Serum neurofilament light and whole brain volume associate with machine‐learning derived brain‐predicted age in the British 1946 birth cohort Article Swipe
YOU?
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· 2020
· Open Access
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· DOI: https://doi.org/10.1002/alz.045965
Background Age is the biggest risk factor for dementia, yet human brains do not age uniformly. The British 1946 birth cohort, the world’s longest continuously running birth cohort, provides a unique opportunity to assess these variations in biological ageing. So‐called ‘brain age’ is a biomarker of brain ageing, derived from machine‐learning analysis trained on a large sample of healthy brains (N=2001). Brain age has previously been related to cognitive ageing, physiological ageing and mortality risk (DOI: 10.1038/mp.2017.62), supporting the validity of this approach for assessing biological ageing. Method 502 participants in the Insight 46 study, all born during one week in 1946, completed baseline cognitive and neuroimaging assessments at age 69‐71. 468 underwent combined 18 florbetapir PET‐MRI scans, from which amyloid status (positive/negative), whole brain volume (WBV), total intracranial volume (TIV) and hippocampal volumes (HV) were derived. The T1‐weighted sequence was passed through the Brain‐age algorithm (https://github.com/james‐cole/brainageR), deriving brain predicted‐age (BPA) and brain‐predicted age difference (brain‐PAD; BPA minus chronological age). Serum neurofilament light (NFL) concentration was measured via Simoa immunoassay. A Preclinical Alzheimer’s Cognitive Composite Score (PACC) was calculated as a mean of z‐scores of the Mini‐mental state exam (MMSE), logical memory delayed recall, digit symbol substitution score and the Face‐Name test. Life course metrics (childhood cognitive scores, education level and Framingham Risk scores) were obtained from previous cohort assessments. Multivariate regression models were used to investigate whether life course metrics predict BPA, as well as whether NFL levels, brain volumes, or cognitive scores correlated with BPA, adjusting for chronological age. Result There was a significant difference between the 229 females assessed (mean BPA 65.2 years) compared with the 239 males assessed (mean BPA 70.7). BPA was independently associated with serum NFL concentration (p = 0.071) and inversely with whole brain volume (p < 0.001). Life course factors did not predict brain age. Conclusion The results showed a significant association of BPA, a cross‐sectional imaging metric, with a biochemical marker of neuronal damage (NFL) and sex. BPA has utility as an imaging metric that can integrate multiple modalities contributing to biological age, with potential as a predictive biomarker of cognitive decline.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/alz.045965
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/alz.045965
- OA Status
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- Related Works
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- OpenAlex ID
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https://openalex.org/W3045635328Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/alz.045965Digital Object Identifier
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Serum neurofilament light and whole brain volume associate with machine‐learning derived brain‐predicted age in the British 1946 birth cohortWork title
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articleOpenAlex work type
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enPrimary language
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2020Year of publication
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2020-12-01Full publication date if available
- Authors
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Aaron Z. Wagen, William Coath, Sarah E Keuss, Sarah M. Buchanan, Mathew Storey, Kirsty Lu, Ivanna M. Pavisic, Sarah‐Naomi James, Rebecca E Street, Thomas D. Parker, Christopher Lane, Ashvini Keshavan, Heidi Murray‐Smith, David M. Cash, Ian B. Malone, Andrew Wong, Susie M.D. Henley, Sebastian J. Crutch, Henrietta Wellington, Amanda Heslegrave, Henrik Zetterberg, Nick C. Fox, Marcus Richards, James H. Cole, Jonathan M. SchottList of authors in order
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/alz.045965Direct OA link when available
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Cohort, Brain size, Psychology, Neuroscience, Developmental psychology, Demography, Medicine, Internal medicine, Magnetic resonance imaging, Sociology, RadiologyTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.brain | 47, 125, 149, 240, 291, 302 |
| abstract_inverted_index.digit | 195 |
| abstract_inverted_index.human | 11 |
| abstract_inverted_index.large | 56 |
| abstract_inverted_index.level | 210 |
| abstract_inverted_index.light | 163 |
| abstract_inverted_index.males | 271 |
| abstract_inverted_index.minus | 158 |
| abstract_inverted_index.score | 198 |
| abstract_inverted_index.serum | 281 |
| abstract_inverted_index.state | 188 |
| abstract_inverted_index.test. | 202 |
| abstract_inverted_index.these | 35 |
| abstract_inverted_index.total | 128 |
| abstract_inverted_index.which | 120 |
| abstract_inverted_index.whole | 124, 290 |
| abstract_inverted_index.(PACC) | 177 |
| abstract_inverted_index.(WBV), | 127 |
| abstract_inverted_index.0.071) | 286 |
| abstract_inverted_index.70.7). | 275 |
| abstract_inverted_index.Method | 88 |
| abstract_inverted_index.Result | 252 |
| abstract_inverted_index.ageing | 72 |
| abstract_inverted_index.age’ | 42 |
| abstract_inverted_index.assess | 34 |
| abstract_inverted_index.brains | 12, 60 |
| abstract_inverted_index.cohort | 219 |
| abstract_inverted_index.course | 204, 230, 297 |
| abstract_inverted_index.damage | 323 |
| abstract_inverted_index.during | 98 |
| abstract_inverted_index.factor | 7 |
| abstract_inverted_index.marker | 320 |
| abstract_inverted_index.memory | 192 |
| abstract_inverted_index.metric | 333 |
| abstract_inverted_index.models | 223 |
| abstract_inverted_index.passed | 142 |
| abstract_inverted_index.sample | 57 |
| abstract_inverted_index.scans, | 118 |
| abstract_inverted_index.scores | 244 |
| abstract_inverted_index.showed | 307 |
| abstract_inverted_index.status | 122 |
| abstract_inverted_index.study, | 95 |
| abstract_inverted_index.symbol | 196 |
| abstract_inverted_index.unique | 31 |
| abstract_inverted_index.volume | 126, 130, 292 |
| abstract_inverted_index.years) | 266 |
| abstract_inverted_index.(MMSE), | 190 |
| abstract_inverted_index.0.001). | 295 |
| abstract_inverted_index.British | 18 |
| abstract_inverted_index.Insight | 93 |
| abstract_inverted_index.ageing, | 48, 70 |
| abstract_inverted_index.ageing. | 39, 87 |
| abstract_inverted_index.amyloid | 121 |
| abstract_inverted_index.between | 258 |
| abstract_inverted_index.biggest | 5 |
| abstract_inverted_index.cohort, | 21, 28 |
| abstract_inverted_index.delayed | 193 |
| abstract_inverted_index.derived | 49 |
| abstract_inverted_index.factors | 298 |
| abstract_inverted_index.females | 261 |
| abstract_inverted_index.healthy | 59 |
| abstract_inverted_index.imaging | 315, 332 |
| abstract_inverted_index.levels, | 239 |
| abstract_inverted_index.logical | 191 |
| abstract_inverted_index.longest | 24 |
| abstract_inverted_index.metric, | 316 |
| abstract_inverted_index.metrics | 205, 231 |
| abstract_inverted_index.predict | 232, 301 |
| abstract_inverted_index.recall, | 194 |
| abstract_inverted_index.related | 67 |
| abstract_inverted_index.results | 306 |
| abstract_inverted_index.running | 26 |
| abstract_inverted_index.scores) | 214 |
| abstract_inverted_index.scores, | 208 |
| abstract_inverted_index.through | 143 |
| abstract_inverted_index.trained | 53 |
| abstract_inverted_index.utility | 329 |
| abstract_inverted_index.volumes | 134 |
| abstract_inverted_index.whether | 228, 237 |
| abstract_inverted_index.69‐71. | 111 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analysis | 52 |
| abstract_inverted_index.approach | 83 |
| abstract_inverted_index.assessed | 262, 272 |
| abstract_inverted_index.baseline | 104 |
| abstract_inverted_index.combined | 114 |
| abstract_inverted_index.compared | 267 |
| abstract_inverted_index.decline. | 351 |
| abstract_inverted_index.derived. | 137 |
| abstract_inverted_index.deriving | 148 |
| abstract_inverted_index.measured | 167 |
| abstract_inverted_index.multiple | 337 |
| abstract_inverted_index.neuronal | 322 |
| abstract_inverted_index.obtained | 216 |
| abstract_inverted_index.previous | 218 |
| abstract_inverted_index.provides | 29 |
| abstract_inverted_index.sequence | 140 |
| abstract_inverted_index.validity | 80 |
| abstract_inverted_index.volumes, | 241 |
| abstract_inverted_index.‘brain | 41 |
| abstract_inverted_index.(N=2001). | 61 |
| abstract_inverted_index.Cognitive | 174 |
| abstract_inverted_index.Composite | 175 |
| abstract_inverted_index.PET‐MRI | 117 |
| abstract_inverted_index.adjusting | 248 |
| abstract_inverted_index.algorithm | 146 |
| abstract_inverted_index.assessing | 85 |
| abstract_inverted_index.biomarker | 45, 348 |
| abstract_inverted_index.cognitive | 69, 105, 207, 243, 350 |
| abstract_inverted_index.completed | 103 |
| abstract_inverted_index.dementia, | 9 |
| abstract_inverted_index.education | 209 |
| abstract_inverted_index.integrate | 336 |
| abstract_inverted_index.inversely | 288 |
| abstract_inverted_index.mortality | 74 |
| abstract_inverted_index.potential | 344 |
| abstract_inverted_index.underwent | 113 |
| abstract_inverted_index.world’s | 23 |
| abstract_inverted_index.(childhood | 206 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Conclusion | 304 |
| abstract_inverted_index.Framingham | 212 |
| abstract_inverted_index.associated | 279 |
| abstract_inverted_index.biological | 38, 86, 341 |
| abstract_inverted_index.calculated | 179 |
| abstract_inverted_index.correlated | 245 |
| abstract_inverted_index.difference | 155, 257 |
| abstract_inverted_index.modalities | 338 |
| abstract_inverted_index.predictive | 347 |
| abstract_inverted_index.previously | 65 |
| abstract_inverted_index.regression | 222 |
| abstract_inverted_index.supporting | 78 |
| abstract_inverted_index.uniformly. | 16 |
| abstract_inverted_index.variations | 36 |
| abstract_inverted_index.z‐scores | 184 |
| abstract_inverted_index.Brain‐age | 145 |
| abstract_inverted_index.Face‐Name | 201 |
| abstract_inverted_index.Preclinical | 172 |
| abstract_inverted_index.So‐called | 40 |
| abstract_inverted_index.assessments | 108 |
| abstract_inverted_index.association | 310 |
| abstract_inverted_index.biochemical | 319 |
| abstract_inverted_index.florbetapir | 116 |
| abstract_inverted_index.hippocampal | 133 |
| abstract_inverted_index.investigate | 227 |
| abstract_inverted_index.opportunity | 32 |
| abstract_inverted_index.significant | 256, 309 |
| abstract_inverted_index.Multivariate | 221 |
| abstract_inverted_index.assessments. | 220 |
| abstract_inverted_index.continuously | 25 |
| abstract_inverted_index.contributing | 339 |
| abstract_inverted_index.immunoassay. | 170 |
| abstract_inverted_index.intracranial | 129 |
| abstract_inverted_index.neuroimaging | 107 |
| abstract_inverted_index.participants | 90 |
| abstract_inverted_index.substitution | 197 |
| abstract_inverted_index.(brain‐PAD; | 156 |
| abstract_inverted_index.Alzheimer’s | 173 |
| abstract_inverted_index.Mini‐mental | 187 |
| abstract_inverted_index.T1‐weighted | 139 |
| abstract_inverted_index.chronological | 159, 250 |
| abstract_inverted_index.concentration | 165, 283 |
| abstract_inverted_index.independently | 278 |
| abstract_inverted_index.neurofilament | 162 |
| abstract_inverted_index.physiological | 71 |
| abstract_inverted_index.predicted‐age | 150 |
| abstract_inverted_index.brain‐predicted | 153 |
| abstract_inverted_index.cross‐sectional | 314 |
| abstract_inverted_index.machine‐learning | 51 |
| abstract_inverted_index.(positive/negative), | 123 |
| abstract_inverted_index.10.1038/mp.2017.62), | 77 |
| abstract_inverted_index.(https://github.com/james‐cole/brainageR), | 147 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 25 |
| citation_normalized_percentile.value | 0.15668028 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |