Jiang Y, Zhou X, Ip FC, Chan P, Chen Y, Lai NC, Cheung K, Lo RM, Tong EP, Wong BW, Chan AL, Mok VC, Kwok TC, Mok KY, Hardy J, Zetterberg H, Fu AK, Ip NY. Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging. Alzheimers Dement. 2021 May 25; PubMed.

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  1. The authors have done a thorough job in identifying 19 candidate AD-associated proteins. A strength of the study is the comparison with the plasma biomarkers Aβ42/40, p-tau181, and NfL. However, the lack of biomarker-verified AD participants and only a comparison between clinical AD versus controls, without knowledge about their amyloid status or other dementias, make it difficult to interpret if this protein panel is specific to AD, or if it reflects more general alterations seen in cognitive impairment.

    The AUCs of 0.97-0.98 for differentiating clinical AD from controls are impressive, but maybe slightly overoptimistic, and warrant further validation in biomarker-verified AD versus other neurodegenerative diseases and in AD at different disease stages.

    Previous studies on complex biomarker panels for identifying AD have been notoriously difficult to replicate by other research groups, and there are, to my knowledge, no such protein panels used in clinical practice or in clinical trials. Future replication studies will tell if this panel of 19 proteins will be different.

    View all comments by Sebastian Palmqvist

  2. AD pathology can now be assessed in plasma with assays for Aβ42/40, total-tau, and p-tau. Jiang et al. studied a broader panel of plasma proteins, not including these key AD markers, as a predictor for a clinical diagnosis of AD relative to cognitively normal individuals. Of the 1,160 proteins, 426 differed between groups, from which 19 proteins were selected. This panel showed an extremely high predictive accuracy for clinical AD relative to controls, with an area under the curve (AUC) of 0.97. For comparison, the AUC of a clinical diagnosis of AD dementia relative to controls is for amyloid PET around 0.87, for CSF Aβ42 0.89, and for CSF p-tau 0.88 (Mattsson et al., 2014; Jung et al., 2020). One possible explanation for this high accuracy is overfitting or “double dipping” if feature selection and classification performance are determined within the same sample (Kriegeskorte et al., 2010; Kriegeskorte et al., 2009). 

    Overfitting can be tested by replication in an independent cohort, which the authors did in a cohort of 97 individuals, of whom 33 had clinical AD. However, it is not clear from the paper whether the original discovery fit was used to predict AD in new, unseen individuals, or if the same group of 19 proteins was refitted again in the validation cohort. If the discovery fit was used, it is remarkable that it performed with such a high accuracy (AUC=0.97) since in the replication cohort 13 of the 19 proteins did not differ between AD and controls, and proteins that did replicate showed smaller effect sizes for four proteins.

    The authors suggest that the profile can identify two disease stages, in addition to the control group. Still the accuracy of the 19-panel for prediction of clinical AD relative to controls, looks very similar across the range of MOCA scores with AUCs between 0.96-1 (supplementary figure 5).

    A number of validation studies would be needed in order to further understand the clinical utility of this panel. It would be great to see whether the panel can predict CSF or PET brain amyloid abnormalities in cognitively normal individuals as a proxy of preclinical AD, in individuals with MCI as a proxy of prodromal AD, and in individuals with dementia, in order to test discriminative value between AD and other dementias such as frontal temporal lobe dementia and Lewy body dementia.

    References:

    Mattsson N, Insel PS, Landau S, Jagust W, Donohue M, Shaw LM, Trojanowski JQ, Zetterberg H, Blennow K, Weiner M, Alzheimer's Disease Neuroimaging Initiativea. Diagnostic accuracy of CSF Ab42 and florbetapir PET for Alzheimer's disease. Ann Clin Transl Neurol. 2014 Aug;1(8):534-43. Epub 2014 Jul 19 PubMed.

    Jung NY, Kim ES, Kim HS, Jeon S, Lee MJ, Pak K, Lee JH, Lee YM, Lee K, Shin JH, Ko JK, Lee JM, Yoon JA, Hwang C, Choi KU, Lee EC, Seong JK, Huh GY, Kim DS, Kim EJ. Comparison of Diagnostic Performances Between Cerebrospinal Fluid Biomarkers and Amyloid PET in a Clinical Setting. J Alzheimers Dis. 2020;74(2):473-490. PubMed.

    Kriegeskorte N, Lindquist MA, Nichols TE, Poldrack RA, Vul E. Everything you never wanted to know about circular analysis, but were afraid to ask. J Cereb Blood Flow Metab. 2010 Sep;30(9):1551-7. Epub 2010 Jun 23 PubMed.

    Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI. Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci. 2009 May;12(5):535-40. PubMed.

    View all comments by Pieter Jelle Visser

  3. This is a nice paper that looks into a very important topic. Blood-based biomarkers have significant advantages over CSF and imaging modalities in screening stages of AD, and can potentially be used to determine which patients require those confirmatory diagnostic procedures. The study is early discovery work conducted with small samples, so overfitting is a significant concern. This work will need to be cross-validated independently.

    The association of the hubs with disease status is not surprising and is consistent with a lot of prior work, though the specific markers and protein panel may or may not validate in the long run. The study used clinical diagnosis instead of confirmatory diagnostic methods (PET or CSF) and MoCA scores rather than full cognitive testing, so it is unknown how many of the patients actually had amyloid (A), tau (T), or neurodegenerative (N) pathology. Despite great progress in the field, plasma markers associated with these pathways are not proxies for cerebral levels. That means that the protein classification is of clinical dementia, not “Alzheimer’s disease” according to the ATN framework.

    Regardless of the limitations, this is a very nice first-step study that should be followed up in larger, independent samples. In future work, it would be helpful for the group to specifically define exactly what the context of use is for the intended “biomarker,” which is not defined here. That will enable others to clearly examine the findings within the intended use. 

    View all comments by Sid O'Bryant

  4. This is an interesting and important study that used the Olink PEA proteomic technology to investigate plasma AD biomarkers in discovery and replication cohorts totaling nearly 300 Chinese subjects, almost half of whom had AD. One of the many nice aspects of the study is that the authors used co-expression analysis to cluster the proteins found to be differentially abundant in AD into 19 modules, and then created a biomarker panel consisting of the strongest hub protein from each module.

    This panel represents all AD-related pathological changes that can be observed through measurement of the approximately 1,200 plasma proteins in this cohort. As the authors note, some of the modules and hub proteins likely represent central or peripheral pathophysiologies that may be stage-specific. A number of important questions arise from the results, including whether the observed changes are linked to brain-specific pathology, how they may change longitudinally in a given patient, whether certain AD subtypes can be differentiated by these 19 plasma markers, as has recently been suggested for protein markers in CSF (Tijms et al., 2020), and if they are specific tor AD. Clearly, biomarker panels that reflect multiple aspects of AD pathophysiology have the potential to expand our understanding of AD heterogeneity and staging beyond what may be possible through measurements of current plasma markers, such as amyloid, p-tau, and NFL (Higginbotham et al., 2020; Johnson et al., 2020). 

    A significant challenge when developing plasma biomarkers is the large variance in plasma protein levels across individuals and populations. The authors are commended for using genomic information to adjust for variation due to population structure in their analyses. Eventually, a better understanding of individual plasma marker variation, including how it relates to age, sex, and genetic background, will be important for implementation of precision medicine approaches using such panels in the clinic.

    References:

    Tijms BM, Gobom J, Reus L, Jansen I, Hong S, Dobricic V, Kilpert F, Ten Kate M, Barkhof F, Tsolaki M, Verhey FR, Popp J, Martinez-Lage P, Vandenberghe R, Lleó A, Molinuevo JL, Engelborghs S, Bertram L, Lovestone S, Streffer J, Vos S, Bos I, Alzheimer’s Disease Neuroimaging Initiative (ADNI), Blennow K, Scheltens P, Teunissen CE, Zetterberg H, Visser PJ. Pathophysiological subtypes of Alzheimer's disease based on cerebrospinal fluid proteomics. Brain. 2020 Dec 1;143(12):3776-3792. PubMed.

    Higginbotham L, Ping L, Dammer EB, Duong DM, Zhou M, Gearing M, Hurst C, Glass JD, Factor SA, Johnson EC, Hajjar I, Lah JJ, Levey AI, Seyfried NT. Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease. Sci Adv. 2020 Oct;6(43) Print 2020 Oct PubMed.

    Johnson EC, Dammer EB, Duong DM, Ping L, Zhou M, Yin L, Higginbotham LA, Guajardo A, White B, Troncoso JC, Thambisetty M, Montine TJ, Lee EB, Trojanowski JQ, Beach TG, Reiman EM, Haroutunian V, Wang M, Schadt E, Zhang B, Dickson DW, Ertekin-Taner N, Golde TE, Petyuk VA, De Jager PL, Bennett DA, Wingo TS, Rangaraju S, Hajjar I, Shulman JM, Lah JJ, Levey AI, Seyfried NT. Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med. 2020 May;26(5):769-780. Epub 2020 Apr 13 PubMed.

    View all comments by Nicholas Seyfried

  5. Jiang et al. assessed the plasma proteome in discovery and validation cohorts of Chinese persons in various stages of clinically diagnosed Alzheimer's disease or who were healthy controls. They initially identified 429 proteins that were differentially expressed in AD, which represented 19 clusters of proteins that were related in function. Within these clusters they identified one protein in each that was mostly correlated with AD diagnosis, and thereby created a panel of 19 proteins (four upregulated and 15 downregulated) that they further studied regarding their ability to differentiate AD from controls and their relation to disease stage markers. They found that this panel had a near-perfect Area Under the Curve (AUC) of 0.9816 in differentiating AD patients from controls. They then applied the panel to a smaller, independent sample (consisting of controls and 36 AD patients) and found an AUC of 0.969, which was better than the AUC of 0.8871 using other A/T/N markers in plasma. They also calculated an index based on these protein levels which classified participants into normal, mild, or severe AD, and this appeared to be correlated with disease stage measured by MoCA score, hippocampal volume, and gray-matter volume.

    Strengths of this study are that it appears to have begun as an unbiased hypothesis-free approach that nonetheless identified proteins that have been previously described in association with AD. They identified 19 clusters of proteins with no direct association with amyloid or tau pathways that were associated with clinical AD diagnosis. This is consistent with our widening perspective on the pathways involved in AD pathogenesis; that is, that there are multiple pathways involved that will likely need to be addressed to successfully treat the disease. Clusters identified include the inflammatory and immune response, platelet activation, apoptosis, cell adhesion, and other aspects of endothelial function. They employed both a discovery cohort and an independent validation cohort, strengthening the likelihood that the association of these protein levels with AD is a real one. The association of plasma markers with severity of disease is intriguing but should be considered preliminary.   

    A question I have about the study is that it indicates that “only individuals in whom the 19-protein biomarker panel and plasma ATN biomarkers were detectable (i.e., above the lower limit of detection; n = 172 and 97 for the discovery and validation cohort, respectively) were included in subsequent analyses.”

    As the paper reads, there were only 180 persons in the discovery cohort and 97 in the validation cohort to start with so, if true, only eight persons total were excluded. This is great if so, but it seems like a more straightforward way of saying it is, "Eight persons were excluded as the relevant proteins could not be measured in plasma." 

    Limitations of this study are that, like many studies of AD biomarkers, a clinical diagnosis of AD is used to classify patients. As we know this is subject to error. The ideal study would involve neuropathological diagnosis as the gold standard. They compared their measures to more well-established A/T/N markers and appear to have found good correspondence, which strengthens the likelihood of an association with AD pathology. However, to then claim the 19-marker panel is superior based on its (slightly) higher ability to predict clinical diagnosis is suspect.

    Might cases with discrepant 19-marker panel and A/T/N indicators of AD be those without AD neuropathology? Also, the validation cohort had a relatively small number of participants with AD, such that the study of these markers in further independent cohorts will be required to establish its utility. 

    The authors appropriately cite quite a few prior papers that have similarly found panels of plasma proteins purported to have diagnostic utility in AD that have not found their way into widespread use.

    View all comments by John Ringman

  6. We are very grateful for the helpful comments about the development of a blood-based biomarker panel for AD in the present stage. One comment is that clinically diagnosed AD is sometimes mixed with other types of dementia, including frontotemporal dementia and Lewy body dementia. To exclude non-AD dementia, AD biomarkers—including amyloid PET, CSF Aβ42, or CSF p-tau—are often used to verify clinical AD diagnosis; nonetheless, information on these biomarkers is not always available in some cohorts. Meanwhile, recent studies showed that changes in plasma p-tau181 are highly specific to AD, and correlate with amyloid and tau status in the brain (Karikari et al., 2020), can also be used to differentiate AD from healthy people as well as from other types of neurodegenerative diseases (AUC = 0.974) (Rodriguez et al., 2020). 

    Notably, in the present study, we showed that the 19 hub proteins exhibit more prominent changes in p-tau-positive AD cases compared with p-tau-negative healthy individuals, achieving accurate classifications between the two (AUC = 0.9863-0.9881).

    Moreover, 10 out of the 19 hub plasma proteins show significant correlation with plasma p-tau181 levels. These findings together support the notion that the 19-protein panel provides indication for the status of AD-specific tau pathology as a diagnostic paradigm. In the future, we will examine the performance of this 19-protein panel in identifying AD cohorts that are verified by other biomarkers such as amyloid PET, as well as its ability to differentiate AD from the other neurodegenerative diseases.

    Another comment is the “overfitting” during the discovery process, which often happens when the sample size of the discovery cohort is not big enough to represent the general AD population. In the present study, to reduce such overfitting effects, we evaluated the classification accuracy of the 19-protein panel in an independent AD cohort, which warranted high performance. To further ensure that the identified AD-associated plasma proteins can be replicated in other AD cohorts, we conducted a comprehensive comparison between our findings and previous AD plasma proteome studies, and we showed that 56 out of 77 previously reported proteins can be replicated in our study (Table A1), including well-known AD-associated plasma proteins such as EGF, sVCAM1, IGFBP2, and PPY (Ray et al., 2007; Sattlecker et al., 2014; Docket et al., 2012; Hu et al., 2012). 

    Moreover, we conducted a correlation analysis of 270 plasma proteins that had been measured in both our cohort and a Swedish MCI cohort (i.e., BioFINDER) using the same PEA technology (Whelan et al., 2019), and it showed strong correlation in the changes of the proteins between the two cohorts (r2 up to 0.49; Figure S3). These findings together suggest that those plasma proteins reported in our study may exhibit consistent alterations in AD across ethnic groups. To consolidate this, we will continue to examine the performance of this protein panel in other independent AD cohorts, which will help evaluate its applicability in the general population.

    References:

    Karikari TK, Pascoal TA, Ashton NJ, Janelidze S, Benedet AL, Rodriguez JL, Chamoun M, Savard M, Kang MS, Therriault J, Schöll M, Massarweh G, Soucy JP, Höglund K, Brinkmalm G, Mattsson N, Palmqvist S, Gauthier S, Stomrud E, Zetterberg H, Hansson O, Rosa-Neto P, Blennow K. Blood phosphorylated tau 181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. 2020 May;19(5):422-433. PubMed.

    Lantero Rodriguez J, Karikari TK, Suárez-Calvet M, Troakes C, King A, Emersic A, Aarsland D, Hye A, Zetterberg H, Blennow K, Ashton NJ. Plasma p-tau181 accurately predicts Alzheimer's disease pathology at least 8 years prior to post-mortem and improves the clinical characterisation of cognitive decline. Acta Neuropathol. 2020 Sep;140(3):267-278. Epub 2020 Jul 27 PubMed.

    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. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat Med. 2007 Nov;13(11):1359-62. PubMed.

    Sattlecker M, Kiddle SJ, Newhouse S, Proitsi P, Nelson S, Williams S, Johnston C, Killick R, Simmons A, Westman E, Hodges A, Soininen H, Kłoszewska I, Mecocci P, Tsolaki M, Vellas B, Lovestone S, the AddNeuroMed Consortium, Dobson RJ. Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimers Dement. 2014 Apr 24; PubMed.

    Doecke JD, Laws SM, Faux NG, Wilson W, Burnham SC, Lam CP, Mondal A, Bedo J, Bush AI, Brown B, De Ruyck K, Ellis KA, Fowler C, Gupta VB, Head R, Macaulay SL, Pertile K, Rowe CC, Rembach A, Rodrigues M, Rumble R, Szoeke C, Taddei K, Taddei T, Trounson B, Ames D, Masters CL, Martins RN, . Blood-Based Protein Biomarkers for Diagnosis of Alzheimer Disease. Arch Neurol. 2012 Jul 16;:1-8. PubMed.

    Hu WT, Holtzman DM, Fagan AM, Shaw LM, Perrin R, Arnold SE, Grossman M, Xiong C, Craig-Schapiro R, Clark CM, Pickering E, Kuhn M, Chen Y, Van Deerlin VM, McCluskey L, Elman L, Karlawish J, Chen-Plotkin A, Hurtig HI, Siderowf A, Swenson F, Lee VM, Morris JC, Trojanowski JQ, Soares H, . Plasma multianalyte profiling in mild cognitive impairment and Alzheimer disease. Neurology. 2012 Aug 28;79(9):897-905. PubMed.

    Whelan CD, Mattsson N, Nagle MW, Vijayaraghavan S, Hyde C, Janelidze S, Stomrud E, Lee J, Fitz L, Samad TA, Ramaswamy G, Margolin RA, Malarstig A, Hansson O. Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer's disease. Acta Neuropathol Commun. 2019 Nov 6;7(1):169. PubMed.

    View all comments by Nancy Ip

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