Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer’s disease prediction
Open accessAbstract
Alzheimer’s disease (AD) is the most common late-onset neurodegenerative disorder. Identifying individuals at increased risk of developing AD is important for early intervention. Using data from the Alzheimer Disease Genetics Consortium, we constructed polygenic risk scores (PRSs) for AD and age-at-onset (AAO) of AD for the UK Biobank participants. We then built machine learning (ML) models for predicting development of AD, and explored feature importance among PRSs, conventional risk factors, and ICD-10 codes from electronic health records, a total of > 11,000 features using the UK Biobank dataset. We used eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), which provided superior ML performance as well as aided ML model explanation. For participants age 40 and older, the area under the curve for AD was 0.88. For subjects of age 65 and older (late-onset AD), PRSs were the most important predictors. This is the first observation that PRSs constructed from the AD risk and AAO play more important roles than age in predicting AD. The ML model also identified important predictors from EHR, including urinary tract infection, syncope and collapse, chest pain, disorientation and hypercholesterolemia, for developing AD. Our ML model improved the accuracy of AD risk prediction by efficiently exploring numerous predictors and identified novel feature patterns.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
By integrating neuroimaging, genetic, and clinical data, researchers have developed models that can identify individuals at high risk of developing Alzheimer's disease.
“By integrating neuroimaging, genetic, and clinical data, researchers have developed models that can identify individuals at high risk of developing Alzheimer’s disease [15–18].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Dementia prevention, intervention, and care: 2020 report of the Lancet Commission
Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., Brayne, C., Burns, A., Cohen-Mansfield, J., Cooper, C., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30367-6 · Cited by 10,430 · see citing works
RetractedXGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551 · see citing works
Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., Roses, A. D., Haines, J. L., & Pericak-Vance, M. A. (1993) · Science · doi:10.1126/science.8346443 · Cited by 9,429 · see citing works
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
Scheltens, P., De Strooper, B., Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., Cummings, J., & van der Flier, W. M. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32205-4 · Cited by 5,465 · see citing works
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Clinical and biomarker changes in dominantly inherited Alzheimer's disease
Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., Marcus, D. S., Cairns, N. J., Xie, X., Blazey, T. M., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1202753 · Cited by 3,814 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Self-citationKnopman, D. S., Amieva, H., Petersen, R. C., Chételat, G., Holtzman, D. M., Hyman, B. T., Nixon, R. A., & Jones, D. T. (2021) · Nature Reviews Disease Primers · doi:10.1038/s41572-021-00269-y · Cited by 2,606 · see citing works
Naj, A. C., Jun, G., Beecham, G. W., Wang, L.-S., Vardarajan, B. N., Buros, J., Gallins, P. J., Buxbaum, J. D., Jarvik, G. P., Crane, P. K., et al. (2011) · Nature Genetics · doi:10.1038/ng.801 · Cited by 1,901 · see citing works
Self-citationRole of Genes and Environments for Explaining Alzheimer Disease
Gatz, M., Reynolds, C. A., Fratiglioni, L., Johansson, B., Mortimer, J. A., Berg, S., Fiske, A., & Pedersen, N. L. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.2.168 · Cited by 1,798 · see citing works
Computationally efficient whole-genome regression for quantitative and binary traits
Mbatchou, J., Barnard, L., Backman, J., Marcketta, A., Kosmicki, J. A., Ziyatdinov, A., Benner, C., O’Dushlaine, C., Barber, M., Boutkov, B., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00870-7 · Cited by 1,502 · see citing works
Rajan, K. B., Weuve, J., Barnes, L. L., McAninch, E. A., Wilson, R. S., & Evans, D. A. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12362 · Cited by 1,268 · see citing works
<p>Alzheimer’s Disease – Why We Need Early Diagnosis</p>
Rasmussen, J., & Langerman, H. (2019) · Degenerative Neurological and Neuromuscular Disease · doi:10.2147/dnnd.s228939 · Cited by 501 · see citing works
Body mass index and risk of dementia: analysis of individual-level data from 1·3 million individuals
Kivimäki, M., Luukkonen, R., Batty, G. D., Ferrie, J. E., Pentti, J., Nyberg, S. T., Shipley, M. J., Alfredsson, L., Fransson, E. I., Goldberg, M., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.09.016 · Cited by 440 · see citing works
The age factor in Alzheimer’s disease
Guerreiro, R., & Bras, J. (2015) · Genome Medicine · doi:10.1186/s13073-015-0232-5 · Cited by 415 · see citing works
Common polygenic variation enhances risk prediction for Alzheimer’s disease
Escott-Price, V., Sims, R., Bannister, C., Harold, D., Vronskaya, M., Majounie, E., Badarinarayan, N., GERAD/PERADES, consortia, I., Morgan, K., et al. (2015) · Brain · doi:10.1093/brain/awv268 · Cited by 414 · see citing works
UK biobank data: come and get it
Allen, N. E., Sudlow, C., Peakman, T., Collins, R., & Biobank, O. B. O. U. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3008601 · Cited by 399 · see citing works
Desikan, R. S., Fan, C. C., Wang, Y., Schork, A. J., Cabral, H. J., Cupples, L. A., Thompson, W. K., Besser, L., Kukull, W. A., Holland, D., et al. (2017) · PLoS Medicine · doi:10.1371/journal.pmed.1002258 · Cited by 395 · see citing works
Precision medicine in 2030—seven ways to transform healthcare
Denny, J. C., & Collins, F. S. (2021) · Cell · doi:10.1016/j.cell.2021.01.015 · Cited by 362 · see citing works
Structural MRI changes detectable up to ten years before clinical Alzheimer's disease
Tondelli, M., Wilcock, G. K., Nichelli, P., de Jager, C. A., Jenkinson, M., & Zamboni, G. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.05.018 · Cited by 251 · see citing works
Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores
Leonenko, G., Baker, E. A., Stevenson‐Hoare, J. O., Sierksma, A. S. R., Fiers, M. W. E. J., Williams, J. H., Strooper, B. D., & Escott‐Price, V. (2021) · Nature Communications · doi:10.1038/s41467-021-24082-z · Cited by 226 · see citing works
Cholesterol and Alzheimer’s Disease; From Risk Genes to Pathological Effects
Feringa, F. M., & van der Kant, R. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.690372 · Cited by 226 · see citing works
Rowe, C. C., Bourgeat, P., Ellis, K. A., Brown, B., Lim, Y. Y., Mulligan, R., Jones, G., Maruff, P., Woodward, M., Price, R., et al. (2013) · Annals of Neurology · doi:10.1002/ana.24040 · Cited by 222 · see citing works
Chapter 13: Mining Electronic Health Records in the Genomics Era
Denny, J. C. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002823 · Cited by 165 · see citing works
Genome-wide association analyses identify new loci influencing intraocular pressure
Gao, X., Huang, H., Nannini, D. R., Fan, F., & Kim, H. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy111 · Cited by 110 · see citing works
Self-citationGao, X., Huang, H., & Kim, H. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy422 · Cited by 89 · see citing works
Self-citationPolygenic hazard scores in preclinical Alzheimer disease
Tan, C. H., Hyman, B. T., Tan, J. J. X., Hess, C. P., Dillon, W. P., Schellenberg, G. D., Besser, L. M., Kukull, W. A., Kauppi, K., McEvoy, L. K., et al. (2017) · Annals of Neurology · doi:10.1002/ana.25029 · Cited by 67 · see citing works
Hsu, D. C., Study, F. T. H. A. B., Mormino, E. C., Schultz, A. P., Amariglio, R. E., Donovan, N. J., Rentz, D. M., Johnson, K. A., Sperling, R. A., & Marshall, G. A. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-150987 · Cited by 60 · see citing works
Gao, X., Huang, H., & Kim, H. (2019) · Translational Vision Science & Technology · doi:10.1167/tvst.8.2.10 · Cited by 59 · see citing works
Self-citationGenetic Stratification to Identify Risk Groups for Alzheimer’s Disease
Marioni, R. E., Campbell, A. I., Hagenaars, S. P., Nagy, R., Amador, C., Hayward, C., Porteous, D. J., Visscher, P. M., & Deary, I. J. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161070 · Cited by 29 · see citing works
Naj, A. C., Leonenko, G., Jian, X., Grenier‐Boley, B., Dalmasso, M. C., Bellenguez, C., Jin, S., Zhao, Y., van der Lee, S. J., Sims, R., et al. (2021) · medRxiv · doi:10.1101/2021.03.14.21253553 · Cited by 17 · see citing works
An exploration of genetic association tests for disease risk and age at onset
Martin, E. R., Gao, X., & Li, Y. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22368 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [17]International Schizophrenia Consortium Common polygenic variation contributes to risk of schizophrenia and bipolar disorder Nature 2009 460 748 752 10.1038/nature08185 19571811 PMC3912837 · DOI · Paper page
- [21]Chen, T. & Guestrin, C. In Proc of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).
- [40]Chang CC Second-generation PLINK: Rising to the challenge of larger and richer datasets Gigascience 2015 4 7 10.1186/s13742-015-0047-8 25722852 PMC4342193 · DOI · Paper page
Cited by
Sorted by citations
Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73 · see citing works
Self-citation
- Authors
- Gao, X. R., Chiariglione, M., Qin, K., Nuytemans, K., Scharre, D. W., Li, Y.-J., & Martin, E. R.
- Affiliations
- The Ohio State University (US)
- University of Miami (US)
- The Ohio State University Wexner Medical Center (US)
- Duke University (US)
- Corresponding author
- Gao, Xiaoyi Raymond
- Venue
- Scientific Reports
- Published
- 2023-01-09
- Volume
- 13
- Issue
- 1
- Pages
- 450-450
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9829871
- OpenAlex
- W4313830798
- Cited by
- 45
- References
- 38
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 15 · 2023: 2
- FWCI
- 9.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (RF1AG060472)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, polygenic risk score, age of onset, Alzheimer's disease risk prediction, electronic health records, ICD-10 codes, feature importance, explainable machine learning, SHAP, XGBoost, urinary tract infection, syncope, hypercholesterolemia, chest pain, disorientation, UK Biobank
- MeSH terms
- Machine Learning, Adult, Aged, Alzheimer Disease, Humans, Risk Factors, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
