Serum neurofilament light and whole brain volume associate with machine‐learning derived brain‐predicted age in the British 1946 birth cohort: Neuroimaging / New imaging methods. (7th December 2020)

• Record Type: Journal Article
• Authors: Wagen, Aaron; Coath, William; Keuss, Sarah E; Buchanan, Sarah M; Storey, Mathew; Lu, Kirsty; Pavisic, Ivanna M; James, Sarah‐Naomi; Street, Rebecca E; Parker, Thomas D; Lane, Christopher A; Keshavan, Ashvini; Murray‐Smith, Heidi; Cash, David M; Malone, Ian B; Wong, Andrew; Henley, Susie; Crutch, Sebastian J; Wellington, Henrietta; Heslegrave, Amanda J; Zetterberg, Henrik; Fox, Nick C; Richards, Marcus; Cole, James; Schott, Jonathan M
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.045965
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: 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.