Multicenter Multivariate Studies

Alzheimer’s Disease (AD) Neuroimaging Initiative (ADNI) Related Biofluid And Genetic Biomarker Studies Using Non-ADNI University Of Pennsylvania (UPenn) Biosamples From Patients Followed Longitudinally Affected By AD, Parkinson’s Disease With (PDD) And Without (PD) Dementia, Frontotemporal Degeneration (FTD), Amyotrophic Lateral Sclerosis (ALS) Or Related Neurodegenerative Diseases (ND)

John Q. Trojanowski and Leslie M. Shaw

The Center for Neurodegenerative Disease (ND) Research (CNDR; (http://www.med.upenn.edu/cndr/) is the home base of of several large programmatic grants focusing on basic science and patient oriented research begining in 1990 with an NIA funded PO1 on shared mechanisms of AD and PD led by John Trojanowski (PO1 AG-09215) that ran from 1990 to 2010 and then was continued from 2007 to the present as the Penn Udall Center (P50 NS-053488). In addition, there is a Penn AD Core Center (ADCC; P30 AG-10124-27) led by John Trojanowski, which has a dementia with Lewy Body (DLB) and frontotemporal degeneration (FTD) module, and an NIA funded PO1 on FTD led by Virginia Lee (P01 AG-17586-16) as well as a previously funded PO1 on ALS and TDP-43 (P01 AG032953) led by Virginia Lee. Of course there many other AD and related dementia (ADRD) grants at Penn including the AD Genetics Consortium led by Jerry Schelenberg and Li-San Wang, but the grants listed above follow patients longitudinally.

Thus, CNDR has systematically collected patient biosamples including CSF, DNA/RNA, plasma, serum, whole blood and postmortem brain samples since 1990 from longitudinally followed patients, many of whom consent to autopsy. Accordingly, CNDR has banked CNS tissues from >1,700 subjects with a primary diagnosis of AD, Lewy body demenias (LBD), FTD, ALS, related NDs and normal controls (NC). This includes cases with the following primary neuropathology diagnoses, although many have other or additional ND co-pathologies¹ such as aging related astroglial tauopathy (ARTAG):^2-4 576 AD, 120 PD, 134 PDD, 52 DLB, 55 MSA brains and 257 NC or pathological aging related tauopathy (PART).⁵ There also are 111 frontotemporal lobar degeneration (FTLD) due to TDP-32 inclusions (FTLD-TDP), 52 ALS, 50 CBD,106 PSP and 23 PiD brains. DNA from brain is available on >1500 (89%) of autopsies.  Some autopsy cases have paired DNA from  blood or saliva and brain (n=515).  DNA from peripheral blood is banked from approximately 480 NC and 3700 individuals in the following diagnostic categories: 1478 AD, 613 ALS, 573 FTLD, 1003 PD/PDD/DLB, 22 MSA. For archival biofluids, we have CSF samples from >1500 individuals in the following diagnostic categories: 634 AD, 507 FTLD, 202 PD/PDD/DLB, 151 ALS, and 105 NC. We have plasma samples from >3300 unique individuals in the following diagnostic categories: 859 AD, 504 FTLD, 948 PD/PDD/DLB, 545 ALS, and 454 NC. In nearly all cases, CSF is matched with plasma collected at the same time, and for many individuals, multiple samples have been collected longitudinally. Thus, our archived DNA/RNA and biofluid biosamples are extremely valuable for comparative studies across diverse ND and they have been used extensively in neuropathology, genetic and biomarker studies supported by the NIH funded grants noted above.^{1, 6, 7} Below are brief highlights of genetic and biofluid biomarker studies conducted on Penn non-ADNI biosamples mainly since 2013 that complement the biomarker studies done in ADNI with ADNI patient samples.

Studies conducted using these UPenn samples often include biosamples from other centers or investigators beyond Penn and the focus here is on biomarkers (mainly CSF, plasma and DNA, but some are combined with PET imaging and structural imaging data) and neuropathology. This is exemplified in particular by comparative studiesof AD and PD including: 1) Recognition of the contribution of co-morbid AD and alpha-synuclein (aSyn) pathology to cognitive decline in AD with concomitant aSyn pathlogy and LBD.^8-13 Specifically, co-morbid A? and tau neuropathology, APOE ?4 genotype, lower CSF A?1-42 levels, and increasing A? amyloid burden on PET imaging are all associated with CI in PD. 2) Establishing that specific plasma biomarkers are predictors of CI in PD and AD.^14-16  3) Comparing the differential effects of genetic factors on cognition in PD and AD.^17-19 4) Other collaborative genetic^{17, 18, 20-34} and biofluid biomarker studies^{15, 35-52} demonstrate that SNPs and biofluid biomarkers changes can be disease specific or are altered in many ND. However, we have focused mainly on AD, PD and FTD in our Penn cohorts that enable comparisons of these UPenn data with data from ADNI subjects. Notably, we continue to expand our collaborations with the Blennow/Zetterberg group so that as ADNI Add-On studies identify potentially informative biomarkers in ADNI biosamples going forward, we will follow up with the interrogation of biosamples from the UPenn AD, PD, FTD and ALS subjects. Finally, it shoult be noted that our UPenn cohort is more diverse than the ADNI cohort and we have followed ~150 patients with antemortem biofluid draws to autopsy so we can extend the type of study we reported in Toledo et al.³⁹ to our collaborations with the Blennow/Zetterberg group. This is exemplified in the Cullen et al collaborative study with the Blennow and Zetterberg group (In review , 2018) wherein we collected a panel of five CSF biomarkers ” A?42, total tau (t-tau), phosphorylated tau (p-tau), neurofilament light (NfL), and neurogranin (Ng) ” in 723 subjects representing nine neurodegenerative disorders including both an autopsy-confirmed AD cohort and a clinically representative, probable AD cohort. To seek to differentiate between AD and each of the other disorders in our study cohort, we identified univariate cutoff values of the most informative single biomarker and also fit a multivariate machine learning model using all biomarkers at once. As a result, we found that the multivariate predictive model achieved high differential diagnostic accuracy and greatly outperformed the univariate cutoff model. The strong results on both a gold standard, autopsy-confirmed AD cohort and a clinically representative AD cohort support the idea that this panel is capable of greatly improving patient selection in AD clinical trials.

REFERENCES

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13)       Irwin DJ, Xie SX, Coughlin D, Nevler N, Akhtar RS, McMillan CT, Lee EB, Wolk DA, Weintraub D, Chen-Plotkin A, Duda JE, Spindler M, Siderowf A, Hurtig HI, Shaw LM, Grossman M, Trojanowski JQ. CSF tau and beta-amyloid predict cerebral synucleinopathy in autopsied Lewy body disorders. Neurology 90(12):e1038-e46, 2018; PMCID: PMC5874449.

14)       Lim NS, Swanson CR, Cherng HR, Unger TL, Xie SX, Weintraub D, Marek K, Stern MB, Siderowf A, Trojanowski JQ, Chen-Plotkin AS. Plasma EGF and cognitive decline in Parkinson’s disease and Alzheimer’s disease. Annals of clinical and translational neurology 3(5):346-55, 2016; PMCID: PMC4863747.

15)       Berlyand Y, Weintraub D, Xie SX, Mellis IA, Doshi J, Rick J, McBride J, Davatzikos C, Shaw LM, Hurtig H, Trojanowski JQ, Chen-Plotkin AS. An Alzheimer’s Disease-Derived Biomarker Signature Identifies Parkinson’s Disease Patients with Dementia. PLoS One 11(1):e0147319, 2016; PMCID: PMC4727929.

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19)       Mata IF, Johnson CO, Leverenz JB, Weintraub D, Trojanowski JQ, Van Deerlin VM, Ritz B, Rausch R, Factor SA, Wood-Siverio C, Quinn JF, Chung KA, Peterson-Hiller AL, Espay AJ, Revilla FJ, Devoto J, Yearout D, Hu SC, Cholerton BA, Montine TJ, Edwards KL, Zabetian CP. Large-scale exploratory genetic analysis of cognitive impairment in Parkinson’s disease. Neurobiol Aging 56:211.e1-.e7, 2017; PMCID: PMC5536182.

20)       Beecham GW, Hamilton K, Naj AC, Martin ER, Huentelman M, Myers AJ, Corneveaux JJ, Hardy J, Vonsattel JP, Younkin SG, Bennett DA, De Jager PL, Larson EB, Crane PK, Kamboh MI, Kofler JK, Mash DC, Duque L, Gilbert JR, Gwirtsman H, Buxbaum JD, Kramer P, Dickson DW, Farrer LA, Frosch MP, Ghetti B, Haines JL, Hyman BT, Kukull WA, Mayeux RP, Pericak-Vance MA, Schneider JA, Trojanowski JQ, Reiman EM, Alzheimer’s Disease Genetics C, Schellenberg GD, Montine TJ. Genome-wide association meta-analysis of neuropathologic features of Alzheimer’s disease and related dementias. PLoS Genet 10(9):e1004606, 2014; PMCID: PMC4154667.

21)       Beecham GW, Dickson DW, Scott WK, Martin ER, Schellenberg G, Nuytemans K, Larson EB, Buxbaum JD, Trojanowski JQ, Van Deerlin VM, Hurtig HI, Mash DC, Beach TG, Troncoso JC, Pletnikova O, Frosch MP, Ghetti B, Foroud TM, Honig LS, Marder K, Vonsattel JP, Goldman SM, Vinters HV, Ross OA, Wszolek ZK, Wang L, Dykxhoorn DM, Pericak-Vance MA, Montine TJ, Leverenz JB, Dawson TM, Vance JM. PARK10 is a major locus for sporadic neuropathologically confirmed Parkinson disease. Neurology 84(10):972-80, 2015; PMCID: PMC4352096.

22)       Chahine LM, Qiang J, Ashbridge E, Minger J, Yearout D, Horn S, Colcher A, Hurtig HI, Lee VM, Van Deerlin VM, Leverenz JB, Siderowf AD, Trojanowski JQ, Zabetian CP, Chen-Plotkin A. Clinical and biochemical differences in patients having Parkinson disease with vs without GBA mutations. JAMA neurology 70(7):852-8, 2013; PMCID: PMC3762458.

23)       Kalia LV, Lang AE, Hazrati LN, Fujioka S, Wszolek ZK, Dickson DW, Ross OA, Van Deerlin VM, Trojanowski JQ, Hurtig HI, Alcalay RN, Marder KS, Clark LN, Gaig C, Tolosa E, Ruiz-Martinez J, Marti-Masso JF, Ferrer I, Lopez de Munain A, Goldman SM, Schule B, Langston JW, Aasly JO, Giordana MT, Bonifati V, Puschmann A, Canesi M, Pezzoli G, Maues De Paula A, Hasegawa K, Duyckaerts C, Brice A, Stoessl AJ, Marras C. Clinical correlations with Lewy body pathology in LRRK2-related Parkinson disease. JAMA neurology 72(1):100-5, 2015; PMCID: PMC4399368.

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28)       Nuytemans K, Inchausti V, Beecham GW, Wang L, Dickson DW, Trojanowski JQ, Lee VM, Mash DC, Frosch MP, Foroud TM, Honig LS, Montine TJ, Dawson TM, Martin ER, Scott WK, Vance JM. Absence of C9ORF72 expanded or intermediate repeats in autopsy-confirmed Parkinson’s disease. Mov Disord 29(6):827-30, 2014; PMCID: PMC4022044.

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33)       Wang LS, Naj AC, Graham RR, Crane PK, Kunkle BW, Cruchaga C, Murcia JD, Cannon-Albright L, Baldwin CT, Zetterberg H, Blennow K, Kukull WA, Faber KM, Schupf N, Norton MC, Tschanz JT, Munger RG, Corcoran CD, Rogaeva E, Alzheimer’s Disease Genetics C, Lin CF, Dombroski BA, Cantwell LB, Partch A, Valladares O, Hakonarson H, St George-Hyslop P, Green RC, Goate AM, Foroud TM, Carney RM, Larson EB, Behrens TW, Kauwe JS, Haines JL, Farrer LA, Pericak-Vance MA, Mayeux R, Schellenberg GD, National Institute on Aging-Late-Onset Alzheimer’s Disease Family S, Albert MS, Albin RL, Apostolova LG, Arnold SE, Barber R, Barmada M, Barnes LL, Beach TG, Becker JT, Beecham GW, Beekly D, Bennett DA, Bigio EH, Bird TD, Blacker D, Boeve BF, Bowen JD, Boxer A, Burke JR, Buxbaum JD, Cairns NJ, Cao C, Carlson CS, Carroll SL, Chui HC, Clark DG, Cribbs DH, Crocco EA, DeCarli C, DeKosky ST, Demirci FY, Dick M, Dickson DW, Duara R, Ertekin-Taner N, Fallon KB, Farlow MR, Ferris S, Frosch MP, Galasko DR, Ganguli M, Gearing M, Geschwind DH, Ghetti B, Gilbert JR, Glass JD, Graff-Radford NR, Growdon JH, Hamilton RL, Hamilton-Nelson KL, Harrell LE, Head E, Honig LS, Hulette CM, Hyman BT, Jarvik GP, Jicha GA, Jin LW, Jun G, Jun G, Kamboh MI, Karydas A, Kaye JA, Kim R, Koo EH, Kowall NW, Kramer JH, LaFerla FM, Lah JJ, Leverenz JB, Levey AI, Li G, Lieberman AP, Lopez OL, Lunetta KL, Lyketsos CG, Mack WJ, Marson DC, Martin ER, Martiniuk F, Mash DC, Masliah E, McCormick WC, McCurry SM, McDavid AN, McKee AC, Mesulam WM, Miller BL, Miller CA, Miller JW, Montine TJ, Morris JC, Murrell JR, Olichney JM, Parisi JE, Perry W, Peskind E, Petersen RC, Pierce A, Poon WW, Potter H, Quinn JF, Raj A, Raskind M, Reiman EM, Reisberg B, Reitz C, Ringman JM, Roberson ED, Rosen HJ, Rosenberg RN, Sano M, Saykin AJ, Schneider JA, Schneider LS, Seeley WW, Smith AG, Sonnen JA, Spina S, Stern RA, Tanzi RE, Thornton-Wells TA, Trojanowski JQ, Troncoso JC, Tsuang DW, Van Deerlin VM, Van Eldik LJ, Vardarajan BN, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Williamson J, Wishnek S, Woltjer RL, Wright CB, Younkin SG, Yu CE, Yu L. Rarity of the Alzheimer disease-protective APP A673T variant in the United States. JAMA neurology 72(2):209-16, 2015; PMCID: PMC4324097.

34)       McMillan CT, Toledo JB, Avants BB, Cook PA, Wood EM, Suh E, Irwin DJ, Powers J, Olm C, Elman L, McCluskey L, Schellenberg GD, Lee VM, Trojanowski JQ, Van Deerlin VM, Grossman M. Genetic and neuroanatomic associations in sporadic frontotemporal lobar degeneration. Neurobiol Aging 35(6):1473-82, 2014; PMCID: PMC3961542.

35)       Kang JH, Irwin DJ, Chen-Plotkin AS, Siderowf A, Caspell C, Coffey CS, Waligorska T, Taylor P, Pan S, Frasier M, Marek K, Kieburtz K, Jennings D, Simuni T, Tanner CM, Singleton A, Toga AW, Chowdhury S, Mollenhauer B, Trojanowski JQ, Shaw LM, Parkinson’s Progression Markers I. Association of cerebrospinal fluid beta-amyloid 1-42, T-tau, P-tau181, and alpha-synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA neurology 70(10):1277-87, 2013; PMCID: PMC4034348.

36)       Kang JH, Mollenhauer B, Coffey CS, Toledo JB, Weintraub D, Galasko DR, Irwin DJ, Van Deerlin V, Chen-Plotkin AS, Caspell-Garcia C, Waligorska T, Taylor P, Shah N, Pan S, Zero P, Frasier M, Marek K, Kieburtz K, Jennings D, Tanner CM, Simuni T, Singleton A, Toga AW, Chowdhury S, Trojanowski JQ, Shaw LM, Parkinson’s Progression Marker I. CSF biomarkers associated with disease heterogeneity in early Parkinson’s disease: the Parkinson’s Progression Markers Initiative study. Acta Neuropathol 131(6):935-49, 2016.

37)       Lim NS, Swanson CR, Cherng HR, Unger TL, Xie SX, Weintraub D, Marek K, Stern MB, Siderowf A, Investigators P, Alzheimer’s Disease Neuroimaging I, Trojanowski JQ, Chen-Plotkin AS. Plasma EGF and cognitive decline in Parkinson’s disease and Alzheimer’s disease. Annals of clinical and translational neurology 3(5):346-55, 2016; PMCID: PMC4863747.

38)       Swanson CR, Li K, Unger TL, Gallagher MD, Van Deerlin VM, Agarwal P, Leverenz J, Roberts J, Samii A, Gross RG, Hurtig H, Rick J, Weintraub D, Trojanowski JQ, Zabetian C, Chen-Plotkin AS. Lower plasma apolipoprotein A1 levels are found in Parkinson’s disease and associate with apolipoprotein A1 genotype. Mov Disord 30(6):805-12, 2015; PMCID: PMC4362847.

39)       Toledo JB, Brettschneider J, Grossman M, Arnold SE, Hu WT, Xie SX, Lee VM, Shaw LM, Trojanowski JQ. CSF biomarkers cutoffs: the importance of coincident neuropathological diseases. Acta Neuropathol 124(1):23-35, 2012; PMCID: PMC3551449.

40)       Toledo JB, Korff A, Shaw LM, Trojanowski JQ, Zhang J. CSF alpha-synuclein improves diagnostic and prognostic performance of CSF tau and Abeta in Alzheimer’s disease. Acta Neuropathol 126(5):683-97, 2013; PMCID: PMC3812407.

41)       Swanson CR, Berlyand Y, Xie SX, Alcalay RN, Chahine LM, Chen-Plotkin AS. Plasma apolipoprotein A1 associates with age at onset and motor severity in early Parkinson’s disease patients. Mov Disord 30(12):1648-56, 2015; PMCID: PMC4609229.

42)       Portelius E, Olsson B, Hoglund K, Cullen NC, Kvartsberg H, Andreasson U, Zetterberg H, Sandelius A, Shaw LM, Lee VMY, Irwin DJ, Grossman M, Weintraub D, Chen-Plotkin A, Wolk DA, McCluskey L, Elman L, McBride J, Toledo JB, Trojanowski JQ, Blennow K. Cerebrospinal fluid neurogranin concentration in neurodegeneration: relation to clinical phenotypes and neuropathology. Acta Neuropathol;10.1007/s00401-018-1851-x, 2018.

43)       O’Bryant SE, Gupta V, Henriksen K, Edwards M, Jeromin A, Lista S, Bazenet C, Soares H, Lovestone S, Hampel H, Montine T, Blennow K, Foroud T, Carrillo M, Graff-Radford N, Laske C, Breteler M, Shaw L, Trojanowski JQ, Schupf N, Rissman RA, Fagan AM, Oberoi P, Umek R, Weiner MW, Grammas P, Posner H, Martins R, Star B, groups Bw. Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer’s disease research. Alzheimers Dement 11(5):549-60, 2015; PMCID: PMC4414664.

44)       Lewczuk P, Riederer P, O’Bryant SE, Verbeek MM, Dubois B, Visser PJ, Jellinger KA, Engelborghs S, Ramirez A, Parnetti L, Jack CR, Jr., Teunissen CE, Hampel H, Lleo A, Jessen F, Glodzik L, de Leon MJ, Fagan AM, Molinuevo JL, Jansen WJ, Winblad B, Shaw LM, Andreasson U, Otto M, Mollenhauer B, Wiltfang J, Turner MR, Zerr I, Handels R, Thompson AG, Johansson G, Ermann N, Trojanowski JQ, Karaca I, Wagner H, Oeckl P, van Waalwijk van Doorn L, Bjerke M, Kapogiannis D, Kuiperij HB, Farotti L, Li Y, Gordon BA, Epelbaum S, Vos SJB, Klijn CJM, Van Nostrand WE, Minguillon C, Schmitz M, Gallo C, Lopez Mato A, Thibaut F, Lista S, Alcolea D, Zetterberg H, Blennow K, Kornhuber J. Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry. World J Biol Psychiatry 19(4):244-328, 2018; PMCID: PMC5916324.

45)       Henriksen K, O’Bryant SE, Hampel H, Trojanowski JQ, Montine TJ, Jeromin A, Blennow K, Lonneborg A, Wyss-Coray T, Soares H, Bazenet C, Sjogren M, Hu W, Lovestone S, Karsdal MA, Weiner MW, Blood-Based Biomarker Interest G. The future of blood-based biomarkers for Alzheimer’s disease. Alzheimers Dement 10(1):115-31, 2014; PMCID: PMC4128378.

46)       Mattsson N, Andreasson U, Persson S, Carrillo MC, Collins S, Chalbot S, Cutler N, Dufour-Rainfray D, Fagan AM, Heegaard NH, Robin Hsiung GY, Hyman B, Iqbal K, Kaeser SA, Lachno DR, Lleo A, Lewczuk P, Molinuevo JL, Parchi P, Regeniter A, Rissman RA, Rosenmann H, Sancesario G, Schroder J, Shaw LM, Teunissen CE, Trojanowski JQ, Vanderstichele H, Vandijck M, Verbeek MM, Zetterberg H, Blennow K. CSF biomarker variability in the Alzheimer’s Association quality control program. Alzheimers Dement 9(3):251-61, 2013; PMCID: PMC3707386.

47)       Toledo JB, Shaw LM, Trojanowski JQ. Plasma amyloid beta measurements – a desired but elusive Alzheimer’s disease biomarker. Alzheimer’s research & therapy 5(2):8, 2013; PMCID: PMC3706955.

48)       Toledo JB, Korff A, Shaw LM, Trojanowski JQ, Zhang J. Low levels of cerebrospinal fluid complement 3 and factor H predict faster cognitive decline in mild cognitive impairment. Alzheimer’s research & therapy 6(3):36, 2014; PMCID: Pmc4255518.

49)       McMillan CT, Avants B, Irwin DJ, Toledo JB, Wolk DA, Van Deerlin VM, Shaw LM, Trojanoswki JQ, Grossman M. Can MRI screen for CSF biomarkers in neurodegenerative disease? Neurology 80(2):132-8, 2013; PMCID: PMC3589187.

50)       Lleo A, Irwin DJ, Illan-Gala I, McMillan CT, Wolk DA, Lee EB, Van Deerlin VM, Shaw LM, Trojanowski JQ, Grossman M. A 2-Step Cerebrospinal Algorithm for the Selection of Frontotemporal Lobar Degeneration Subtypes. JAMA neurology 75(6):738-45, 2018; PMCID: PMC5885205.

51)       Spotorno N, McMillan CT, Irwin DJ, Clark R, Lee EB, Trojanowski JQ, Weintraub D, Grossman M. Decision-Making Deficits Associated with Amyloidosis in Lewy Body Disorders. Front Hum Neurosci 10:693, 2016; PMCID: PMC5225123.

52)       Grossman M, Elman L, McCluskey L, McMillan CT, Boller A, Powers J, Rascovsky K, Hu W, Shaw L, Irwin DJ, Lee VM, Trojanowski JQ. Phosphorylated tau as a candidate biomarker for amyotrophic lateral sclerosis. JAMA neurology 71(4):442-8, 2014; PMCID: PMC3989393.

Please refer to the following manuscripts for comparison with ADNI data files:

• ADNI 1 Procedures Manual Alterations in metabolic pathways and networks in Alzheimer’s disease (2013)
• Ante Mortem Cerebrospinal Fluid Tau Levels Correlate With Postmortem Tau Pathology in Frontotemporal Lobar Degeneration (2017)
• Cerebrospinal fluid a-synuclein contributes to the differential diagnosis of Alzheimer’s disease (2018)
• Cerebrospinal fluid neurogranin concentration in neurodegeneration: relation to clinical phenotypes and neuropathology (2017)
• Comparing metabolomic and pathologic biomarkers alone and in combination for discriminating Alzheimer’s disease from normal cognitive aging (2013)
• CSF biomarkers cutoffs: the importance of coincident neuropathological diseases (2012)
• CSF tau and amyloid-beta predict cerebral synucleinopathy in autopsied Lewy body disorders (2018)

CUT POINTS

• Completion and upload to LONI the Methods Report, “ADNI3: Batch analyses of Ab42, t-tau, and p-tau181 in ADNI1, GO, 2 CSF samples using the fully automated Roche Elecsys and cobas e immunoassay analyzer system”.  This dataset, uploaded April 2017,  includes a total of 2,401 never-before-thawed aliquots of ADNI1, GO and 2 CSF samples that had been collected between 9/7/2005 and 7/25/2016.  See PPT set #101 for a description of major parts of the method validation for Ab42, and some cut-point estimations and see PPT set # 102 for further analyses, determinations of cut-points and relationships of “abnormal” and “normal” biomarker results to cognitive decline and progression from MCI to AD dementia done so far and working toward:
  • definition of cut-points for Ab1-42, t-tau, p-tau181 and the ratios, Ab1-42/t-tau and Ab1-42/p-tau181 using Mixture Modeling and ROC analyses;
  • an understanding of the predictive performance for cognitive decline and progression of MCI participants to a clinical diagnosis of AD dementia using these cut-points;

• Assessments of the comparisons between Ab-|tau-, Ab-|tau+, Ab+|tau-, and Ab+|tau+ for predictive performance of each pair [Ab+ is below CSF cut-point value; Ab- is at or above CSF cut-point value; tau- is below and tau+ is at or above the cut-point value for CSF tau; analogous pairs for Ab and p-tau181] for cognitive, memory and functional decline and progression from MCI to a clinical diagnosis of AD.

• • assessments of concordance between CSF Ab1-42, t-tau, p-tau181, the ratios, Ab1-42/t-tau and Ab1-42/p-tau181 and Florbetapir PET imaging-based plaque burden assessments.
  • The inclusion of validated CSF Ab40 to Ab1-42, t-tau, and p-tau181 in ADNI3 will permit evaluation of the Ab1-42/Ab40 ratio for possible improvement over Ab1-42 alone for clinical utility.

 A manuscript describing the concordance performance of ADNIGO/2 Roche Elecsys CSF Ab42, t-tau and p-tau181 biomarker data and that from the Swedish BioFINDER study with either Florbetapir PET or Flutemetamol amyloid PET imaging, respectively, in the respective study cohorts, has been accepted for publication (Hansson et al, 2018b).

Provided support for the development of new immunoassays for CSF Ab42, t-tau, p-tau181 by providing residual CSF aliquot samples to 3 vendor laboratories.

Figure 2. Timing for the onset and progression of AD

The figure below illustrates the timing for the onset and progression of the AD in the upper right panel with examples of mixed pathologies found in AD brains in the upper left panel while the lower panel summarizes the timing of pathology deposition and neuron death as well as current considerations for the treatment of AD. This figure is from a recently published update of earlier ADNI reviews from Kang et al, 2015, that provides our current understanding of the hypothetical timeline for the onset and progression of Alzheimer’s Disease neurodegeneration and cognitive impairments progressing from normal to mild cognitive impairment and then to Alzheimer’s disease dementia.

STANDARDIZATION

Leslie M Shaw and John Q Trojanowski

Standardization of measurement methods for AD biomarkers in biofluids:

CSF

• Analyses of CSF for A?1-42, t-tau and p-tau181 moved from the Research-Use-Only Fujirebio AlzBio3 xMAP bead-based immunoassay to the fully automated Roche Elecsys® platform following extensive validation studies (Bittner, 2016; Shaw, 2016) and for A?42, comparisons with validated reference method LC/MSMS using the primary reference standard preparation of A?42, provided by the Institute for Reference Materials and Measurements (IRMM) following finalization of replicate amino acid analyses(Korecka, 2014; Shaw, 2016; Kuhlman, 2017).  This transition to the fully automated Elecsys® platform supports an important aim for the ADNI Biomarker Core in the ADNI3 grant renewal namely to support the implementation of AD biomarkers in future clinical trials making use of a highly validated methodology with robust, reproducible cross-laboratory performance standardization that will enable effective implementation of universal cut-point values (Hansson, 2018; Schindler, 2018).  Starting with the CSF samples collected from ADNI3 participants A?1-40 will be added to A?1-42, t-tau and p-tau181 in order to provide measurement of the A?1-42/A?1-40 ratio in each ADNI3 CSF sample.  In addition, the Biomarker core provided Roche Elecsys-based A?1-42, A?1-40, t-tau and p-tau181 measurements in all DIAN participant CSF samples as part of a collaborative study between DIAN and ADNI.  A subset of ADNIGO/2 CSF samples were also analyzed as part of this study and results are summarized in a Methods report posted April, 2018.
• Implementation of a fully validated reference LC/MSMS method for CSF A?42 and further validated for CSF A?40 and A?38 (see Korecka, 2014, Panee, 2016; Kuhlman, 2016) for measurements in all ADNIGO/2 BASELINE and LONGITUDINAL CSF samples over a total of 70 analytical runs. The Methods document and dataset for these analyses were uploaded to the LONI ADNI website, June, 2018.  The methods document includes frequency plots for each analyte and for the A?42/A?40 ratio and makes use of cut-points based on ROC analysis wherein FBP PET served as the AD biomarker, and documents major analytical factors including precision performance and experience and included one change in the lot of calibration standards (Korecka, 2018).
• Important NOTE: The preparation of Certified Reference Material for CSF A?1-42 was completed late 2017 (Kuhlmann, 2017, Certification report). A number of immunoassay vendors are collaborating pre-competitively on the use of this material for adjusting their calibrators based on the three prepared human CSF pools whose A?1-42 concentrations were established by participating reference laboratories each using validated LC/MSMS methods including two JCTLM-listed reference methods (Korecka, 2014; Leinenbach, 2014).  The purpose of this effort is to achieve harmonization across the different immunoassay platforms.  In concert with this effort the ADNI Biomarker Core laboratory will make use of the three CRMs to adjust all ADNI GO/2 CSFs that were measured by a reference UPLC/MSMS method (Korecka, 2014) and we plan to collaborate with Roche on adjustment of all ADNI CSF A?1-42 values based on the CRMs and this will assure that ADNI CSF A?1-42 data are based on this common CRM-based standardization process.

A?1-40

• Recently a number of studies (Lewczuk,2017; Janeldze S, 2016; 2017; Niemantsverdriet, 2017) have reported that compared to CSF A?1-42 alone, the CSF A?1-42/A?1-40 ratio might improve:

1. The prediction accuracy of Alzheimer’s disease (AD) in MCI study participants
2. Discrimination of AD from other forms of dementia
3. Concordance between CSF and PET amyloidosis

The statistical analyses of this ADNI dataset as reported in the Methods document focused on confirming the improved detection of amyloid pathology when using the CSF A?1-42/A?1-40 ratio vs CSF A?1-42 alone based on improved concordance between the A?1-42/A?1-40 ratio and FBP amyloid PET compared to CSF A?1-42 alone.  A secondary focus of the statistical analyses was assessment of the diagnostic utility of A?1-42/A?1-38 ratio.  An improved concordance from 83% to 89% was observed for the A?1-42/A?1-40 ratio as described in the Methods document.  Similar degree of improvement was observed for the A?1-42/A?1-38 ratio.  Further studies are required to determine the potential added value of A?1-38.

Given the heightened interest in and growing number of studies showing the improved utility of the A?1-42/A?1-40 ratio, an effort is planned to prepare CRMs for A?1-40 analogous to what was done for A?1-42 and we will participate in this effort as well.  In the ADNI3 phase we are adding A?1-40 to A?1-42, t-tau and p-tau181 measurements using the Roche Elecsys® platform in CSFs collected in ADNI3.

Pre-analytical factors:

• Another key element of overall standardization of CSF AD biomarker measurements is recognition and control of pre-analytical factors including (1) timing of CSF sampling; (2) location of sampling and volume of CSF; (3) type of puncture needle; (4) collection method (gravity drip or syringe pull); (5) blood contamination; (6) type of plastic CSF comes into contact with; (7) the number of transfer steps; (8) additives; (9) shaking/mixing; (10) heat; (11) centrifugation; (12) storage temperature; (13) the # of freeze/thaw cycles; (14) storage time at -80 ⁰ These factors were considered in a recent review and recommendations for future study were made including a proposed collaborative study that includes a unified CSF collection protocol (Hansson, 2018).  Many investigators have contributed to these studies of pre-analytical factors and all are acknowledged in Hansson, 2018 and will not be tabulated here due to the need for conservation of space.  In the ADNI study the recommended time for LP, (as well as blood draws for serum and plasma collections), is in the morning following an overnight fast; for LPs, based on improved patient safety, the use of a blunt tipped atraumatic Sprotte needle is recommended-22 g and gravity drip, although 24 g Sprotte and syringe pull without use of plastic catheter tubing is permitted and favored by a number of experienced ADNI clinical sites.  Since tube transfer has been shown to reduce CSF A?1-42 concentration, keeping this to a minimum in the preparation of aliquots is recommended.  In the ADNI study this factor is reduced to a minimum for both gravity drip and syringe pull as described in the ADNI3 Biomarker Sample Collection, Processing and Shipmenten documnet .When CSF aliquots are prepared in the Biomarker core laboratory, an additional transfer step is undertaken in order to permit mixing of the two tubes of CSF and eliminate any possible gradient effects.   All ADNI CSF samples undergo one thaw-freeze cycle in the preparation of 0.5 mL aliquots in the Biomarker core laboratory, thus one additional freeze-thaw cycle.  Available study data suggests that up to 2 freeze-thaw cycles will not result in diminished CSF A?1-42 concentration (Lewczuk, 2017). Further description of the time dynamics for CSF is available in the “BIOFLUID BANKING” section.

Plasma

• There is increased interest in the development and validation of plasma-based biomarkers for at least as a screening test to detect AD pathology. Recent studies show promise that new approaches to measurements of A?1-42 and A?1-40 can detect AD with AUC values approaching 0.9 (Ovod, 2017; Nakamura, 2018) when amyloid PET is used as the standard of comparison, whereas in the past results across 26 studies using then-available immunoassays were not consistent for reliable detection of AD (Rissman, 2012).  New and improved mass spectrometry based methods (Ovod, 2017; Nakamura, 2018) and immunoassays (Song, 2018) are now being further evaluated with the promise that one or more of these will prove to be rugged and reliable for use as screening tests in the context of treatment trials as well as in the clinic.  Essential to progress in development of rugged and reliable AD biomarkers in plasma is recognition and control of pre-analytical factors that affect the concentration measurements.
• A proposed set of guidelines for the standardization of pre-analytic variables was recently published (O’Bryant et al, 2015). A review of plasma and serum processing experience in the ADNI study is available in the “Biofluid Banking” section.
• With the acceleration of studies and assessments of various platforms and biomarker analytes a key need is for determinations and definitions for best practice pre-analytical procedures including sample processing time and temperature, number of freeze-thaw cycles, plastic tube type, volume of sample aliquot in relationship to tube volume, sample stability at different temperatures, the need to check for and identification of other factors such as the presence of HAMA(human anti-mouse antibodies) that are well known to cause interference in various immunoassays in routine laboratory testing(Sturgeon, 2018), and hemoglobin.
• A recently published study (Keshavan, 2018) described the effect of repeated freeze-thaw cycles on plasma A?1-42, A?1-40, t-tau and serum NFL using the Quanterix high sensitivity platform. Up to four freeze-thaw cycles did not influence concentrations of these biomarkers to a significant degree with at most minor reductions in A?40 after the 4^th The authors recommended that for measurements that include A?40 be limited to no more than 2 freeze-thaw cycles.  In our view this study suggests that careful documentation of the effect of freeze-thaw is needed for each analytical methodology as these investigators have done for the Simoa (Quanterix) platform.
• Round robin studies will be an essential step for determination of the comparative utilities and robustness of these new biomarker tests and we look forward to participation in these.
• The Alzheimer’s Association Global Biomarkers Standardization Consortium (GBSC) is actively considering addition of blood based biomarkers to the CSF QC program that is being run by Kaj Blennow and colleagues in Gothenburg Sweden.

REFERENCES

Bittner T, Zetterberg H, Teunissen CE, Ostlund RE, Jr., Militello M, Andreasson U et al (2016) Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of beta-amyloid (1-42) in human cerebrospinal fluid. Alzheimers Dement 12:517–526.  PMID: 26555316

Shaw LM, Fields L, Korecka M, Waligorksa T, Trojanowski JQ, Allegranza D, et al. Method comparison of A?(1-42) measured in human cerebrospinal fluid samples by liquid chromatography-tandem mass spectrometry, the INNO-BIA AlzBio3 assay, and the Elecsys® ?-Amyloid(1-42) assay. Alzheimers Dement 2016;12:668.

Korecka M, Waligorska T, Figurski M, Toledo JB, Arnold SE, Grossman M, Trojanowski JQ, Shaw LM. Qualification of a surrogate matrix-based absolute quantification method for amyloid-??? in human cerebrospinal fluid using 2D UPLC-tandem mass spectrometry. Journal of Alzheimer’s disease: JAD. 2014;41(2):441-51. PMID: 24625802. {C12RMP1}

Korecka M, Figurski M, Fields L, Trojanowski JQ, Shaw LM.  2D-UPLC tandem mass spectrometry measurement of A?1-42, A?1-40 and A?1-38 in ADNI2 and ADNIGO CSF.  ADNI Methods document, 6/20/2018.

Leinenbach A, Pannee J, Dulffer T, Huber A, Bittner T, Andreasson U, Gobom J, Zetterberg H, Kobold U, Portelius E, Blennow K. Mass spectrometry-based candidate reference measurement procedure for quantification of amyloid-beta in cerebrospinal fluid. Clin Chem. 2014; 60:987–994. PMID: 24842955. {C11RMP9}

Kuhlmann J, Boulo S, Andreasson U, Bierke M, Pannee J, Charoud-Got J, et al.  The certification of Amyloid ?1-42 in CSF ERM®-DA481/IFCC and ERM®-DA482/IFCC. 2018 Reference Materials Report, JRC 107381.

Kuhlmann J, Andreasson U, Pannee J, Bjerke M, Portelius E, etal.  CSF A?1-42-an excellent but complicated Alzheimer’s biomarker-a route to standardization.  Clin Chim Acta 2017; 467: 27-33.

Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, Lifke V, Corradini V, Eichenlaub U, Batrla R, Buck K, Zink K, Rabe C, Blennow K, Shaw LM.  CSF biomarkers of Alzheimer’s disease concord with amyloid-beta PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 2018; https://doi.org/10.1016/j.jalz.2018.01.010 [Epub ahead of print].

Schindler SE, Gray JD, Gordon BA, Xiong C, Bartrla-Uttermann, Quan M, Wahl S, Benzinger TLS, Holtzman DM, Morris JC, Fagan AM.  Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging.  Alzheimers Dement. 2018; in press, https://doi.org/10.1016/j.jalz.2018.01.013

Hansson O, Mikulskis A, Fagan AE, Teunissen C, Zetterberg H, Vanderstichele H, Molinuevo JL, Shaw LM, Vandijck M, Berbeek MM, Savage M, Mattsson N, Lewczuk P, Batrla R, Rutz S, Dean RA, Blennow K.  The impact of preanalytical variables on measureing CSF biomarkers for Alzheimer’s disease diagnosis: a review.  Alzheimer’s Dementia 2018, in press, https://doi.org/10.1016/j.alz.2018.05.008.

Lewczuk P, Matzen A, Blennow K, Parnetti L, Molinuevo JL, Eusebi P, Kornhuber J, Morris JC, Fagan AM. Cerebrospinal fluid Ab42/40 corresponds better than Ab42 to amyloid PET in Alzheimer’s disease. J AlzheimersDis 2017;55:813-822. PMID: 27792012

Janeldze S, Pannee J, Mikulskis A, Chiao P, Zetterberg H, Blennow K, Hansson O. Concordance between different amyloid immunoassays and visual amyloid positron emission tomographic assessment. JAMA Neurol 2017;74:1492-1501. PMID: 29114726

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BIOFLUID BANKING

Leslie M Shaw and John Q Trojanowski

The Biomarker Core continues to receive, aliquot, store and curate all biofluid samples (CSF, plasma, serum) collected from subjects enrolled in ADNI3, including all who “carry over” from ADNIGO/2 and all newly enrolled individuals, with 24/7 surveillance in the ADNI freezers (-80 ⁰C) that are housed in secure, dedicated space at UPENN. The updated (as of June 4, 2018) list of pristine aliquots of CSF, plasma and serum samples collected from ADNI subjects, can be found on the ADNI LONI website under BIOSPECIMENS, but below in Table 1 is a brief summary of these samples.

Table 1. Summary of ADNI CSF, plasma and serum samples received and aliquots prepared as of 7/18/2018.

Biomarker Core staff continue to monitor details involved in the preparation of all Biofluid samples at study sites including time from obtaining each sample to the time of freezing on dry ice (a summary of this is provided in Figure 1 below). We continue to review details involved in the pre-analytical steps involved in biofluid sample collections. In the figure below a focus on sample preparation time shows that for CSF the mean, 95%CI and median values across 1,318 samples collected from ADNIGO/2 phase participants is: 44.8 min, (41.7-47.8 min) and 28 min respectively. For 3,908 plasma samples the respective values are: 71.7 min, (70.0-73.4 min) and 55 min. This information is available for each ADNI sample. The handling at each ADNI site of these biofluid samples is very important to assure the quality of each sample. Avoidance of hemolysis and time-efficient sample preparation are essential to the goal of sample quality. For plasma, the recommended time from collection to freezing is no longer than 120 min; for CSF the recommended time is 60 min or less in order to minimize risk for biomarker degradation due to various metabolic processes.

Figure 1. Sample collection to freezing time for ADNI GO/2 plasma and CSF primary samples

Biomarker Core staff regularly communicate with individual sites and Clinical Core staff regarding biofluid collections and any issues concerning sample quality, labeling discrepancies, and provision of updated samples-received summaries.

We are continuing our collaborative studies on identifying and controlling pre-analytical factors that can contribute to variability in CSF or plasma biomarker measurements especially A?1-42 (Vanderstichele, 2011; O’Bryant, 2015; Hansson, 2018). An example of this is a world-wide collaborative effort under the auspices of the Alz Association Global Biomarker Standardization Consortium(GBSC) whose members from industry and academic centers are defining a unified protocol for CSF sample collection for use in routine clinical practice that does not require freezing of CSF and another for large multicenter studies such as ADNI, wherein freezing bio-fluid samples prepared at 59 study sites and shipment to the Biomarker core laboratory is essential for assurance of sample stability for the long-term.  The latter is also consistent with international treatment trials wherein long-term storage is an essential aspect for biomarker studies in bio-fluids.  A new effort is the Biomarkers Consortium NSC – Plasma A? Working Group that was recently organized to pursue various aspects of plasma A? measurement.  Although there have been mixed results for plasma A?42/A?40 for accurate detection of AD using a number of immunoassay approaches, it is fervently hoped that by identifying and controlling pre-analytical factors, improved analytical techniques, and controlling for concomitant disease factors(Rissman, 2012) that progress can be made on improving the diagnostic utility of these measurements (Ovod, 2017; Nakamura, 2018; Song, 2018). We will provide updates of these developments at the annual ADNI Steering Committee meetings and FTF meetings with our ADNI PPSB colleagues.

RARC-approved studies using ADNI Biofluids

Biomarker Core staff prepare and ship bio-fluid samples(CSF, plasma or serum) to all investigators whose biomarker study proposals have been approved by the RARC (Resource Allocation Review Committee, appointed by the NIA) and following final review by NIA (see Table summarizing ADNI Biofluid Samples Sent to Investigators for RARC-Approved Studies for an up to date brief summary of these studies and status of results upload). Once completed the data from these studies, performed blinded, are uploaded on the LONI/ADNI website together with a Methods document that describes the methodology involved and quality control performance. Biomarker Core faculty, Drs Trojanowski and Shaw are happy to provide input on any study although this is not required but often we are asked. The procedure for making an application to the Resource Allocation Review Board(RARC) for ADNI biofluid aliquot samples is available on the Access Sample page.

A study that builds upon the 2013-2014 proteomic study that used LC/MSMS mrm mass spectrometry methodology will be conducted in 2018 to determine accurate concentration values for 5 candidate biomarkers that showed promise in the earlier semi-quantitative-based study (see Table 2 for a synopsis of all of these studies). An important characteristic of this study is its emphasis on measurements in longitudinal CSF samples across time from entry into the ADNI study (BASELINE) to at least 4 years later in order to assess these biomarker trajectories. Such data can be informative to clinical trials that are seeking to use biomarkers as indices of drug engagement and drug effect.

We continue to collaborate with biomarker scientists at UPenn and elsewhere (Irwin, 2017, 2018; Hu, 2010, 2015; Toledo, 2013, 2018; Mattsson, 2013, 2016; Zetterberg and Blennow, 2016). New CSF biomarker-neuropathology correlations done with UPenn collaborators resulted in studies involving CSF tau and histochemical tau in FTLD and CSF tau and A?42 and synucleinopathy in autopsied Lewy Body disorders (Irwin etal, 2017, 2018). Such studies take advantage of the large set of CSF collected at UPenn from individuals prior to death and for whom an autopsy diagnosis provides accurate detection of not only AD neuropathology but concomitant pathologies such as Lewy Bodies, TDP-43 deposits, hippocampal sclerosis. A list of the publications describing the results of these studies thus far is included in a more detailed discussion in “Alzheimer’s Disease Neuroimaging (ADNI) Related Biofluid and Genetic Biomarker Studies Using non-ADNI UPenn Biosamples Followed Longitudinally.”