Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury

Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups

Categorizing people with late-onset Alzheimer's disease into biologically coherent subgroups is important for personalized medicine. We evaluated data from five studies (total n = 4050, of whom 2431 had genome-wide single-nucleotide polymorphism (SNP) data). We assigned people to cognitively defined subgroups on the basis of relative performance in memory, executive functioning, visuospatial functioning, and language at the time of Alzheimer's disease diagnosis. We compared genotype frequencies for each subgroup to those from cognitively normal elderly controls. We focused on APOE and on SNPs with p < 10-5 and odds ratios more extreme than those previously reported for Alzheimer's disease (<0.77 or >1.30). There was substantial variation across studies in the proportions of people in each subgroup. In each study, higher proportions of people with isolated substantial relative memory impairment had ≥1 APOE ε4 allele than any other subgroup (overall p = 1.5 × 10-27). Across subgroups, there were 33 novel suggestive loci across the genome with p < 10-5 and an extreme OR compared to controls, of which none had statistical evidence of heterogeneity and 30 had ORs in the same direction across all datasets. These data support the biological coherence of cognitively defined subgroups and nominate novel genetic loci.R01 AG042437/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG029672/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ K23 AG046377/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 AG042904/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ P30 AG010133/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG019771/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ P50 AG005136/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ K01 AG050699/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ P50 AG005133/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG030653/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG041718/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG017917/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 AG032984/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG030146/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 AG006781/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 HG006375/U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)/ P50 AG005136/AG/NIA NIH HHS/United States P50 AG005133/AG/NIA NIH HHS/United States K23 AG046377/AG/NIA NIH HHS/United State