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Cited by 7Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCancer-related cognitive impairment studies

Comparison of commonly measured plasma and cerebrospinal fluid proteins and their significance for the characterization of cognitive impairment status

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Rehman, Habbiburr, for the Alzheimer’s Disease Neuroimaging Initiative, Ang, Ting Fang Alvin, Tao, Qiushan, Espenilla, Arielle Lauren, Au, Rhoda, Farrer, Lindsay A., Zhang, Xiaoling +1 more·2024·Journal of Alzheimer's Disease
DOI 10.3233/jad-230837PMC PMC13147912
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Abstract

Background: Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer’s disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI). Objective: This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status. Methods: This cohort study included Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification. Results: We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOE ϵ4 genotype. Conclusions: This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

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Research works printed but not linked: 47

  1. [2]Alzheimer’s Association (2010) 2010 Alzheimer’s disease facts and figures. Alzheimers Dement 6, 158– 194. 20298981
  2. [3]Schneider P, Hampel H, Buerger K (2009) Biological marker candidates of Alzheimer’s disease in blood, plasma, and serum. CNS Neurosci Ther 15, 358– 374. 19840034 PMC6493996
  3. [4]Arnerić SP, Batrla-Utermann R, Beckett L, Bittner T, Blennow K, Carter L, Dean R, Engelborghs S, Genius J, Gordon MF (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. 27662307 PMC5115607
  4. [5]Rajmohan R, Reddy PH (2017) Amyloid-beta and phosphorylated tau accumulations cause abnormalities at synapses of Alzheimer’s disease neurons. J Alzheimers Dis 57, 975– 999. 27567878 PMC5793225
  5. [6]Scheltens P, Blennow K, Breteler MM, De Strooper B, Frisoni GB, Salloway S, Van der Flier WM (2016) Alzheimer’s disease. Lancet 388, 505– 517. 26921134
  6. [7]Blennow K, de Leon MJ, Zetterberg H (2006) Alzheimer’s disease. Lancet 368, 387– 403. 16876668
  7. [8]Khoury R, Ghossoub E (2019) Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review. Biomark Neuropsychiatry 1, 100005.
  8. [9]Blennow K, Hampel H, Weiner M, Zetterberg H (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6, 131– 144. 20157306
  9. [10]Bouwman FH, Frisoni GB, Johnson SC, Chen X, Engelborghs S, Ikeuchi T, Paquet C, Ritchie C, Bozeat S, Quevenco FC (2022) Clinical application of CSF biomarkers for Alzheimer’s disease: From rationale to ratios. Alzheimers Dement (Amst) 14, e12314. 35496374 PMC9044123
  10. [12]Grothe MJ, Moscoso A, Ashton NJ, Karikari TK, Lantero-Rodriguez J, Snellman A, Zetterberg H, Blennow K, Schöll M, Alzheimer’s Disease Neuroimaging Initiative (2021) Associations of fully automated CSF and novel plasma biomarkers with Alzheimer disease neuropathology at autopsy. Neurology 97, e1229– e1242. 34266917 PMC8480485
  11. [13]Schöll M, Maass A, Mattsson N, Ashton NJ, Blennow K, Zetterberg H, Jagust W (2019) Biomarkers for tau pathology. Mol Cell Neurosci 97, 18– 33. 30529601 PMC6584358
  12. [14]Rehiman SH, Lim SM, Neoh CF, Majeed ABA, Chin A-V, Tan MP, Kamaruzzaman SB, Ramasamy K (2020) Proteomics as a reliable approach for discovery of blood-based Alzheimer’s disease biomarkers: A systematic review and meta-analysis. Ageing Res Rev 60, 101066. 32294542
  13. [15]Cullen NC, Leuzy A, Janelidze S, Palmqvist S, Svenningsson AL, Stomrud E, Dage JL, Mattsson-Carlgren N, Hansson O (2021) Plasma biomarkers of Alzheimer’s disease improve prediction of cognitive decline in cognitively unimpaired elderly populations. Nat Commun 12, 3555. 34117234 PMC8196018
  14. [16]Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Alzheimer’s Disease Neuroimaging I, Dage JL, Stomrud E, Janelidze S, Mattsson-Carlgren N, Hansson O (2021) Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med 27, 1034– 1042. 34031605
  15. [18]Klyucherev TO, Olszewski P, Shalimova AA, Chubarev VN, Tarasov VV, Attwood MM, Syvänen S, Schiöth HB (2022) Advances in the development of new biomarkers for Alzheimer’s disease. Transl Neurodegener 11, 25. 35449079 PMC9027827
  16. [19]Britschgi M, Wyss-Coray T (2009) Blood protein signature for the early diagnosis of Alzheimer disease. Arch Neurol 66, 161– 165. 19064741
  17. [20]Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, Blennow K, Friedman LF, Galasko DR, Jutel M, Karydas A, Kaye JA, Leszek J, Miller BL, Minthon L, Quinn JF, Rabinovici GD, Robinson WH, Sabbagh MN, So YT, Sparks DL, Tabaton M, Tinklenberg J, Yesavage JA, Tibshirani R, Wyss-Coray T (2007) Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nat Med 13, 1359– 1362. 17934472
  18. [21]Kim Y, Kim J, Son M, Lee J, Yeo I, Choi KY, Kim H, Kim BC, Lee KH, Kim Y (2022) Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry. Sci Rep 12, 1282. 35075217 PMC8786819
  19. [22]Cheng Z, Yin J, Yuan H, Jin C, Zhang F, Wang Z, Liu X, Wu Y, Wang T, Xiao S (2018) Blood-derived plasma protein biomarkers for Alzheimer’s disease in Han Chinese. Front Aging Neurosci 10, 414. 30618720 PMC6305130
  20. [23]Lehallier B, Essioux L, Gayan J, Alexandridis R, Nikolcheva T, Wyss-Coray T, Britschgi M, Alzheimer’s Disease Neuroimaging Initiative (2016) Combined plasma and cerebrospinal fluid signature for the prediction of midterm progression from mild cognitive impairment to Alzheimer disease. JAMA Neurol 73, 203– 212. 26659895 PMC5214993
  21. [24]Tao Q, Ang TFA, Akhter-Khan SC, Itchapurapu IS, Killiany R, Zhang X, Budson AE, Turk KW, Goldstein L, Mez J (2021) Impact of C-reactive protein on cognition and Alzheimer disease biomarkers in homozygous APOE ϵ4 carriers. Neurology 97, e1243– e1252. 34266923 PMC8480484
  22. [25]Huang K, Lin Y, Yang L, Wang Y, Cai S, Pang L, Wu X, Huang L, Alzheimer’s Disease Neuroimaging Initiative (2020) A multipredictor model to predict the conversion of mild cognitive impairment to Alzheimer’s disease by using a predictive nomogram. Neuropsychopharmacology 45, 358– 366. 31634898 PMC6901533
  23. [26]Huang J, Tao Q, Ang TFA, Farrell J, Zhu C, Wang Y, Stein TD, Lunetta KL, Massaro J, Mez J (2022) The impact of increasing levels of blood C-reactive protein on the inflammatory loci SPI1 and CD33 in Alzheimer’s disease. Transl Psychiatry 12, 523. 36550123 PMC9780312
  24. [27]Abe K, Chiba Y, Hattori S, Tamazawa A, Yoshimi A, Katsuse O, Suda A, Alzheimer’s Disease Neuroimaging Imitative (2020) Influence of plasma cytokine levels on the conversion risk from MCI to dementia in the Alzheimer’s disease neuroimaging initiative database. J Neurol Sci 414, 116829. 32289574
  25. [28]Tubi MA, Kothapalli D, Hapenney M, Feingold FW, Mack WJ, King KS, Thompson PM, Braskie MN, for Alzheimer’s Disease Neuroimaging Initiative (2021) Regional relationships between CSF VEGF levels and Alzheimer’s disease brain biomarkers and cognition. Neurobiol Aging 105, 241– 251. 34126466 PMC8544907
  26. [29]Wirth M, Lange C, Huijbers W, Alzheimer’s Disease Neuroimaging Initiative (2019) Plasma cortisol is associated with cerebral hypometabolism across the Alzheimer’s disease spectrum. Neurobiol Aging 84, 80– 89. 31518952
  27. [30]Aisen PS, Petersen RC, Donohue MC, Gamst A, Raman R, Thomas RG, Walter S, Trojanowski JQ, Shaw LM, Beckett LA (2010) Clinical core of the Alzheimer’s Disease Neuroimaging Initiative: Progress and plans. Alzheimers Dement 6, 239– 246. 20451872 PMC2867843
  28. [32]Saykin AJ, Shen L, Foroud TM, Potkin SG, Swaminathan S, Kim S, Risacher SL, Nho K, Huentelman MJ, Craig DW (2010) Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers Dement 6, 265– 273. 20451875 PMC2868595
  29. [33]Heinze G, Wallisch C, Dunkler D (2018) Variable selection - A review and recommendations for the practicing statistician. Biom J 60, 431– 449. 29292533 PMC5969114
  30. [34]Utazirubanda JC, Leon T, Ngom P (2021) Variable selection with group LASSO approach: Application to Cox regression with frailty model. Commun Stat Simul Comput 50, 881– 901. 34248255 PMC8261624
  31. [35]Qi Y (2012) Random forest for bioinformatics. In Ensemble Machine Learning: Methods and Applications. Springer, pp. 307– 323.
  32. [36]Perkins NJ, Schisterman EF (2006) The inconsistency of “Optimal” cut-points using two ROC based criteria. Am J Epidemiol 163, 670– 675. 16410346 PMC1444894
  33. [37]Unal I (2017) Defining an optimal cut-point value in ROC analysis: An alternative approach. Comput Math Methods Med 2017, 3762651. 28642804 PMC5470053
  34. [39]McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R (2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 263– 269. 21514250 PMC3312024
  35. [41]Yarchoan M, Louneva N, Xie SX, Swenson FJ, Hu W, Soares H, Trojanowski JQ, Lee VM-Y, Kling MA, Shaw LM (2013) Association of plasma C-reactive protein levels with the diagnosis of Alzheimer’s disease. J Neurol Sci 333, 9– 12. 23978419 PMC3815534
  36. [42]Fernandes A, Tábuas-Pereira M, Duro D, Lima M, Gens H, Santiago B, Durães J, Almeida MR, Leitão MJ, Baldeiras I (2020) C-reactive protein as a predictor of mild cognitive impairment conversion into Alzheimer’s disease dementia. Exp Gerontol 138, 111004. 32561398
  37. [44]O’Bryant SE, Xiao G, Barber R, Reisch J, Doody R, Fairchild T, Adams P, Waring S, Diaz-Arrastia R, Texas Alzheimer’s Research Consortium (2010) A serum protein-based algorithm for the detection of Alzheimer disease. Arch Neurol 67, 1077– 1081. 20837851 PMC3069805
  38. [45]Reyer A, Schindler N, Ohde D, Walz C, Kunze M, Tuchscherer A, Wirthgen E, Brenmoehl J, Hoeflich A (2015) The RGD sequence present in IGFBP-2 is required for reduced glucose clearance after oral glucose administration in female transgenic mice. Am J Physiol Endocrinol Metab 309, E409– E417. 26105006
  39. [46]Williams T, Borchelt DR, Chakrabarty P (2020) Therapeutic approaches targeting Apolipoprotein E function in Alzheimer’s disease. Mol Neurodegener 15, 8. 32005122 PMC6995170
  40. [47]Huang Y, Mahley RW (2014) Apolipoprotein E: Structure and function in lipid metabolism, neurobiology, and Alzheimer’s diseases. Neurobiol Dis 72, 3– 12. 25173806 PMC4253862
  41. [48]Guo L-H, Alexopoulos P, Wagenpfeil S, Kurz A, Perneczky R, Alzheimer’s Disease Neuroimaging Initiative (2013) Plasma proteomics for the identification of Alzheimer’s disease. Alzheimer Dis Assoc Disord 27, 337– 342. 23314060 PMC3626738
  42. [49]Ahmed S, Ahmed A, Rådegran G (2021) Plasma tumour and metabolism related biomarkers AMBP, LPL and Glyoxalase I differentiate heart failure with preserved ejection fraction with pulmonary hypertension from pulmonary arterial hypertension. Int J Cardiol 345, 68– 76. 34710494
  43. [50]Higginbotham L, Ping L, Dammer EB, Duong DM, Zhou M, Gearing M, Hurst C, Glass JD, Factor SA, Johnson EC (2020) Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease. Sci Adv 6, eaaz9360. 33087358 PMC7577712
  44. [51]Delanghe JR, Speeckaert MM (2022) Host polymorphisms and COVID-19 infection. Adv Clin Chem 107, 41– 77. 35337605 PMC8381193
  45. [52]Muffat J, Walker DW (2010) Apolipoprotein D: An overview of its role in aging and age-related diseases. Cell Cycle 9, 269– 273. 20023409 PMC3691099
  46. [53]Fyfe-Desmarais G, Desmarais F, Rassart É, Mounier C (2023) Apolipoprotein D in oxidative stress and inflammation. Antioxidants 12, 1027. 37237893 PMC10215970
  47. [54]Khan W, Aguilar C, Kiddle SJ, Doyle O, Thambisetty M, Muehlboeck S, Sattlecker M, Newhouse S, Lovestone S, Dobson R (2015) A subset of cerebrospinal fluid proteins from a multi-analyte panel associated with brain atrophy, disease classification and prediction in Alzheimer’s disease. PLoS One 10, e0134368. 26284520 PMC4540455

Cited by

Sorted by citations

Authors
Rehman, H., Initiative, F. T. A. D. N., Ang, T. F. A., Tao, Q., Espenilla, A. L., Au, R., Farrer, L. A., Zhang, X., & Qiu, W. Q.
Affiliations
Boston University (US)
Corresponding author
  • Zhang, Xiaoling
  • Qiu, Wei Qiao
ORCID
Venue
Journal of Alzheimer's Disease
Publisher
SAGE Publications
Published
2024-01-16
Volume
97
Issue
2
Pages
621-633
ISSN
  • 1387-2877 (ISSN-L)
  • 1875-8908
DOI
10.3233/jad-230837
PMC
PMC13147912
OpenAlex
W4390103499
Cited by
7
References
7
Citations by year
2026: 2 · 2025: 1 · 2024: 4
FWCI
1.42
Type
journal article
Language
en
Open access
Locations
  • Journal of Alzheimer s Disease (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion)
Funders
  • National Institutes of Health (5u01ag024904-10)
  • National Institute on Aging (U01-AG-024904, RF1AG075832, R01AG048927, RF1AG057519, U19AG068753, U01AG072577)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
Keywords
mild cognitive impairment, Alzheimer's disease
MeSH terms
Alzheimer Disease, Blood Proteins, Cerebrospinal Fluid Proteins, Humans, Peptide Fragments, Cohort Studies, Biomarkers, Amyloid beta-Peptides, tau Proteins, Cognitive Dysfunction

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