Phenotyping Alzheimer's disease with blood tests

doi:10.1126/science.abi5208

GSTDTAP > 气候变化

DOI	10.1126/science.abi5208
	Phenotyping Alzheimer's disease with blood tests
	Kaj Blennow
	2021-08-06
发表期刊	Science
出版年	2021
英文摘要	Alzheimer's disease (AD) is characterized by brain protein aggregates of amyloid-β (Aβ) and phosphorylated tau (pTau) that become plaques and tangles, and dystrophic neurites surrounding the plaques, which are accompanied by downstream neurodegeneration. These protein changes can be used as biomarkers detected through positron emission tomography (PET) imaging and in cerebrospinal fluid (CSF), allowing for ATN (amyloid, tau, and neurodegeneration) classification of patients. This phenotyping has become standard in AD clinical trials to overcome the high misclassification rate (20 to 30%) for clinical criteria and also enables enrollment of preclinical AD patients. The recent approval of the first disease-modifying anti-amyloid immunotherapy, aducanumab, for AD will generate a need for widely accessible and inexpensive biomarkers for ATN classification of patients with cognitive complaints. Technological advances have also overcome the challenges of measuring the extraordinarily low amounts of brain-derived proteins in blood samples, and recent studies indicate that AD blood tests may soon be possible. The Aβ42 variant of Aβ is aggregation-prone and is deposited in plaques in the brains of people with AD, whereas the shorter Aβ40 isoform is by far the most abundant Aβ peptide (see the figure). Thus, as AD progresses and Aβ42 forms plaques, its concentration in the CSF and blood is reduced. Ascertaining the ratio of Aβ42 and Aβ40 concentrations in the CSF is known to adjust for between-individual differences in “total” Aβ production, thereby increasing concordance with amyloid PET imaging to detect brain amyloidosis. Applying the same principle for blood plasma Aβ, immunoprecipitation–mass spectrometry (IP-MS) measures of plasma Aβ42/Aβ40 ratio can reach an accuracy exceeding 90% to identify brain amyloidosis ([ 1 ][1]). A population-based study of 441 asymptomatic elderly individuals indicates that IP-MS plasma Aβ can identify those who are amyloid PET-positive with high accuracy ([ 2 ][2]). ![Figure][3] Biomarkers of Alzheimer's disease Low amyloid-β (Aβ) 42/40 isoform ratio is associated with brain amyloidosis, and several phosphorylated tau (pTau) fragments increase with tau pathology; both are specific blood biomarkers for Alzheimer's disease (AD). Among neurodegeneration biomarkers, neurofilament light (NFL) is modestly increased in AD, and total tau (T-tau) is markedly increased only in cerebrospinal fluid (CSF), and not blood, in AD. Glial fibrillary acidic protein (GFAP) is a candidate blood biomarker for astrocytic activation, to indicate neuroinflammation. GRAPHIC: KELLIE HOLOSKI/ SCIENCE The question then arises whether plasma Aβ detection can replace PET or CSF tests for brain amyloidosis. A potential issue is that Aβ is produced not only in the brain but also in platelets and peripheral tissues, which will obscure the central nervous system–derived Aβ signal in plasma. Consequently, in amyloid PET-positive cases, plasma Aβ42/Aβ40 ratio is only ∼10% lower than in individuals without brain amyloidosis, whereas it is more than 40% lower in CSF ([ 3 ][4]). This leads to an overlap that introduces challenges to robustly classify individuals as being either amyloid positive or negative, especially in those with Aβ42/Aβ40 ratios close to the cut-off for normality. Algorithms combining plasma Aβ42/Aβ40 ratio with the ϵ4 variant of apolipoprotein E ( APOE ), which is the major AD risk gene, and age (the main risk factor for AD) increase accuracy in detecting brain amyloidosis by 2 to 6% ([ 2 ][2], [ 3 ][4]). However, merging biomarker data with genetic risk and aging may cause confusion because some younger APOE -ϵ4 noncarriers with low plasma Aβ42/Aβ40 will be misclassified as amyloid negative by the algorithm, whereas a proportion of older individuals with homozygous APOE -ϵ4 but normal plasma Aβ42/Aβ40 will be wrongly classified as amyloid positive. Tau protein is truncated into amino-terminal to mid-domain fragments before being secreted in blood plasma and CSF ([ 4 ][5]). CSF pTau has long been used as an AD-specific biomarker. A major breakthrough is the use of new ultrasensitive methods that allow for quantification of pTau in blood plasma, with high concentrations occurring in AD ([ 5 ][6]). Of 321 patients and controls, high plasma concentrations of pTau181 fragments were associated with brain tau pathology as measured by PET ([ 6 ][7]). Similar results were subsequently presented for other pTau species, including pTau217 ([ 7 ][8]) and pTau231 ([ 8 ][9]). The findings of very high accuracy of plasma pTau217 in the ability to discriminate AD from other neurodegenerative disorders ([ 7 ][8]) and IP-MS data showing a higher magnitude of increase and better association with amyloid plaques by PET of plasma pTau217 than of pTau181 ([ 4 ][5]) suggest that there may be diagnostic or pathophysiological differences between pTau species, but this remains a matter of debate. Nonetheless, these pTau blood biomarkers all show high concordance with AD pathology at autopsy, with accuracies in differentiating AD from non-AD dementia cases up to 99% for pTau231 ([ 8 ][9]). However, these studies are based on different analytical methods and cohorts. In an attempt to directly compare these pTau species, a study of 381 participants employing digital immunoassays for pTau181, pTau217, and pTau231 found strong correlations with amounts of pTau species in CSF. Moreover, although the fold change was highest for pTau217, the accuracy in identifying amyloid PET positivity was very high for all pTau species ([ 9 ][10]), suggesting that differences are not meaningful. A study of two large cohorts of 883 individuals with cognitive symptoms also showed high accuracy (90 to 91%) of both plasma pTau181 and pTau217 to predict clinical progression to AD dementia in algorithms that include memory and executive function tests and APOE genotyping ([ 10 ][11]). Overall, plasma pTau biomarkers fulfill many requirements for a clinically useful AD test, with a high fold change in AD (between two to four times higher in AD than non-AD controls across studies), and an increase early in the AD continuum (even preclinically), an association with amyloid-associated tau pathophysiology and tangle burden in the brain, and an increase specifically found in AD but not in other types of dementia. The findings of an early increase in plasma pTau fragments in patients with evidence of amyloid plaques, but not tau abnormalities, by PET imaging may be interpreted as a neuronal response to Aβ aggregates that gives rise to increased pTau secretion into CSF and blood plasma. However, findings in biomarker studies are only associations and may not directly reveal causal relationships. For example, plasma pTau231 shows a 10- to 15-fold increase within 24 hours after acute traumatic brain injury, especially evident in younger patients (who are unlikely to have amyloid or tau pathology) ([ 11 ][12]). Total tau (T-tau), referring to any tau variant or fragment regardless of phosphorylation, and other brain proteins such as glial fibrillary acidic protein (GFAP) also increase in blood plasma, hypothetically mediated by a trauma-induced compromise of the blood-brain barrier, with release of proteins preexisting in the extracellular space. Even if different mechanisms operate in specific disorders, further research is needed to understand the mechanisms underlying the increase in plasma pTau in AD. In the search for blood biomarkers of neurodegeneration, it has become evident that in contrast to CSF, where T-tau is markedly increased in AD, T-tau does not work as a biomarker of AD neurodegeneration in blood. Instead, another axonal protein, neurofilament light (NFL), has been evaluated as a substitute AD neurodegeneration biomarker, even though it is not involved in AD pathogenesis. Plasma NFL concentrations correlate well with CSF concentrations, supporting that it reflects brain pathophysiology. But high amounts are found in a wide variety of neurodegenerative disorders, so this biomarker lacks specificity. Nevertheless, plasma NFL, which shows a modest increase in AD, predicts both cognitive deterioration and rate of neurodegeneration as measured by atrophy on brain imaging. Notably, both plasma and CSF NFL concentrations increase in cognitively unimpaired people with autosomal dominant AD 7 years before symptom onset ([ 12 ][13]), so this may be a good biomarker for predicting AD. Another candidate AD blood biomarker includes the astrocytic protein GFAP, which is markedly increased in AD. Plasma GFAP distinguishes amyloid PET-positive and -negative cognitively normal elderly with high accuracy ([ 13 ][14]), and may serve as a blood biomarker for glial activation and neuroinflammation. Despite both rapid and robust reductions in amyloid PET ligand binding after treatment with Aβ immunotherapies (indicative of drug target engagement), effects on cognitive outcomes have been less evident. Therefore, biomarker evidence for downstream effects on reducing tau pathology and neurodegeneration is important to support disease-modifying effects by this class of drugs. Given that in most clinical trials only a small percentage of enrolled patients undergo repeat lumbar puncture for CSF testing, blood biomarkers could play an important role to accomplish this. Data from other areas of clinical neuroscience show that children with spinal muscular atrophy have a marked increase in CSF NFL, but treatment with the antisense oligonucleotide drug nusinersen results in a successive reduction of NFL concentrations in CSF with normalization after ∼7 months, and the reduction correlates with clinical improvements ([ 14 ][15]). Similar, but less pronounced, reductions of plasma NFL are seen with disease-modifying treatments in multiple sclerosis patients. These findings may serve as proof of concept for the usefulness of plasma NFL in identifying downstream drug effects on neurodegeneration. Target engagement for the anti-Aβ drug, aducanumab, was demonstrated in 2017, with dose-dependent reductions on amyloid PET ([ 15 ][16]), but to date there are no reports of effects on blood biomarkers of neurodegeneration (or tau pathology) from any Aβ immunotherapy trial. Current studies of blood AD biomarkers come exclusively from cohorts at highly specialized research centers. Thus, further clinical validation is needed, specifically on the diagnostic accuracy of the AD blood biomarkers, alone or in combination, in consecutive patient populations at memory clinics and in primary care settings. In addition, because plasma pTau increases progressively with tau pathology in the brain and more advanced clinical stage, more data are needed on the accuracy of plasma pTau biomarkers to identify individuals with preclinical or early symptoms who will go on to develop AD. Moreover, studies comparing plasma pTau species in the same cohorts and using the same technology are needed to understand if there are pathophysiological differences across the pTau epitopes. Current assays are research grade, and full analytical validation of methods is needed to achieve accurate and comparable results between laboratories, as well as global efforts to develop certified reference materials to achieve harmonization across assay platforms. Transferring the blood tests to fully automated platforms would also help to streamline these procedures and to establish these blood tests as clinically useful tools. Lastly, to make blood biomarkers attractive substitutes for imaging, costs need to be substantially lower than costs for the PET scans. 1. [↵][17]1. A. Nakamura et al ., Nature 554, 249 (2018). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. A. Keshavan et al ., Brain 144, 434 (2021). [OpenUrl][22] 3. [↵][23]1. S. E. Schindler et al ., Neurology 93, 17 (2019). [OpenUrl][24] 4. [↵][25]1. N. R. Barthélemy, 2. K. Horie, 3. C. Sato, 4. R. J. Bateman , J. Exp. Med. 217, e20200861 (2020). [OpenUrl][26][CrossRef][27][PubMed][28] 5. [↵][29]1. M. M. Mielke et al ., Alzheimers Dement. 14, 989 (2018). [OpenUrl][30][CrossRef][31][PubMed][28] 6. [↵][32]1. T. K. Karikari et al ., Lancet Neurol. 19, 422 (2020). [OpenUrl][33][CrossRef][34][PubMed][28] 7. [↵][35]1. S. Palmqvist et al ., JAMA 324, 772 (2020). [OpenUrl][36][PubMed][28] 8. [↵][37]1. N. J. Ashton et al ., Acta Neuropathol. 141, 709 (2021). [OpenUrl][38][PubMed][28] 9. [↵][39]1. M. Suárez-Calvet et al ., EMBO Mol. Med. 12, e12921 (2020). [OpenUrl][40] 10. [↵][41]1. S. Palmqvist et al ., Nat. Med. 27, 1034 (2021). [OpenUrl][42] 11. [↵][43]1. R. Rubenstein et al ., JAMA Neurol. 74, 1063 (2017). [OpenUrl][44] 12. [↵][45]1. O. Preische et al ., Nat. Med. 25, 277 (2019). [OpenUrl][46][PubMed][28] 13. [↵][47]1. P. Chatterjee et al ., Transl. Psychiatry 11, 27 (2021). [OpenUrl][48] 14. [↵][49]1. B. Olsson et al ., J. Neurol. 266, 2129 (2019). [OpenUrl][50] 15. [↵][51]1. J. Sevigny et al ., Nature 546, 564 (2017). [OpenUrl][52] Acknowledgments: K.B. has consulted for Axon, Biogen, Lilly, and Roche Diagnostics and is cofounder of Brain Biomarker Solutions in Gothenburg AB. [1]: #ref-1 [2]: #ref-2 [3]: pending:yes [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: #ref-6 [8]: #ref-7 [9]: #ref-8 [10]: #ref-9 [11]: #ref-10 [12]: #ref-11 [13]: #ref-12 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D554%26rft.spage%253D249%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature25456%26rft_id%253Dinfo%253Apmid%252F29420472%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: /lookup/external-ref?access_num=10.1038/nature25456&link_type=DOI [20]: /lookup/external-ref?access_num=29420472&link_type=MED&atom=%2Fsci%2F373%2F6555%2F626.atom [21]: #xref-ref-2-1 "View reference 2 in text" [22]: {openurl}?query=rft.jtitle%253DBrain%26rft.volume%253D144%26rft.spage%253D434%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: #xref-ref-3-1 "View reference 3 in text" [24]: {openurl}?query=rft.jtitle%253DNeurology%26rft.volume%253D93%26rft.spage%253D17%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [25]: #xref-ref-4-1 "View reference 4 in text" [26]: {openurl}?query=rft.jtitle%253DJ.%2BExp.%2BMed.%26rft.volume%253D217%26rft.spage%253De20200861%26rft_id%253Dinfo%253Adoi%252F10.1084%252Fjem.20200861%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [27]: /lookup/external-ref?access_num=10.1084/jem.20200861&link_type=DOI [28]: /lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fsci%2F373%2F6555%2F626.atom [29]: #xref-ref-5-1 "View reference 5 in text" [30]: {openurl}?query=rft.jtitle%253DAlzheimers%2BDement.%26rft.volume%253D14%26rft.spage%253D989%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.jalz.2018.02.013%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [31]: /lookup/external-ref?access_num=10.1016/j.jalz.2018.02.013&link_type=DOI [32]: #xref-ref-6-1 "View reference 6 in text" [33]: {openurl}?query=rft.jtitle%253DLancet%2BNeurol.%26rft.volume%253D19%26rft.spage%253D422%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS1474-4422%252820%252930071-5%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [34]: /lookup/external-ref?access_num=10.1016/S1474-4422(20)30071-5&link_type=DOI [35]: #xref-ref-7-1 "View reference 7 in text" [36]: {openurl}?query=rft.jtitle%253DJAMA%26rft.volume%253D324%26rft.spage%253D772%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [37]: #xref-ref-8-1 "View reference 8 in text" [38]: {openurl}?query=rft.jtitle%253DActa%2BNeuropathol.%26rft.volume%253D141%26rft.spage%253D709%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: #xref-ref-9-1 "View reference 9 in text" [40]: {openurl}?query=rft.jtitle%253DEMBO%2BMol.%2BMed.%26rft.volume%253D12%26rft.spage%253De12921%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: #xref-ref-10-1 "View reference 10 in text" [42]: {openurl}?query=rft.jtitle%253DNat.%2BMed.%26rft.volume%253D27%26rft.spage%253D1034%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [43]: #xref-ref-11-1 "View reference 11 in text" [44]: {openurl}?query=rft.jtitle%253DJAMA%2BNeurol.%26rft.volume%253D74%26rft.spage%253D1063%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [45]: #xref-ref-12-1 "View reference 12 in text" [46]: {openurl}?query=rft.jtitle%253DNat.%2BMed.%26rft.volume%253D25%26rft.spage%253D277%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [47]: #xref-ref-13-1 "View reference 13 in text" [48]: {openurl}?query=rft.jtitle%253DTransl.%2BPsychiatry%26rft.volume%253D11%26rft.spage%253D27%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [49]: #xref-ref-14-1 "View reference 14 in text" [50]: {openurl}?query=rft.jtitle%253DJ.%2BNeurol.%26rft.volume%253D266%26rft.spage%253D2129%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [51]: #xref-ref-15-1 "View reference 15 in text" [52]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D546%26rft.spage%253D564%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx
领域	气候变化 ; 资源环境
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/335570
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Kaj Blennow. Phenotyping Alzheimer's disease with blood tests[J]. Science,2021.
APA	Kaj Blennow.(2021).Phenotyping Alzheimer's disease with blood tests.Science.
MLA	Kaj Blennow."Phenotyping Alzheimer's disease with blood tests".Science (2021).