Commentary
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive impairments that might be accompanied by declines in activities of daily living, neuropsychiatric disorders and a loss of motor function [1,2]. At present, the prevalence of dementia of Alzheimer’s type (DAT) is approximately 4% among elderly people [3]. With the increase in the aging the population, the number of individuals with DAT is still increasing. The world bears heavy medical costs and economic burdens of AD [4]. Although some medicines have been approved to be utilized for AD, such as acetylcholinesterase inhibitors (AChEIs) [5], these medicines are merely symptomatic treatments. At present, no disease-modifying drug can be used to reverse the onset of AD and prevent the progression of AD or the pathogenetic mechanisms of AD [6,7]. Accordingly, early identification and timely intervention before the stage of dementia are crucial [8,9]. Notably, a good diagnostic tool should be inexpensive, less invasive and brief to perform. Plasma markers are superior to other examinations due to their convenience and low price. For decades, researchers have been devoted to exploring plasma proteins in AD. Although some plasma proteins were found to be effective biomarkers for the early stage of AD, these markers varied in different studies and have not been widely replicated among different populations. This situation might be related to the heterogeneity of the sample population, such as the education levels of subjects. Yang et al. [10] explored plasma protein panels as potential biomarkers to identify mild cognitive impairment (MCI) in elderly Chinese individuals with different education backgrounds in a study entitled ‘Plasma Protein Panels for Mild Cognitive Impairment Among Elderly Chinese Individuals with Different Educational Backgrounds’ [10]. In Yang et al.’s study, plasma clusterin was included in the diagnostic model of MCI for the total sample, with a classification rate of 63.68%. However, the MCI diagnostic model for the illiterate group included cystatin C, plasminogen activator inhibitor-1, and apolipoprotein A-I, with an accuracy of 77.25%. Human serum biomarkers of MCI for the lower education group had an accuracy of 75.00%. Additionally, the MCI diagnostic biomarker panel composed of alpha-acid glycoprotein, soluble intercellular adhesion molecule-1 and pancreatic polypeptide had an accuracy of 83.60% for identifying MCI elderly adults with higher education [10].
These results indicated that the diagnostic efficiencies of models for mild cognitive impairment (MCI) based on different educational levels were superior to those of models for MCI without grouping by educational level (accuracy: 77.75% vs. 67.4%; sensitivity: 83.8% vs. 72.1%; specificity: 71.6% vs. 62.7%). Furthermore, the study was a significant attempt, which suggested that protein expression might vary for individuals with MCI with different levels of education [10].
Epidemiological investigations have demonstrated that education is an essential influencing factor for the onset and progression of AD [11,12]. For instance, the prevalence of DAT among elderly Chinese individuals with elementary school education or below is 2 times that among elderly Chinese individuals with middle school education or above (6% vs 3%) [13]. In Vlachos et al.’s study [14], the odds of MCI decreased by 6.3% with every additional year of education among the Greek elderly population. A study by Aderson et al. [15], which included 54162 subjects from the International Genomics of Alzheimer’s Project (IGAP) consortium, indicated that the risk of AD decreased by 37% with every 3.6 years of schooling [15]. Kim et al.’s study [16] established a model of the progression of AD according to education level among the Korean population. Kim et al.’s study showed that the lower education group (≤12 years) took less time to progress into amnestic MCI from SCI than the higher education group (>12 years). A 17% decreased risk of AD by each increase in education years was observed in an American community cohort study [17]. Afgin et al. [18] investigated the prevalence of MCI in an Arabic village, and the results showed that more years of schooling reduced the risk of MCI among men [18].
Cognitive reserve (CR) is a concept that reflects the capacity to withstand physiological or pathological factors that cause cognitive decline [19]. These factors include age-related changes, brain damage and neurodegenerative illnesses. Stronger CR is able to minimize the clinical manifestations of the pathology of AD by utilizing cognitive networks flexibly and efficiently [20]. Education level is an important indicator of CR. The theory of cognitive reserve (CR) suggests that education plays a protective role in cognitive function [21-24]. A higher education level could compensate for cognitive decline to some extent, especially in the early stage of cognitive decline. Accordingly, the different educational levels of people with DAT or MCI reflect that they might be in different stages of the progress of pathological alterations related to AD [25].
Previous studies have suggested that there are differences in the pathological alterations related to AD among individuals with different education levels, including global and regional brain volume, glucose metabolism or tau deposition in brain regions; the severity of white matter lesions (WMLs); and cerebrospinal fluid (CSF) Aβ concentrations. For example, Wada et al. [22] found a positive connection between education years and brain volume among individuals with MCI but not among people with DAT [22]. Nicolas et al.’s study showed higher glucose metabolism in the basal forebrain and hippocampus among individuals with MCI with higher education levels, which indicated that higher education levels could be beneficial to upregulate cholinergic activity in the early stage of AD [26]. Trombella et al. [27] found an opposite relationship between tau deposition and education duration in the left posterior cingulate for people with amnestic MCI [28]. Mortamais et al.’s study [28] showed that there were significant interaction effects between WMLs and education levels. Individuals with more years of education potentially tolerated WMLs [28]. Dumergier et al. [29] found opposite relationships between education duration and CSF Aβ levels among subjects with mild DAT, in which Aβ neuropathological lesions were inversely linked to CSF Aβ levels [29]. Mondragón et al. [30] found greater hippocampal volume among less educated individuals with MCI. It was concluded that people with less education might withstand less atrophic neuropathology [30]. Higher tau accumulation was observed among higher educated subjects with MCI or DAT in Yasuno et al.’s study [31]. All these studies suggested that people with higher educational backgrounds tend to have more resistance to severe pathological alterations related to cognitive decline. In other words, under the same degree of pathological alteration, people with more years of education are more likely to be clinically healthy or have less cognitive impairment [32,33]. Hence, there are inconsistences between the severity of cognitive decline and alterations in pathology. In addition, other factors influence the expression of plasma protein biomarkers related to AD, including the source of the sample (community cohort or clinical cohort), sex distributions, age distributions and the severity of disease. Among these factors, education is regarded as the most important factor that affects pathology related to AD [12]. In view of this point, the expression of plasma protein might be influenced by educational level like other pathologies related to AD. Yang et al.’s work was exactly based on this perspective.
Yang et al.’s work attempted to establish plasma biomarkers for MCI based on CR theory. It was found that the plasma protein biomarkers for identifying MCI were different for elderly Chinese individuals with different education levels [10]. This work indicated an effect of educational level on Alzheimer's disease-related biomarkers. Additionally, the limitation of the study was the relatively small sample of 135 subjects. It is necessary to verify these diagnostic models of MCI according to different educational levels in a larger population in the future.
Of course, the drawback of plasma proteins as disease biomarkers should be discussed. Concentrations of proteins in the peripheral blood tend to change as a result of the influence of many factors. These factors that mediate the expression of plasma proteins include nutritional conditions, inflammatory responses, stress reactions, material metabolism, growth and nervous system repair. More precise technique might improve these issues in the future.
Therefore, it is necessary to consider these factors in future studies regarding plasma proteins.
In summary, Yang et al.’s [10] work provided relatively novel insight into the exploration of plasma biomarkers for MCI based on educational background. Furthermore, inconsistencies between cognitive performance and pathological alterations among MCI individuals should be considered. A diagnostic model of plasma protein biomarkers of MCI based on education levels should be established.
Acknowledgments
This study was supported by The Social Development Key Projects in Jiangsu Province (BE2015615).
Conflict of Interest
The authors declare that they have no conflicts of interest.
References
2. Buchman AS, Bennett DA. Loss of motor function in preclinical Alzheimer’s disease. Expert Review of Neurotherapeutics. 2011 May 1;11(5):665-76.
3. Niu H, Álvarez-Álvarez I, Guillén-Grima F, Aguinaga-Ontoso I. Prevalence and incidence of Alzheimer's disease in Europe: A meta-analysis. Neurología (English Edition). 2017 Oct 1;32(8):523-32
4. Jia J, Wei C, Chen S, Li F, Tang Y, Qin W, et al. The cost of Alzheimer's disease in China and re‐estimation of costs worldwide. Alzheimer's & Dementia. 2018 Apr;14(4):483-91.
5. Galimberti D, Scarpini E. Disease-modifying treatments for Alzheimer’s disease. Therapeutic Advances in Neurological Disorders. 2011 Jul;4(4):203-16.
6. Mullard A. Anti-amyloid failures stack up as Alzheimer antibody flops. Nature Reviews. Drug Discovery. 2019 Apr 5.
7. Yiannopoulou KG, Papageorgiou SG. Current and Future Treatments in Alzheimer Disease: An Update. Journal of Central Nervous System Disease. 2020 Feb;12:1179573520907397.
8. Tangalos EG, Petersen RC. Mild cognitive impairment in geriatrics. Clinics in Geriatric Medicine. 2018 Nov 1;34(4):563-89.
9. Tong C, Huang C, Wu J, Xu M, Cao H. The prevalence and impact of undiagnosed mild cognitive impairment in elderly patients undergoing thoracic surgery: A prospective cohort study. Journal of Cardiothoracic and Vascular Anesthesia. 2020 Mar 19.
10. Yang H, Gu S, Wu Y, Jiang Y, Zhao J, Cheng Z. Plasma Protein Panels for Mild Cognitive Impairment Among Elderly Chinese Individuals with Different Educational Backgrounds. Journal of Molecular Neuroscience. 2020 Oct;70(10):1629-38.
11. Cadar D, Stephan BC, Jagger C, Sharma N, Dufouil C, den Elzen WP, et al. Is education a demographic dividend? The role of cognitive reserve in dementia-related cognitive decline: a comparison of six longitudinal studies of ageing. The Lancet. 2015 Nov 13;386:S25.
12. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer's disease: an analysis of population-based data. The Lancet Neurology. 2014 Aug 1;13(8):788-94.
13. Zhao X, Li X. The prevalence of Alzheimer’s disease in the Chinese Han population: a meta-analysis. Neurological Research. 2020 Apr 2;42(4):291-8.
14. Vlachos GS, Kosmidis MH, Yannakoulia M, Dardiotis E, Hadjigeorgiou G, Sakka P, et al. Prevalence of Mild Cognitive Impairment in the Elderly Population in Greece: Results From the HELIAD Study. Alzheimer Disease & Associated Disorders. 2020 Apr 1;34(2):156-62.
15. Anderson EL, Howe LD, Wade KH, Ben-Shlomo Y, Hill WD, Deary IJ, et al. Education, intelligence and Alzheimer’s disease: evidence from a multivariable two-sample Mendelian randomization study. International Journal of Epidemiology. 2020 Aug;49(4):1163-72.
16. Kim KW, Woo SY, Kim S, Jang H, Kim Y, Cho SH, et al. Disease progression modeling of Alzheimer’s disease according to education level. Scientific Reports. 2020 Oct 8;10(1):1-9.
17. Evans DA, Hebert LE, Beckett LA, Scherr PA, Albert MS, Chown MJ, et al. Education and other measures of socioeconomic status and risk of incident Alzheimer disease in a defined population of older persons. Archives of Neurology. 1997 Nov 1;54(11):1399-405.
18. Afgin AE, Massarwa M, Schechtman E, Israeli-Korn SD, Strugatsky R, Abuful A, et al. High prevalence of mild cognitive impairment and Alzheimer's disease in arabic villages in northern Israel: impact of gender and education. Journal of Alzheimer's Disease. 2012 Jan 1;29(2):431-9.
19. Stern Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc. 2002;8(3):448-460.
20. Stern Y. Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord. 2006;20(2):112-117.
21. Mubangizi V, Maling S, Obua C, Tsai AC. Prevalence and correlates of Alzheimer’s disease and related dementias in rural Uganda: cross-sectional, population-based study. BMC Geriatrics. 2020 Dec 1;20(1):48.
22. Wada M, Noda Y, Shinagawa S, Chung JK, Sawada K, Ogyu K, et al. Effect of education on Alzheimer’s disease-related neuroimaging biomarkers in healthy controls, and participants with mild cognitive impairment and Alzheimer’s disease: A cross-sectional study. Journal of Alzheimer's Disease. 2018 Jan 1;63(2):861-9.
23. Gonneaud J, Bedetti C, Binette AP, Benzinger TL, Morris JC, Bateman RJ, et al. Association of education with Aβ burden in preclinical familial and sporadic Alzheimer disease. Neurology. 2020 Sep 15;95(11):e1554-64.
24. Ding D, Zhao Q, Wu W, Xiao Z, Liang X, Luo J, et al. Prevalence and incidence of dementia in an older Chinese population over two decades: The role of education. Alzheimer's & Dementia. 2020 Sep 4.
25. Carapelle E, Mundi C, Cassano T, Avolio C. Interaction between Cognitive Reserve and Biomarkers in Alzheimer Disease. International Journal of Molecular Sciences. 2020 Jan;21(17):6279.
26. Nicolas B, Alessandra D, Daniela P, Osman R, Sara T, Valentina G, et al. Basal forebrain metabolism in Alzheimer's disease continuum: relationship with education. Neurobiology of Aging. 2020 Mar 1;87:70-7.
27. Trombella S, Frisoni GB, Garibotto VG. The impact of education on the association between tau deposits and cognition in Mild Cognitive Impairment. InEUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2017 Oct 1 (Vol. 44, pp. S250-S250). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.
28. Mortamais M, Portet F, Brickman AM, Provenzano FA, Muraskin J, Akbaraly TN, et al. Education modulates the impact of white matter lesions on the risk of mild cognitive impairment and dementia. The American Journal of Geriatric Psychiatry. 2014 Nov 1;22(11):1336-45.
29. Dumurgier J, Paquet C, Benisty S, Kiffel C, Lidy C, Mouton-Liger F, et al. Inverse association between CSF Aβ 42 levels and years of education in mild form of Alzheimer's disease: The cognitive reserve theory. Neurobiology of Disease. 2010 Nov 1;40(2):456-9.
30. Mondragón JD, Celada-Borja C, Barinagarrementeria-Aldatz F, Burgos-Jaramillo M, Barragán-Campos HM. Hippocampal volumetry as a biomarker for dementia in people with low education. Dementia and Geriatric Cognitive Disorders Extra. 2016;6(3):486-99.
31. Yasuno F, Minami H, Hattori H, Alzheimer's Disease Neuroimaging Initiative. Interaction effect of Alzheimer's disease pathology and education, occupation, and socioeconomic status as a proxy for cognitive reserve on cognitive performance: in vivo positron emission tomography study. Psychogeriatrics. 2020 Apr 14.
32. Dumurgier J, Paquet C, Benisty S, Kiffel C, Lidy C, Mouton-Liger F, et al. Inverse association between CSF Aβ 42 levels and years of education in mild form of Alzheimer's disease: The cognitive reserve theory. Neurobiology of Disease. 2010 Nov 1;40(2):456-9.
33. Chiu NT, Lee BF, Hsiao S, Pai MC. Educational level influences regional cerebral blood flow in patients with Alzheimer’s disease. Journal of Nuclear Medicine. 2004 Nov 1;45(11):1860-3.