns, N. J., Xie, X., Blazey, T. M., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1202753 · Cited by 3,814
Pathways towards and Away from Alzheimer’s Disease
Mattson, M. P. (2004) · Nature · doi:10.1038/nature02621 · Cited by 3,052
Alzheimer disease in the United States (2010–2050) estimated using the 2010 census
Hebert, L. E., Weuve, J., Scherr, P. A., & Evans, D. A. (2013) · Neurology · doi:10.1212/wnl.0b013e31828726f5 · Cited by 2,642
Alzheimer’s Disease: The Challenge of the Second Century
Holtzman, D. M., Morris, J. C., & Goate, A. M. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002369 · Cited by 1,424
Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer's dementia
Reiman, E. M., Chen, K., Alexander, G. E., Caselli, R. J., Bandy, D., Osborne, D., Saunders, A. M., & Hardy, J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2635903100 · Cited by 977
Donepezil and memantine for moderate-to-severe Alzheimer's disease
Howard, R., McShane, R., Lindesay, J., Ritchie, C., Baldwin, A., Barber, R., Burns, A., Dening, T., Findlay, D., Holmes, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1106668 · Cited by 782
Han, X., M. Holtzman, D., W. McKeel, D., Kelley, J., & Morris, J. C. (2002) · Journal of Neurochemistry · doi:10.1046/j.1471-4159.2002.00997.x · Cited by 626
Cited by
Oxidative Stress, Synaptic Dysfunction, and Alzheimer’s Disease
Tönnies, E., & Trushina, E. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161088 · Cited by 1,750
Self-citationPlasma phospholipids identify antecedent memory impairment in older adults
Mapstone, M., Cheema, A. K., Fiandaca, M. S., Zhong, X., Mhyre, T. R., MacArthur, L. H., Hall, W. J., Fisher, S. G., Peterson, D. R., Haley, J. M., et al. (2014) · Nature Medicine · doi:10.1038/nm.3466 · Cited by 1,043
Application of Metabolomics in Alzheimer’s Disease
Wilkins, J. M., & Trushina, E. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2017.00719 · Cited by 268
Self-citationMetabolomics of Human Brain Aging and Age-Related Neurodegenerative Diseases
Jové, M., Portero-Otín, M., Naudí, A., Ferrer, I., & Pamplona, R. (2014) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0000000000000091 · Cited by 195
Gaitán, J. M., Moon, H. Y., Stremlau, M., Dubal, D. B., Cook, D. B., Okonkwo, O. C., & van Praag, H. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.660181 · Cited by 160
Metadata
- Authors
- Trushina, E., & Mielke, M. M.
- Venue
- Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 1842
- Issue
- 8
- Pages
- 1232-1239
- ISSN
- 0925-4439 (ISSN-L)
- OpenAlex
- W1994243437
- Cited by
- 196
- References
- 16
- FWCI
- 9.59
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Recent advances in the application of metabolomics to Alzheimer's Disease. https://app.cochat.ai/papers/10.1016/j.bbadis.2013.06.014
- The paper
- Trushina, E., & Mielke, M. M. (2014). Recent advances in the application of metabolomics to Alzheimer's Disease. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. https://doi.org/10.1016/j.bbadis.2013.06.014
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.