Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Use of an Alzheimer’s disease polygenic risk score to identify mild cognitive impairment in adults in their 50s

Abstract

Early identification of younger, non-demented adults at elevated risk for Alzheimer’s disease (AD) is crucial because the pathological process begins decades before dementia onset. Toward that end, we showed that an AD polygenic risk score (PRS) could identify mild cognitive impairment (MCI) in adults who were only in their 50s. Participants were 1176 white, non-Hispanic community-dwelling men of European ancestry in the Vietnam Era Twin Study of Aging (VETSA): 7% with amnestic MCI (aMCI); 4% with non-amnestic MCI (naMCI). Mean age was 56 years, with 89% <60 years old. Diagnosis was based on the Jak-Bondi actuarial/neuropsychological approach. We tested six P-value thresholds (0.05–0.50) for single nucleotide polymorphisms included in the ADPRS. After controlling for non-independence of twins and non-MCI factors that can affect cognition, higher PRSs were associated with significantly greater odds of having aMCI than being cognitively normal (odds ratios (ORs) = 1.36–1.43 for thresholds P < 0.20–0.50). The highest OR for the upper vs. lower quartile of the ADPRS distribution was 3.22. ORs remained significant after accounting for APOE-related SNPs from the ADPRS or directly genotyped APOE. Diabetes was associated with significantly increased odds of having naMCI (ORs = 3.10–3.41 for thresholds P < 0.05–0.50), consistent with naMCI having more vascular/inflammation components than aMCI. Analysis of sensitivity, specificity, and negative and positive predictive values supported some potential of ADPRSs for selecting participants in clinical trials aimed at early intervention. With participants 15+ years younger than most MCI samples, these findings are promising with regard to efforts to more effectively treat or slow AD progression.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1

References

  1. 1.

    Golde TE, Schneider LS, Koo EH. Anti-abeta therapeutics in Alzheimer’s disease: the need for a paradigm shift. Neuron. 2011;69:203–13.

    CAS  Article  Google Scholar 

  2. 2.

    Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:280–92.

    Article  Google Scholar 

  3. 3.

    Sperling RA, Jack CR Jr, Aisen PS. Testing the right target and right drug at the right stage. Sci Transl Med. 2011;3:111cm133.

    Article  Google Scholar 

  4. 4.

    Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7:270–9.

    Article  Google Scholar 

  5. 5.

    Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol. 2005;62:1160–3.

    Article  Google Scholar 

  6. 6.

    Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45:1452–8.

    CAS  Article  Google Scholar 

  7. 7.

    Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, et al. Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain. 2015;138:3673–84.

    Article  Google Scholar 

  8. 8.

    Escott-Price V, Myers AJ, Huentelman M, Hardy J. Polygenic risk score analysis of pathologically confirmed Alzheimer’s disease. Ann Neurol. 2017;82:311–4.

    Article  Google Scholar 

  9. 9.

    Mormino EC, Sperling RA, Holmes AJ, Buckner RL, De Jager PL, Smoller JW, et al. Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology. 2016;87:481–8.

    CAS  Article  Google Scholar 

  10. 10.

    Tosto G, Bird TD, Tsuang D, Bennett DA, Boeve BF, Cruchaga C, et al. Polygenic risk scores in familial Alzheimer disease. Neurology. 2017;88:1180–6.

    Article  Google Scholar 

  11. 11.

    Lupton MK, Strike L, Hansell NK, Wen W, Mather KA, Armstrong NJ, et al. The effect of increased genetic risk for Alzheimer’s disease on hippocampal and amygdala volume. Neurobiol Aging. 2016;40:68–77.

    CAS  Article  Google Scholar 

  12. 12.

    Harrison TM, Mahmood Z, Lau EP, Karacozoff AM, Burggren AC, Small GW, et al. An Alzheimer’s disease genetic risk score predicts longitudinal thinning of hippocampal complex subregions in healthy older adults. eNeuro. 2016;3 doi: 10.1523/ENEURO.0098-16.2016.

    Article  Google Scholar 

  13. 13.

    Sabuncu MR, Buckner RL, Smoller JW, Lee PH, Fischl B, Sperling RA. The association between a polygenic Alzheimer score and cortical thickness in clinically normal subjects. Cereb Cortex. 2012;22:2653–61.

    Article  Google Scholar 

  14. 14.

    Marioni RE, Campbell A, Hagenaars SP, Nagy R, Amador C, Hayward C, et al. Genetic stratification to identify risk groups for Alzheimer’s disease. J Alzheimers Dis. 2017;57:275–83.

    Article  Google Scholar 

  15. 15.

    Chauhan G, Adams HH, Bis JC, Weinstein G, Yu L, Toglhofer AM, et al. Association of Alzheimer’s disease GWAS loci with MRI markers of brain aging. Neurobiol Aging. 2015;36:1765 e1767–1716.

    Article  Google Scholar 

  16. 16.

    Schultz SA, Boots EA, Darst BF, Zetterberg H, Blennow K, Edwards DF, et al. Cardiorespiratory fitness alters the influence of a polygenic risk score on biomarkers of AD. Neurology. 2017;88:1650–8.

    Article  Google Scholar 

  17. 17.

    Foley SF, Tansey KE, Caseras X, Lancaster T, Bracht T, Parker G, et al. Multimodal brain imaging reveals structural differences in Alzheimer’s disease polygenic risk carriers: a study in healthy young adults. Biol Psychiatry. 2017;81:154–61.

    Article  Google Scholar 

  18. 18.

    Adams HHH, RFAG deBruijn, Hofman A, Uitterlinden AG, van Duijn CM, Vernooij MW, et al. Genetic risk of neurodegenerative diseases is associated with mild cognitive impairment and conversion to dementia. Alzheimers Dement. 2015;11:1277–85.

    Article  Google Scholar 

  19. 19.

    Kremen WS, Franz CE, Lyons MJ. VETSA: the Vietnam era twin study of aging. Twin Res Hum Genet. 2013;16:399–402.

    Article  Google Scholar 

  20. 20.

    Kremen WS, Thompson-Brenner H, Leung YJ, Grant MD, Franz CE, Eisen SA, et al. Genes, environment, and time: the Vietnam era twin study of aging (VETSA). Twin Res Hum Genet. 2006;9:1009–22.

    Article  Google Scholar 

  21. 21.

    Bondi MW, Edmonds EC, Jak AJ, Clark LR, Delano-Wood L, McDonald CR, et al. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. J Alzheimers Dis. 2014;42:275–89.

    Article  Google Scholar 

  22. 22.

    Jak AJ, Bondi MW, Delano-Wood L, Wierenga C, Corey-Bloom J, Salmon DP, et al. Quantification of five neuropsychological approaches to defining mild cognitive impairment. Am J Geriatr Psychiatry. 2009;17:368–75.

    Article  Google Scholar 

  23. 23.

    Kremen WS, Jak AJ, Panizzon MS, Spoon KM, Franz CE, Thompson WK, et al. Early identification and heritability of mild cognitive impairment. Int J Epidemiol. 2014;43:600–10.

    Article  Google Scholar 

  24. 24.

    Edmonds EC, Delano-Wood L, Clark LR, Jak AJ, Nation DA, McDonald CR, et al. Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimers Dement. 2014;11:415–24.

    Article  Google Scholar 

  25. 25.

    Edmonds EC, Delano-Wood L, Galasko DR, Salmon DP, Bondi MW, Alzheimer’s Disease Neuroimaging Initiative. Subtle cognitive decline and biomarker staging in preclinical Alzheimer’s disease. J Alzheimers Dis. 2015;47:231–42..

    Article  Google Scholar 

  26. 26.

    Edmonds EC, Delano-Wood L, Jak AJ, Galasko DR, Salmon DP, Bondi MW, et al. “Missed” mild cognitive impairment: High false-negative error rate based on conventional diagnostic criteria. J Alzheimers Dis. 2016;52:685–91.

    Article  Google Scholar 

  27. 27.

    Delano-Wood L, Bondi MW, Sacco J, Abeles N, Jak AJ, Libon DJ, et al. Heterogeneity in mild cognitive impairment: differences in neuropsychological profile and associated white matter lesion pathology. J Int Neuropsychol Soc. 2009;15:906–14.

    Article  Google Scholar 

  28. 28.

    Jak AJ, Urban S, McCauley A, Bangen KJ, Delano-Wood L, Corey-Bloom J, et al. Profile of hippocampal volumes and stroke risk varies by neuropsychological definition of mild cognitive impairment. J Int Neuropsychol Soc. 2009;15:890–7.

    Article  Google Scholar 

  29. 29.

    Wierenga CE, Clark LR, Dev SI, Shin DD, Jurick SM, Rissman RA, et al. Interaction of age and APOE genotype on cerebral blood flow at rest. J Alzheimer’s Dis. 2013;34:921–35.

    CAS  Article  Google Scholar 

  30. 30.

    Wierenga CE, Dev SI, Shin DD, Clark LR, Bangen KJ, Jak AJ, et al. Effect of mild cognitive impairment and APOE genotype on resting cerebral blood flow and its association with cognition. J Cereb Blood Flow Metab. 2012;32:1589–99.

    CAS  Article  Google Scholar 

  31. 31.

    Kremen WS, Moore CS, Franz CE, Panizzon MS, Lyons MJ. Cognition in middle adulthood. In: Finkel D, Reynolds CA, editors. Behavior genetics of cognition across the lifespan. New York: Springer; 2013. pp. 105–34.

    Google Scholar 

  32. 32.

    Palmer BW, Boone KB, Lesser IM, Wohl MA. Base rates of “impaired” neuropsychological test performance among healthy older adults. Arch Clin Neuropsychol. 1998;13:503–11.

    CAS  Google Scholar 

  33. 33.

    Schoeneborn CA, Heyman KM. Health characteristics of adults aged 55 years and over: United States, 2004–7. Natl Health Stat Report. 2009;16:1–31.

  34. 34.

    Granholm EL, Panizzon MS, Elman JA, Jak AJ, Hauger RL, Bondi MW, et al. Pupillary responses as a biomarker of early risk for Alzheimer’s disease. J Alzheimer’s Dis. 2017;56:1419–28.

    CAS  Article  Google Scholar 

  35. 35.

    Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256:183–94.

    CAS  Article  Google Scholar 

  36. 36.

    Lyons MJ, York TP, Franz CE, Grant MD, Eaves LJ, Jacobson KC, et al. Genes determine stability and the environment determines change in cognitive ability during 35 years of adulthood. Psychol Sci. 2009;20:1146–52.

    Article  Google Scholar 

  37. 37.

    Lyons MJ, Panizzon MS, Liu W, McKenzie R, Bluestone NJ, Grant MD, et al. A longitudinal twin study of general cognitive ability over four decades. Dev Psychol. 2017;53:1170–7.

    Article  Google Scholar 

  38. 38.

    Chen Y, Denny KG, Harvey D, Farias ST, Mungas D, DeCarli C, et al. Progression from normal cognition to mild cognitive impairment in a diverse clinic-based and community-based elderly cohort. Alzheimers Dement. 2016;13:399–405.

    Article  Google Scholar 

  39. 39.

    Meehl P, Rosen A. Antecedent probability and the efficiency of psychometric signs, patterns, or cutting scores. Psychol Bull. 1955;52:194–216.

    CAS  Article  Google Scholar 

  40. 40.

    Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4:1–16.

    Article  Google Scholar 

  41. 41.

    Chen CY, Pollack S, Hunter DJ, Hirschhorn JN, Kraft P, Price AL. Improved ancestry inference using weights from external reference panels. Bioinformatics. 2013;29:1399–406.

    CAS  Article  Google Scholar 

  42. 42.

    1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  Google Scholar 

  43. 43.

    Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012;44:955–9.

    CAS  Article  Google Scholar 

  44. 44.

    Fuchsberger C, Abecasis GR, Hinds DA. minimac2: faster genotype imputation. Bioinformatics. 2015;31:782–4.

    CAS  Article  Google Scholar 

  45. 45.

    Schultz MR, Lyons MJ, Franz CE, Grant MD, Boake C, Jacobson KC, et al. Apolipoprotein E genotype and memory in the sixth decade of life. Neurology. 2008;70:1771–7.

    CAS  Article  Google Scholar 

  46. 46.

    SAS Institute Inc. SAS OnlineDoc 9.4. Carey, NC: SAS Institute; 2013.

    Google Scholar 

  47. 47.

    Sadeh N, Spielberg JM, Logue MW, Wolf EJ, Smith AK, Lusk J, et al. SKA2 methylation predicts reduced cortical thickness in prefrontal cortex. Mol Psychiatry. 2016;21:299.

    CAS  Article  Google Scholar 

  48. 48.

    Sadeh N, Wolf EJ, Logue MW, Lusk J, Hayes JP, McGlinchey RE, et al. Polygenic risk for externalizing psychopathology and executive dysfunction in trauma-exposed veterans. Clin Psychol Sci. 2016;4:545–58.

    Article  Google Scholar 

  49. 49.

    Wolf EJ, Logue MW, Hayes JP, Sadeh N, Schichman SA, Stone A, et al. Accelerated DNA methylation age: associations with PTSD and neural integrity. Psychoneuroendocrinology. 2016;63:155–62.

    CAS  Article  Google Scholar 

  50. 50.

    Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity. 2005;95:221–7.

    CAS  Article  Google Scholar 

  51. 51.

    R Development Core Team. R: a Language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.

  52. 52.

    Lobo JM, Jiménez-Valverde A, Real R. AUC: A misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008;17:145–51.

    Article  Google Scholar 

  53. 53.

    Finch CE, Shams S. Apolipoprotein E and sex bias in cerebrovascular aging of men and mice. Trends Neurosci. 2016;39:625–37.

    CAS  Article  Google Scholar 

  54. 54.

    Blacker D, Haines JL, Rodes L, Terwedow H, Go RC, Harrell LE, et al. ApoE-4 and age at onset of Alzheimer’s disease: The NIMH genetics initiative. Neurology. 1997;48:139–47.

    CAS  Article  Google Scholar 

  55. 55.

    Jack CR Jr, Wiste HJ, Weigand SD, Knopman DS, Vemuri P, Mielke MM, et al. Age, sex, and APOE ε4 effects on memory, brain structure, and β-Amyloid across the adult life span. JAMA Neurol. 2015;72:511–9.

    Article  Google Scholar 

  56. 56.

    Katon W, Pedersen HS, Ribe AR, Fenger-Gron M, Davydow D, Waldorff FB, et al. Effect of depression and diabetes mellitus on the risk for dementia: a national population-based cohort study. JAMA Psychiatry. 2015;72:612–9.

    Article  Google Scholar 

  57. 57.

    Mez J, Mukherjee S, Thornton T, Fardo DW, Trittschuh E, Sutti S, et al. The executive prominent/memory prominent spectrum in Alzheimer’s disease is highly heritable. Neurobiol Aging. 2016;41:115–21.

    Article  Google Scholar 

  58. 58.

    Harris SE, Davies G, Luciano M, Payton A, Fox HC, Haggarty P, et al. Polygenic risk for Alzheimer’s disease is not associated with cognitive ability or cognitive aging in non-demented older people. J Alzheimers Dis. 2014;39:565–74.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Institute on Aging R01 AG018386, AG022381, AG022982, AG050595 (W.S.K.), R01 AG018384 (M.J.L.), R03 AG046413 (C.E.F), and K08 AG047903 (M.S.P), and the VA San Diego Center of Excellence for Stress and Mental Health. The content is the responsibility of the authors and does not necessarily represent official views of the NIA, NIH, or VA. The Cooperative Studies Program of the U.S. Department of Veterans Affairs provided financial support for development and maintenance of the Vietnam Era Twin Registry. We would also like to acknowledge the continued cooperation and participation of the members of the VET Registry and their families.

Author information

Affiliations

Authors

Corresponding author

Correspondence to William S. Kremen.

Ethics declarations

Conflict of interest

Dr. Dale is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. and receives funding through research agreements with General Electric Healthcare and Medtronic, Inc. The terms of these arrangements have been reviewed and approved by UCSD in accordance with its conflict of interest policies. The remaining authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Logue, M.W., Panizzon, M.S., Elman, J.A. et al. Use of an Alzheimer’s disease polygenic risk score to identify mild cognitive impairment in adults in their 50s. Mol Psychiatry 24, 421–430 (2019). https://doi.org/10.1038/s41380-018-0030-8

Download citation

Further reading

Search

Quick links