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Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures

Nature Medicine volume 27, pages 1034–1042 (2021)Cite this article

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Abstract

A combination of plasma phospho-tau (P-tau) and other accessible biomarkers might provide accurate prediction about the risk of developing Alzheimer’s disease (AD) dementia. We examined this in participants with subjective cognitive decline and mild cognitive impairment from the BioFINDER (n = 340) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n = 543) studies. Plasma P-tau, plasma Aβ42/Aβ40, plasma neurofilament light, APOE genotype, brief cognitive tests and an AD-specific magnetic resonance imaging measure were examined using progression to AD as outcome. Within 4 years, plasma P-tau217 predicted AD accurately (area under the curve (AUC) = 0.83) in BioFINDER. Combining plasma P-tau217, memory, executive function and APOE produced higher accuracy (AUC = 0.91, P < 0.001). In ADNI, this model had similar AUC (0.90) using plasma P-tau181 instead of P-tau217. The model was implemented online for prediction of the individual probability of progressing to AD. Within 2 and 6 years, similar models had AUCs of 0.90–0.91 in both cohorts. Using cerebrospinal fluid P-tau, Aβ42/Aβ40 and neurofilament light instead of plasma biomarkers did not improve the accuracy significantly. The clinical predictions by memory clinic physicians had significantly lower accuracy (4-year AUC = 0.71). In summary, plasma P-tau, in combination with brief cognitive tests and APOE genotyping, might greatly improve the diagnostic prediction of AD and facilitate recruitment for AD trials.

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Fig. 1: Model selection process and performance for predicting AD dementia within 4 years.
Fig. 2: Comparison with the clinical prediction for predicting AD dementia within 4 years.
Fig. 3: Model selection and performance in ADNI for predicting AD dementia within 4 years with comparisons using models selected in BioFINDER.
Fig. 4: Cross-validation and implementation of an algorithm.

Data availability

For BioFINDER data, anonymized data will be shared by request from a qualified academic investigator for the sole purpose of replicating procedures and results presented in the article and as long as data transfer is in agreement with EU legislation on the general data protection regulation and decisions by the Ethical Review Board of Sweden and Region Skåne, which should be regulated in a material transfer agreement. ADNI data are stored (publicly available) in the loni database (https://ida.loni.usc.edu/).

Code availability

No custom code or mathematical algorithm that was central to the conclusions was used in this study.

References

  1. Palmqvist, S. et al. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid β-amyloid 42: a cross-validation study against amyloid positron emission tomography. JAMA Neurol. 71, 1282–1289 (2014).

    Article  PubMed  Google Scholar 

  2. Janelidze, S. et al. CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios: better diagnostic markers of Alzheimer disease. Ann. Clin. Transl. Neurol. 3, 154–165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Barthelemy, N. R. et al. Cerebrospinal fluid phospho-tau T217 outperforms T181 as a biomarker for the differential diagnosis of Alzheimer’s disease and PET amyloid-positive patient identification. Alzheimers Res. Ther. 12, 26 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Mattsson, N. et al. Aβ deposition is associated with increases in soluble and phosphorylated tau that precede a positive tau PET in Alzheimer’s disease. Sci. Adv. 6, eaaz2387 (2020).

    Article  Google Scholar 

  5. Mattsson-Carlgren, N. et al. The implications of different approaches to define AT(N) in Alzheimer disease. Neurology 94, e2233–e2244 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mattsson, N., Palmqvist, S., Stomrud, E., Vogel, J. & Hansson, O. Staging β-amyloid pathology with amyloid positron emission tomography. JAMA Neurol. https://doi.org/10.1001/jamaneurol.2019.2214 (2019).

  7. Rabinovici, G. D. et al. Association of amyloid positron emission tomography with subsequent change in clinical management among Medicare beneficiaries with mild cognitive impairment or dementia. JAMA 321, 1286–1294 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Leuzy, A. et al. Diagnostic performance of RO948 F 18 tau positron emission tomography in the differentiation of Alzheimer disease from other neurodegenerative disorders. JAMA Neurol. 77, 955–965 (2020).

  9. Ossenkoppele, R. et al. Discriminative accuracy of [18F]flortaucipir positron emission tomography for Alzheimer disease vs other neurodegenerative disorders. JAMA 320, 1151–1162 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gisslen, M. et al. Plasma concentration of the neurofilament light protein (NFL) is a biomarker of CNS injury in HIV infection: a cross-sectional study. EBioMedicine 3, 135–140 (2016).

    Article  PubMed  Google Scholar 

  11. Preische, O. et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277–283 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Palmqvist, S. et al. Performance of fully automated plasma assays as screening tests for Alzheimer disease-related β-amyloid status. JAMA Neurol. 76, 1060–1069 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Schindler, S. E. et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology 93, e1647–e1659 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Janelidze, S. et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat. Med. 26, 379–386 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Karikari, T. K. et al. 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. 19, 422–433 (2020).

    Article  CAS  PubMed  Google Scholar 

  16. Palmqvist, S. et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. JAMA 324, 772–781 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Cullen, N. C. et al. Individualized prognosis of cognitive decline and dementia in mild cognitive impairment based on plasma biomarker combinations. Nat. Aging 1, 114–123 (2021).

  18. Jack, C. R. Jr. et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimers Dement. 13, 205–216 (2017).

    Article  PubMed  Google Scholar 

  19. Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Soc. Methods Res. https://doi.org/10.1177/0049124104268644 (2004).

  20. Olofsen, E. & Dahan, A. Using Akaike’s information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study. F1000Research 2, 71 (2013).

    Article  PubMed  Google Scholar 

  21. Toledo, J. B. et al. Factors affecting Aβ plasma levels and their utility as biomarkers in ADNI. Acta Neuropathol. 122, 401–413 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ovod, V. et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 13, 841–849 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Borland, E. et al. The Montreal Cognitive Assessment: normative data from a large Swedish population-based cohort. J. Alzheimers Dis. 59, 893–901 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Borland, E., Stomrud, E., van Westen, D., Hansson, O. & Palmqvist, S. The age-related effect on cognitive performance in cognitively healthy elderly is mainly caused by underlying AD pathology or cerebrovascular lesions: implications for cutoffs regarding cognitive impairment. Alzheimers Res. Ther. 12, 30 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Petrazzuoli, F. et al. Brief cognitive tests used in primary care cannot accurately differentiate mild cognitive impairment from subjective cognitive decline. J. Alzheimers Dis. 75, 1191–1201 (2020).

  26. Barthelemy, N. R., Horie, K., Sato, C. & Bateman, R. J. Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer’s disease. J. Exp. Med. 217, e20200861 (2020).

  27. Karikari, T. K. et al. Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the Alzheimer’s Disease Neuroimaging Initiative. Mol. Psychiatry 26, 429–442 (2020).

  28. Mattsson-Carlgren, N. et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease. Brain 143, 3234–3241 (2020).

  29. Mielke, M. M. et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 14, 989–997 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Palmqvist, S. et al. Cerebrospinal fluid and plasma biomarker trajectories with increasing amyloid deposition in Alzheimer’s disease. EMBO Mol. Med. 11, e11170 (2019).

  31. Insel, P. S. et al. Determining clinically meaningful decline in preclinical Alzheimer disease. Neurology 93, e322–e333 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Palmqvist, S. et al. Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer’s disease: cross-validation study of practical algorithms. Alzheimers Dement. 15, 194–204 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Papp, K. V., Rentz, D. M., Orlovsky, I., Sperling, R. A. & Mormino, E. C. Optimizing the preclinical Alzheimer’s cognitive composite with semantic processing: the PACC5. Alzheimers Dement. 3, 668–677 (2017).

    Article  Google Scholar 

  34. Jansen, W. J. et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 313, 1924–1938 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Scheyer, O. et al. Female sex and Alzheimer’s risk: the menopause connection. J. Prev. Alzheimers Dis. 5, 225–230 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Jack, C. R. Jr. et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Petersen, R. C. Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256, 183–194 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Roberts, R. O. et al. The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology 30, 58–69 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Gothlin, M., Eckerstrom, M., Rolstad, S., Wallin, A. & Nordlund, A. Prognostic accuracy of mild cognitive impairment subtypes at different cut-off levels. Dement. Geriatr. Cogn. Disord. 43, 330–341 (2017).

    Article  PubMed  Google Scholar 

  40. Sato, C. et al. Tau kinetics in neurons and the human central nervous system. Neuron 97, 1284–1298 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lopponen, M., Raiha, I., Isoaho, R., Vahlberg, T. & Kivela, S. L. Diagnosing cognitive impairment and dementia in primary health care—a more active approach is needed. Age Ageing 32, 606–612 (2003).

    Article  PubMed  Google Scholar 

  42. Valcour, V. G., Masaki, K. H., Curb, J. D. & Blanchette, P. L. The detection of dementia in the primary care setting. Arch. Intern. Med. 160, 2964–2968 (2000).

    Article  CAS  PubMed  Google Scholar 

  43. Åstrand, R., Rolstad, S. & Wallin, A. Cognitive Impairment Questionnaire (CIMP-QUEST): reported topographic symptoms in MCI and dementia. Acta Neurol. Scand. 121, 384–391 (2009).

  44. Pfeffer, R. I., Kurosaki, T. T., Harrah, C. H. Jr., Chance, J. M. & Filos, S. Measurement of functional activities in older adults in the community. J. Gerontol. 37, 323–329 (1982).

    Article  CAS  PubMed  Google Scholar 

  45. Rosen, W. G., Mohs, R. C. & Davis, K. L. A new rating scale for Alzheimer’s disease. Am. J. Psychiatry 141, 1356–1364 (1984).

    Article  CAS  PubMed  Google Scholar 

  46. Shulman, K. I. Clock-drawing: is it the ideal cognitive screening test? Int J. Geriatr. Psychiatry 15, 548–561 (2000).

    Article  CAS  PubMed  Google Scholar 

  47. Palmqvist, S. et al. Comparison of brief cognitive tests and CSF biomarkers in predicting Alzheimer’s disease in mild cognitive impairment: six-year follow-up study. PLoS ONE 7, e38639 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Mattsson-Carlgren, N., Palmqvist, S., Blennow, K. & Hansson, O. Increasing the reproducibility of fluid biomarker studies in neurodegenerative studies. Nat. Commun. 11, 6252 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hansson, O. et al. CSF biomarkers of Alzheimer’s disease concord with amyloid-β PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 14, 1470–1481 (2018).

  50. Palmqvist, S. et al. Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat. Commun. 8, 1214 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  51. McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–269 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Aisen, P. S., Petersen, R. C., Donohue, M. & Weiner, M. W. & Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s Disease Neuroimaging Initiative 2 Clinical Core: progress and plans. Alzheimers Dement. 11, 734–739 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  53. McKhann, G. et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944 (1984).

    Article  CAS  PubMed  Google Scholar 

  54. Landau, S. M. et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann. Neurol. 72, 578–586 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Work at the authors’ research center was supported by the Swedish Research Council (2016-00906 (O.H.) and 2018-02052 (S.P.)), the Knut and Alice Wallenberg Foundation (2017-0383 (O.H.)), the Marianne and Marcus Wallenberg Foundation (2015.0125 (O.H.)), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-745911 (O.H.) and AF-940046 (S.P.)), the Swedish Brain Foundation (FO2019-0326 (O.H.) and FO2020-0271 (S.P.)), the Parkinson Foundation of Sweden (1280/20 (O.H.)), the Skåne University Hospital Foundation (2020-O000028 (S.P.)), Regionalt Forskningsstöd (2020-0314 (O.H.) and 2020-0383 (S.P.)) and the Swedish federal government under the ALF agreement (2018-Projekt0279 (O.H.) and 2018-Projekt0226 (S.P.)). H.Z. is a Wallenberg Scholar supported by grants from the Swedish Research Council (2018-02532), the European Research Council (681712), Swedish State Support for Clinical Research (ALFGBG-720931), the Alzheimer Drug Discovery Foundation, USA (201809-2016862), AD Strategic Fund and the Alzheimer’s Association (ADSF-21-831376-C, ADSF-21-831381-C and ADSF-21-831377-C), the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie grant agreement no. 860197 (MIRIADE) and the UK Dementia Research Institute at University College London. K.B. is supported by the Swedish Research Council (2017-00915), the Alzheimer Drug Discovery Foundation, USA (RDAPB-201809-2016615), the Swedish Alzheimer Foundation (AF-742881), Hjärnfonden, Sweden (FO2017-0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF agreement (ALFGBG-715986) and the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236). The precursor of 18F-flutemetamol was sponsored by GE Healthcare. The precursor of 18F-RO948 was provided by Roche. ADNI data collection and sharing was funded by the Alzheimer’s Disease Neuroimaging Initiative (National Institutes of Health grant U01 AG024904) and Department of Defense award no. W81XWH-12-2-0012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol-Myers Squibb Company; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company, Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co.; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. ADNI investigators contributed to the design and implementation of the ADNI database and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden

    Sebastian Palmqvist, Pontus Tideman, Nicholas Cullen, Erik Stomrud, Shorena Janelidze, Niklas Mattsson-Carlgren & Oskar Hansson

  2. Memory Clinic, Skåne University Hospital, Malmö, Sweden

    Sebastian Palmqvist, Pontus Tideman, Erik Stomrud & Oskar Hansson

  3. Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

    Henrik Zetterberg & Kaj Blennow

  4. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

    Henrik Zetterberg & Kaj Blennow

  5. Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK

    Henrik Zetterberg

  6. UK Dementia Research Institute at University College London, London, UK

    Henrik Zetterberg

  7. Eli Lilly and Company, Indianapolis, IN, USA

    Jeffery L. Dage

  8. Department of Neurology, Skåne University Hospital, Lund, Sweden

    Niklas Mattsson-Carlgren

  9. Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden

    Niklas Mattsson-Carlgren

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Consortia

the Alzheimer’s Disease Neuroimaging Initiative

  • Jeffery L. Dage
  • , Erik Stomrud
  • , Shorena Janelidze
  • , Niklas Mattsson-Carlgren
  •  & Oskar Hansson

Contributions

S.P., P.T., N.C., H.Z., K.B., J.L.D., E.S., S.J., N.M.-C. and O.H. collected the data and reviewed the manuscript for intellectual content. S.P. drafted the manuscript, analyzed the data and prepared the figures. S.P. and O.H. interpreted the data, and N.C. provided statistical support. N.M.-C. implemented the models online. O.H. was the principal investigator of BioFINDER and supervised collection, analysis and interpretation of the study data.

Corresponding authors

Correspondence to Sebastian Palmqvist or Oskar Hansson.

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Competing interests

S.P. has served on the scientific advisory boards for Hoffman-La Roche and Geras Solutions. H.Z. has served on the scientific advisory boards for Denali, Roche Diagnostics, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics and CogRx, has given lectures in symposia sponsored by Fujirebio, Alzecure and Biogen and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. K.B. has served as a consultant, on advisory boards or on the data monitoring committees for Abcam, Axon, Biogen, JOMDD/Shimadzu, Julius Clinical, Eli Lilly, MagQu, Novartis, Roche Diagnostics and Siemens Healthineers and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. J.L.D. is an employee of Eli Lilly and Company. O.H. has acquired research support (for the institution) from AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, GE Healthcare, Pfizer and Roche. In the past 2 years, O.H. has also received consultancy/speaker fees from AC Immune, Alzpath, Biogen, Cerveau and Roche. S.J., P.T., N.C., E.S. and N.M.-C. report no disclosures.

Additional information

Peer review information Nature Medicine thanks Ronald Petersen, Stephen Salloway and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jerome Staal was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team

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Extended data

Extended Data Fig. 1 Enrollment flowchart for the BioFINDER sample.

Eligible population was defined as being referred to any of the participating memory clinics and being non-demented.

Extended Data Fig. 2 Univariable logistic regression models for predicting progression to AD dementia vs other conditions within 2–6 years.

Data are shown as AUCs at each time point (error bars show the 95% CIs of the AUCs). AUCs above the dashed lines represent a predictive accuracy better than chance (AUC 0.5). APOE genotype was coded as 0, 1 or 2 ε4 alleles. Regarding the cognitive measures, memory had high accuracy from short- to long-term predictions, while executive function had lower accuracies for long-term prediction. This suggest that memory changes earlier than executive function during the development of AD. Regarding the biomarkers, cortical thickness representing AD-specific neurodegeneration were best for short- to mid-term prediction, plasma P-tau217 for mid- to long-term prediction and plasma Aβ42/Aβ40 better for long-term prediction. This is congruent with the model for the development of AD that begins with the accumulation of Aβ, then phosphorylation of tau and the deposition of tau tangles, and finally neurodegeneration.

Extended Data Fig. 3 Model selection process and performance for predicting AD dementia within 2 years in BioFINDER.

a, Model selection process. Best Model Fit shows the data-driven model selection with the lowest AIC (that is, the best model fit). The parsimonious model shows the model that had a similar model fit (ΔAIC <2) with as few significant predictors as possible. In subsequent models, the least important modalities were removed in a step-wise procedure. Model specifications including comparisons between all models are shown in Supplementary Table 2. b, ROC curves of the different models. Abbreviations: AD, Alzheimer’s disease; AIC, Akaike Information Criterion; APOE, Apolipoprotein E genotype (number of ε4 alleles); AUC, Area under the ROC curve; MRI, Cortical thickness of a temporal AD-specific region; ROC, Receiver Operating Characteristic.

Extended Data Fig. 4 Model selection process and performance for predicting AD dementia within 6 years in BioFINDER.

a, Model selection process. Best Model Fit shows the data-driven model selection with the lowest AIC (that is, the best model fit). The parsimonious model shows the model that had a similar model fit (ΔAIC <2) with as few significant predictors as possible. In subsequent models, the least important modalities were removed in a step-wise procedure. Model specifications including comparisons between all models are shown in Supplementary Table 3. b, ROC curves of the different models. Abbreviations: AD, Alzheimer’s disease; AIC, Akaike Information Criterion; APOE, Apolipoprotein E genotype (number of ε4 alleles); AUC, Area under the ROC curve; MRI, Cortical thickness of a temporal AD-specific region; ROC, Receiver Operating Characteristic.

Extended Data Fig. 5 Cross-validation and implementation of an algorithm using plasma P-tau z-scores.

Plasma P-tau z-scores based on the distribution of Aβ-negative cognitively unimpaired participants in BioFINDER and ADNI, respectively (see Methods), was used in the logistic regression models. Model coefficients were established in BioFINDER (AUC 0.90) and tested in ADNI (AUC 0.89). Cognitive z-scores have been inverted so that higher scores equal poorer results. This model is implemented at http://predictAD.app where one can enter the raw cognitive test scores that constitute the z-scores, number of APOE ε4 alleles and plasma P-tau z-score (either from P-tau217 or P-tau181).

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Palmqvist, S., Tideman, P., Cullen, N. et al. Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med 27, 1034–1042 (2021). https://doi.org/10.1038/s41591-021-01348-z

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