Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks Article Swipe
Yuting Zhang
,
Upamanyu Ghose
,
Noel J. Buckley
,
Sebastiaan Engelborghs
,
Kristel Sleegers
,
Giovanni B. Frisoni
,
Anders Wallin
,
Alberto Lleó
,
Julius Popp
,
Pablo Martínez‐Lage
,
Cristina Legido‐Quigley
,
Frederik Barkhof
,
Henrik Zetterberg
,
Pieter Jelle Visser
,
Lars Bertram
,
Simon Lovestone
,
Alejo Nevado‐Holgado
,
Liu Shi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnagi.2022.1040001
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnagi.2022.1040001
Background and objective Blood-based biomarkers represent a promising approach to help identify early Alzheimer’s disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E ( APOE ) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein–protein interaction enrichment analysis. Results Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase–protein kinase B/Akt signaling pathway. Conclusion Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnagi.2022.1040001
- OA Status
- gold
- Cited By
- 12
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310811141
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310811141Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnagi.2022.1040001Digital Object Identifier
- Title
-
Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-29Full publication date if available
- Authors
-
Yuting Zhang, Upamanyu Ghose, Noel J. Buckley, Sebastiaan Engelborghs, Kristel Sleegers, Giovanni B. Frisoni, Anders Wallin, Alberto Lleó, Julius Popp, Pablo Martínez‐Lage, Cristina Legido‐Quigley, Frederik Barkhof, Henrik Zetterberg, Pieter Jelle Visser, Lars Bertram, Simon Lovestone, Alejo Nevado‐Holgado, Liu ShiList of authors in order
- Landing page
-
https://doi.org/10.3389/fnagi.2022.1040001Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fnagi.2022.1040001Direct OA link when available
- Concepts
-
Apolipoprotein E, Neurodegeneration, Biomarker, Disease, Alzheimer's disease, Tau protein, Medicine, Bioinformatics, Biomarker discovery, Artificial intelligence, Neuroscience, Oncology, Machine learning, Computational biology, Psychology, Internal medicine, Biology, Proteomics, Computer science, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 5, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 59, 107, 173, 182, 242, 250, 270 |
| abstract_inverted_index.on | 41 |
| abstract_inverted_index.to | 9, 24, 55, 66, 122, 161, 167, 189, 255, 301 |
| abstract_inverted_index.we | 53 |
| abstract_inverted_index.β | 110 |
| abstract_inverted_index.100 | 143 |
| abstract_inverted_index.881 | 88 |
| abstract_inverted_index.AD. | 79, 168 |
| abstract_inverted_index.Age | 224 |
| abstract_inverted_index.NNs | 316 |
| abstract_inverted_index.The | 248, 262 |
| abstract_inverted_index.age | 147, 184, 291 |
| abstract_inverted_index.aim | 54 |
| abstract_inverted_index.and | 1, 29, 74, 117, 137, 139, 148, 177, 185, 213, 218, 225, 232, 246, 259, 282, 292, 308 |
| abstract_inverted_index.but | 34 |
| abstract_inverted_index.did | 317 |
| abstract_inverted_index.fed | 140 |
| abstract_inverted_index.for | 31, 96, 208, 215, 306 |
| abstract_inverted_index.has | 18, 44 |
| abstract_inverted_index.not | 318 |
| abstract_inverted_index.our | 330 |
| abstract_inverted_index.p53 | 279 |
| abstract_inverted_index.tau | 114, 119 |
| abstract_inverted_index.the | 35, 50, 57, 91, 141, 196, 206, 238, 295, 299, 315, 323 |
| abstract_inverted_index.top | 142 |
| abstract_inverted_index.(ML) | 23 |
| abstract_inverted_index.APOE | 152, 178, 186, 226, 293 |
| abstract_inverted_index.AUCs | 254 |
| abstract_inverted_index.Aβ, | 134, 229 |
| abstract_inverted_index.age, | 176 |
| abstract_inverted_index.area | 236 |
| abstract_inverted_index.been | 46 |
| abstract_inverted_index.data | 28 |
| abstract_inverted_index.deep | 42, 61 |
| abstract_inverted_index.each | 209 |
| abstract_inverted_index.five | 268 |
| abstract_inverted_index.four | 164 |
| abstract_inverted_index.from | 90 |
| abstract_inverted_index.have | 298 |
| abstract_inverted_index.help | 10 |
| abstract_inverted_index.into | 155 |
| abstract_inverted_index.main | 200 |
| abstract_inverted_index.most | 36 |
| abstract_inverted_index.over | 198 |
| abstract_inverted_index.risk | 201 |
| abstract_inverted_index.tau, | 73 |
| abstract_inverted_index.than | 322 |
| abstract_inverted_index.that | 70, 193, 204 |
| abstract_inverted_index.used | 328 |
| abstract_inverted_index.were | 211, 265, 335 |
| abstract_inverted_index.with | 133, 146, 180, 235, 290 |
| abstract_inverted_index.(AD). | 15 |
| abstract_inverted_index.(AUC) | 240 |
| abstract_inverted_index.(Aβ) | 111 |
| abstract_inverted_index.(NNs) | 65 |
| abstract_inverted_index.3,635 | 83 |
| abstract_inverted_index.AT(N) | 125, 233 |
| abstract_inverted_index.B/Akt | 285 |
| abstract_inverted_index.MBD). | 103 |
| abstract_inverted_index.While | 314 |
| abstract_inverted_index.alone | 188, 227 |
| abstract_inverted_index.along | 145 |
| abstract_inverted_index.await | 309 |
| abstract_inverted_index.based | 40 |
| abstract_inverted_index.brain | 108 |
| abstract_inverted_index.curve | 239 |
| abstract_inverted_index.early | 12 |
| abstract_inverted_index.human | 274 |
| abstract_inverted_index.input | 159 |
| abstract_inverted_index.model | 327 |
| abstract_inverted_index.omics | 27 |
| abstract_inverted_index.power | 58 |
| abstract_inverted_index.serve | 302 |
| abstract_inverted_index.size. | 341 |
| abstract_inverted_index.small | 339 |
| abstract_inverted_index.study | 101 |
| abstract_inverted_index.their | 124, 131, 333 |
| abstract_inverted_index.these | 163, 199 |
| abstract_inverted_index.total | 118 |
| abstract_inverted_index.under | 237 |
| abstract_inverted_index.using | 85, 174, 183 |
| abstract_inverted_index.virus | 276 |
| abstract_inverted_index.0.662, | 244 |
| abstract_inverted_index.0.674, | 257 |
| abstract_inverted_index.0.710, | 245 |
| abstract_inverted_index.0.734, | 258 |
| abstract_inverted_index.0.748, | 243 |
| abstract_inverted_index.0.782, | 256 |
| abstract_inverted_index.0.795. | 247 |
| abstract_inverted_index.0.831, | 260 |
| abstract_inverted_index.AT(N), | 138 |
| abstract_inverted_index.better | 320 |
| abstract_inverted_index.burden | 121, 234 |
| abstract_inverted_index.future | 312 |
| abstract_inverted_index.kinase | 284 |
| abstract_inverted_index.likely | 336 |
| abstract_inverted_index.modern | 37 |
| abstract_inverted_index.neural | 63 |
| abstract_inverted_index.p-tau, | 135, 230 |
| abstract_inverted_index.panels | 192 |
| abstract_inverted_index.plasma | 26, 68 |
| abstract_inverted_index.ranked | 128 |
| abstract_inverted_index.sample | 340 |
| abstract_inverted_index.scores | 241, 321 |
| abstract_inverted_index.search | 30 |
| abstract_inverted_index.status | 154, 187 |
| abstract_inverted_index.study, | 52, 332 |
| abstract_inverted_index.t-tau, | 136, 231 |
| abstract_inverted_index.vector | 325 |
| abstract_inverted_index.(AT[N]) | 76 |
| abstract_inverted_index.(p-tau) | 115 |
| abstract_inverted_index.(t-tau) | 120 |
| abstract_inverted_index.Medical | 93 |
| abstract_inverted_index.Methods | 80 |
| abstract_inverted_index.Results | 223 |
| abstract_inverted_index.achieve | 319 |
| abstract_inverted_index.amyloid | 109 |
| abstract_inverted_index.analyze | 25 |
| abstract_inverted_index.applied | 19 |
| abstract_inverted_index.burden, | 112, 116 |
| abstract_inverted_index.current | 51 |
| abstract_inverted_index.disease | 14 |
| abstract_inverted_index.harness | 56 |
| abstract_inverted_index.however | 45 |
| abstract_inverted_index.limited | 337 |
| abstract_inverted_index.machine | 21, 326 |
| abstract_inverted_index.methods | 39 |
| abstract_inverted_index.outcome | 210 |
| abstract_inverted_index.pathway | 216 |
| abstract_inverted_index.predict | 71, 162 |
| abstract_inverted_index.protein | 191 |
| abstract_inverted_index.support | 324 |
| abstract_inverted_index.(EMIF-AD | 102 |
| abstract_inverted_index.Combined | 289 |
| abstract_inverted_index.European | 92 |
| abstract_inverted_index.Previous | 16 |
| abstract_inverted_index.Proteins | 203 |
| abstract_inverted_index.SOMAscan | 86 |
| abstract_inverted_index.addition | 249 |
| abstract_inverted_index.amyloid, | 72 |
| abstract_inverted_index.approach | 8 |
| abstract_inverted_index.clusters | 269 |
| abstract_inverted_index.compared | 170 |
| abstract_inverted_index.enriched | 266 |
| abstract_inverted_index.factors. | 202 |
| abstract_inverted_index.features | 160 |
| abstract_inverted_index.genotype | 179 |
| abstract_inverted_index.identify | 11, 67, 190 |
| abstract_inverted_index.improved | 195, 205, 253 |
| abstract_inverted_index.learning | 22, 43, 62 |
| abstract_inverted_index.measured | 82 |
| abstract_inverted_index.networks | 64 |
| abstract_inverted_index.outcomes | 165 |
| abstract_inverted_index.pathway, | 281 |
| abstract_inverted_index.pathway. | 287 |
| abstract_inverted_index.pathways | 272 |
| abstract_inverted_index.previous | 331 |
| abstract_inverted_index.proteins | 69, 84, 129, 144, 251, 264, 296 |
| abstract_inverted_index.relevant | 166 |
| abstract_inverted_index.research | 17 |
| abstract_inverted_index.scarcely | 47 |
| abstract_inverted_index.studies. | 313 |
| abstract_inverted_index.Biomarker | 99 |
| abstract_inverted_index.Discovery | 100 |
| abstract_inverted_index.Framework | 95 |
| abstract_inverted_index.analysis. | 222 |
| abstract_inverted_index.determine | 123 |
| abstract_inverted_index.explored. | 48 |
| abstract_inverted_index.genotype, | 294 |
| abstract_inverted_index.including | 273 |
| abstract_inverted_index.nominated | 214 |
| abstract_inverted_index.objective | 2 |
| abstract_inverted_index.optimally | 194 |
| abstract_inverted_index.potential | 32, 300 |
| abstract_inverted_index.predicted | 228 |
| abstract_inverted_index.promising | 7 |
| abstract_inverted_index.proteins, | 175 |
| abstract_inverted_index.represent | 5 |
| abstract_inverted_index.signaling | 280, 286 |
| abstract_inverted_index.statuses. | 126 |
| abstract_inverted_index.underwent | 105 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 288 |
| abstract_inverted_index.Multimodal | 98 |
| abstract_inverted_index.aggregated | 212 |
| abstract_inverted_index.biomarkers | 4, 305 |
| abstract_inverted_index.enrichment | 217, 221 |
| abstract_inverted_index.identified | 263, 297 |
| abstract_inverted_index.infection, | 278 |
| abstract_inverted_index.prediction | 197, 207 |
| abstract_inverted_index.validation | 310 |
| abstract_inverted_index.Blood-based | 3 |
| abstract_inverted_index.Information | 94 |
| abstract_inverted_index.association | 132 |
| abstract_inverted_index.biomarkers, | 33 |
| abstract_inverted_index.blood-based | 304 |
| abstract_inverted_index.classifiers | 157 |
| abstract_inverted_index.interaction | 220 |
| abstract_inverted_index.pathologies | 77 |
| abstract_inverted_index.performance | 172, 181 |
| abstract_inverted_index.traditional | 20 |
| abstract_inverted_index.Participants | 104 |
| abstract_inverted_index.measurements | 106 |
| abstract_inverted_index.participants | 89 |
| abstract_inverted_index.performances | 334 |
| abstract_inverted_index.AD-associated | 271 |
| abstract_inverted_index.Alzheimer’s | 13 |
| abstract_inverted_index.respectively. | 261 |
| abstract_inverted_index.significantly | 252 |
| abstract_inverted_index.apolipoprotein | 149 |
| abstract_inverted_index.phosphorylated | 113 |
| abstract_inverted_index.immunodeficiency | 275 |
| abstract_inverted_index.state-of-the-art | 60 |
| abstract_inverted_index.neurodegeneration | 75 |
| abstract_inverted_index.protein–protein | 219 |
| abstract_inverted_index.phosphoinositide-3-kinase–protein | 283 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5045725813, https://openalex.org/A5042602412 |
| countries_distinct_count | 9 |
| institutions_distinct_count | 18 |
| corresponding_institution_ids | https://openalex.org/I40120149 |
| citation_normalized_percentile.value | 0.82912477 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |