Skip to main content

Advertisement

Log in

Three plasma metabolites in elderly patients differentiate mild cognitive impairment and Alzheimer’s disease: a pilot study

  • Short Communication
  • Published:
European Archives of Psychiatry and Clinical Neuroscience Aims and scope Submit manuscript

Abstract

The metabolomic profile of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) may suggest potential diagnostic biomarkers and provide information on the pathophysiology of dementia. Our aim was to quantify plasmatic metabolites of AD patients, MCI and controls. We investigated the metabolomic profile—using the AbsoluteIDQ®p180 assay—of 79 older adults with primary cognitive impairment (34 AD and 20 MCI) and 25 healthy elders (controls). A cluster analysis revealed that a combination C12-DC, C12 and PCaaC26:0 could differentiate the patients according to diagnostic. Future studies should combine metabolomic profiles with other biomarkers to identify diagnostic groups.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Abbreviations

PC:

Phosphatidylcholine

AC:

Acylcarnitines

PLA2 :

Phospholipase A2

LPC:

Lysophospholipids

AD:

Alzheimer’s disease

HC:

Healthy controls

DSM:

Diagnostic and statistical manual of mental disorders

CAMCOG:

Cambridge Cognitive Test

MMSE:

Mini-Mental State Examination

CART:

Classification and regression tree

PUFAs:

Polyunsaturated fatty acids

References

  1. Qiu C, De Ronchi D, Fratiglioni L (2007) The epidemiology of the dementias: an update. Curr Opin Psychiatry 20:380–385

    PubMed  Google Scholar 

  2. Ahmad W (2013) Overlapped metabolic and therapeutic links between Alzheimer and diabetes. Mol Neurobiol 47:399–424

    CAS  PubMed  Google Scholar 

  3. Blennow K (2005) CSF biomarkers for mild cognitive impairment. J Intern Med 256:224–234

    Google Scholar 

  4. Humpel C (2011) Identifying and validating biomarkers for diagnosing Alzheimer’s disease. Trends Biotechnol 29:26–32

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Jack CJ, Albert MS, Knopman DS et al (2011) Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:257–262

    PubMed  PubMed Central  Google Scholar 

  6. Arnerić SP, Batrla-Utermann R, Beckett L et al (2017) Cerebrospinal fluid biomarkers for Alzheimer’s disease: a view of the regulatory science qualification landscape from the coalition against major diseases CSF biomarker team. J Alzheimers Dis 55:19–35

    PubMed  Google Scholar 

  7. Wood PL (2014) Mass spectrometry strategies for clinical metabolomics and lipidomics in psychiatry, neurology, and neuro-oncology. Neuropsychopharmacology 39:24–33

    PubMed  Google Scholar 

  8. Zhang A, Sun H, Wang X (2013) Power of metabolomics in biomarker discovery and mining mechanisms of obesity. Obes Rev 14:344–349

    CAS  PubMed  Google Scholar 

  9. Kiehntopf M, Nin N, Bauer M (2013) Metabolism, metabolome, and metabolomics in intensive care: is it time to move beyond monitoring of glucose and lactate? Am J Respir Crit Care Med 187:906–907

    CAS  PubMed  Google Scholar 

  10. Costa AC, Joaquim HPG, Forlenza O et al (2017) Plasma lipids metabolism in mild cognitive impairment and Alzheimer’s disease. World J Biol Psychiatry 19:1–7

    Google Scholar 

  11. Talib LL, Hototian SR, Joaquim HP et al (2015) Increased iPLA2 activity and levels of phosphorylated GSK3B in platelets are associated with donepezil treatment in Alzheimer’s disease patients. Eur Arch Psychiatry Clin Neurosci 265:701–706

    CAS  PubMed  Google Scholar 

  12. Fonteh AN, Chiang J, Cipolla M et al (2013) Alterations in cerebrospinal fluid glycerophospholipids and phospholipase A2 activity in Alzheimer’s disease. J Lipid Res 54:2884–2897

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Smesny S, Stein S, Willhardt I et al (2008) Decreased phospholipase A2 activity in cerebrospinal fluid of patients with dementia. J Neural Transm 115:1173–1179

    CAS  PubMed  Google Scholar 

  14. Forlenza OV, Wacker P, Nunes PV et al (2005) Reduced phospholipid breakdown in Alzheimer’s brains: a 31P spectroscopy study. Psychopharmacology 180:359–365

    CAS  PubMed  Google Scholar 

  15. Gattaz WF, Talib LL, Schaeffer EL et al (2014) Low platelet iPLA2 activity predicts conversion from mild cognitive impairment to Alzheimer’s disease: a 4-year follow-up study. J Neural Transm 121:193–200

    CAS  PubMed  Google Scholar 

  16. Gattaz WF, Forlenza OV, Talib LL et al (2004) Platelet phospholipase A(2) activity in Alzheimer’s disease and mild cognitive impairment. J Neural Transm 111:591–601

    CAS  PubMed  Google Scholar 

  17. Gattaz WF, Maras A, Cairns et al (1995) Decreased phospholipase A2 activity in Alzheimer brains. Biol Psychiatry 37:13–17

    CAS  PubMed  Google Scholar 

  18. Gattaz WF, Cairns NJ, Levy R et al (1996) Decreased phospholipase A2 activity in the brain and in platelets of patients with Alzheimer’s disease. Eur Arch Psychiatry Clin Neurosci 246:129–131

    CAS  PubMed  Google Scholar 

  19. Forlenza OV, Radanovic M, Talib LL et al (2015) Cerebrospinal fluid biomarkers in Alzheimer’s disease: diagnostic accuracy and prediction of dementia. Alzheimers Dement 1:455–463

    Google Scholar 

  20. Forlenza OV, Diniz BS, Gattaz WF (2010) Diagnosis and biomarkers of predementia in Alzheimer’s disease. BMC Med 8:89

    PubMed  PubMed Central  Google Scholar 

  21. Hurtado MO, Kohler I, de Lange EC (2018) Next-generation biomarker discovery in Alzheimer’s disease using metabolomics-from animal to human studies. Bioanalysis 10:1525–1546

    CAS  PubMed  Google Scholar 

  22. Veiga S, Wahrheit J, Rodríguez-Martín A, Sonntag D (2018) Quantitative metabolomics in Alzheimer’s disease: technical considerations for improved reproducibility. Methods Mol Biol 1779:463–470

    CAS  PubMed  Google Scholar 

  23. Varma VR, Oommen AM, Varma S et al (2018) Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: a targeted metabolomics study. PLoS Med 15:1002482

    Google Scholar 

  24. de Leeuw FA, Peeters CFW, Kester MI et al (2017) Blood-based metabolic signatures in Alzheimer’s disease. Alzheimers Dement 8:196–207

    Google Scholar 

  25. McKhann G, Drachman D, Folstein M et al (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology 34:939–944

    CAS  PubMed  Google Scholar 

  26. Roth M, Tym E, Mountjoy CQ et al (1986) CAMDEX: a standardized instrument for the diagnosis of mental disorders in the elderly with special reference to early detection of dementia. Br J Psychiatry 149:698–709

    CAS  PubMed  Google Scholar 

  27. Folstein M, Folstein SE, McHugh PR (1975) “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiat Res 12:189–198

    CAS  PubMed  Google Scholar 

  28. Breiman L, Friedman JH, Olshen RA et al (1984) Classification and regression trees. Wadsworth, Belmont

    Google Scholar 

  29. Glenn AL (2009) Neuroendocrine markers of psychopathy. In: Ritsner MS (ed) The handbook of neuropsychiatric biomarkers, endophenotypes and genes, 3rd edn. Metabolic and peripheral biomarkers. Springer, New York, pp 59–71

    Google Scholar 

  30. Boksa P (2013) A way forward for research on biomarkers for psychiatric disorders. J Psychiatry Neurosci 38:75–77

    PubMed  PubMed Central  Google Scholar 

  31. Mufson EJ, Binder L, Counts SE et al (2012) Mild cognitive impairment: pathology and mechanisms. Acta Neuropathol 123:13–30

    CAS  PubMed  Google Scholar 

  32. Sperling RA, Aisen PS, Beckett LA et al (2011) Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:280–292

    PubMed  PubMed Central  Google Scholar 

  33. Fiandaca MS, Zhong X, Cheema AK et al (2015) Plasma 24-metabolite panel predicts preclinical transition to clinical stages of Alzheimer’s disease. Front Neurol 6:1–13

    Google Scholar 

  34. Mapstone M, Cheema AK, Fiandaca MS et al (2014) Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20:415–418

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Kennedy EP, Weiss SB (1956) The function of cytidine coenzymes in the biosynthesis of phospholipids. J Biol Chem 222:193–214

    CAS  PubMed  Google Scholar 

  36. Conquer JA, Tierney MC, Zecevic J et al (2000) Fatty acid analysis of blood plasma of patients with Alzheimer’s disease, other types of dementia and cognitive impairment. Lipids 35:1305–1312

    CAS  PubMed  Google Scholar 

  37. Mulder C, Wahlund LO, Teerlink T et al (2003) Decreased lysophosphatidylcholine/phosphatidylcholine ratio in cerebrospinal fluid in Alzheimer’s disease. J Neural Transm 110:949–955

    CAS  PubMed  Google Scholar 

  38. González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2014) Combination of metabolomic and phospholipid-profiling approaches for the study of Alzheimer’s disease. J Proteomics 104:37–47

    PubMed  Google Scholar 

  39. Jones LL, McDonald DA, Borum PR (2010) Acylcarnitines: role in brain. Prog Lipid Res 49:61–75

    CAS  PubMed  Google Scholar 

  40. Reuter SE, Evans AM (2012) Carninine and acylcarnitines: pharmacokinetic, pharmacological and clinical aspects. Clin Pharmacokinet 51:553–572

    CAS  PubMed  Google Scholar 

  41. Gongadze N, Antelava N, Kezeli T et al (2008) The mechanisms of neurodegenerative processes and current pharmacotherapy of Alzheimer’s disease. Georgian Med News 155:44–48

    Google Scholar 

  42. Naudí A, Cabré R, Jové M et al (2015) Lipidomics of human brain aging and Alzheimer’s disease pathology. Int Rev Neurobiol 122:133–189

    PubMed  Google Scholar 

  43. Ady CNAE, Lim SM, Teh LK et al (2017) Metabolomic-guided discovery of Alzheimer’s disease biomarkers from body fluid. J Neurosci Res 95:2005–2024

    Google Scholar 

  44. Schneider M, Levant B, Reichel M et al (2016) Lipids in psychiatric disorders and preventive medicine. Neurosci Biobehav Rev 76:336–362

    PubMed  Google Scholar 

Download references

Acknowledgements

We are thankful to Danilo Pereira of the Waters Corporation, who performed the assay analyses. This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo-FAPESP (Grants Nos 2014/20913-3 and 2013/103509), Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBioN-Grant No. 2014/50873-3). The Laboratory of Neuroscience receives financial support from Associação Beneficente Alzira Denise Hertzog da Silva (ABADHS).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leda L. Talib.

Ethics declarations

Conflict of interest

The authors have no conflict of interest to report.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Costa, A.C., Joaquim, H.P.G., Forlenza, O.V. et al. Three plasma metabolites in elderly patients differentiate mild cognitive impairment and Alzheimer’s disease: a pilot study. Eur Arch Psychiatry Clin Neurosci 270, 483–488 (2020). https://doi.org/10.1007/s00406-019-01034-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00406-019-01034-9

Keywords

Navigation