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Toward a predictive model of Alzheimer's disease progression using capillary electrophoresis-mass spectrometry metabolomics
Ibáñez C, Simó C, Martín-Álvarez PJ, Kivipelto M, Winblad B, Cedazo-Mínguez A, Cifuentes A.. Analytical chemistry, 2012-09-25
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is the most prevalent form of dementia with an estimated worldwide prevalence of over 30 million people, and its incidence is expected to increase dramatically with an increasing elderly population. Up until now, cerebrospinal fluid (CSF) has been the preferred sample to investigate central nervous system (CNS) disorders since its composition is directly related to metabolite production in the brain. In this work, a nontargeted metabolomic approach based on capillary electrophoresis-mass spectrometry (CE-MS) is developed to examine metabolic differences in CSF samples from subjects with different cognitive status related to AD progression. To do this, CSF samples from 85 subjects were obtained from patients with (i) subjective cognitive impairment (SCI, i.e. control group), (ii) mild cognitive impairment (MCI) which remained stable after a follow-up period of 2 years, (iii) MCI which progressed to AD within a 2-year time after the initial MCI diagnostic and, (iv) diagnosed AD. A prediction model for AD progression using multivariate statistical analysis based on CE-MS metabolomics of CSF samples was obtained using 73 CSF samples. Using our model, we were able to correctly classify 97-100% of the samples in the diagnostic groups. The prediction power was confirmed in a blind small test set of 12 CSF samples, reaching a 83% of diagnostic accuracy. The obtained predictive values were higher than those reported with classical CSF AD biomarkers (Aβ42 and tau) but need to be confirmed in larger samples cohorts. Choline, dimethylarginine, arginine, valine, proline, serine, histidine, creatine, carnitine, and suberylglycine were identified as possible disease progression biomarkers. Our results suggest that CE-MS metabolomics of CSF samples can be a useful tool to predict AD progression.
Key findings
- Our results suggest that CE-MS metabolomics of CSF samples can be a useful tool to predict AD progression.
Why it matters
This study used a metabolomics method on cerebrospinal fluid to try to predict whether people with mild cognitive impairment would progress to Alzheimer's disease. It correctly classified most samples in the main analysis and reached 83% accuracy in a small blind test set, but the authors describe this as a potentially useful tool rather than a proven test.
Study details
- Participants85
- Evidence strengthLow
How this study scored 47/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Analytical chemistry) |
| Journal's citation record | 3 / 4 | Analytical Chemistry: journal h-index 483 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2012 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 85 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 170 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.6 citations a year over 16 year(s) since 2012 |
| Impact for its field and year | 3 / 6 | Cited 5.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 21 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Alejandro Cifuentes: h-index 82 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Karolinska Institutet: institution h-index 1,477 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 22967182) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Ibáñez, C., Simó, C., Martín-Álvarez, P. J., Kivipelto, M., Winblad, B., Cedazo-Mínguez, A., et al. (2012). Toward a predictive model of Alzheimer's disease progression using capillary electrophoresis-mass spectrometry metabolomics. Analytical chemistry. https://doi.org/10.1021/ac301243k
Vancouver
Ibáñez C, Simó C, Martín-Álvarez PJ, Kivipelto M, Winblad B, Cedazo-Mínguez A, et al. Toward a predictive model of Alzheimer's disease progression using capillary electrophoresis-mass spectrometry metabolomics. Analytical chemistry. 2012. doi:10.1021/ac301243k. PMID: 22967182.
BibTeX
@article{ibanez2012-22967182,
author = {Ibáñez, C. and Simó, C. and Martín-Álvarez, P. J. and Kivipelto, M. and Winblad, B. and Cedazo-Mínguez, A. and Cifuentes, A.},
title = {{Toward a predictive model of Alzheimer's disease progression using capillary electrophoresis-mass spectrometry metabolomics}},
journal = {Analytical chemistry},
year = {2012},
doi = {10.1021/ac301243k},
pmid = {22967182},
url = {https://doi.org/10.1021/ac301243k}
}
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