Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK; Neurodegeneration Biology Laboratory, The Francis Crick Institute, London, UK.
Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK.
Lancet Healthy Longev. 2022 Sep;3(9):e607-e616. doi: 10.1016/S2666-7568(22)00167-2. Epub 2022 Aug 22.
A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age.
Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18-90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy.
Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (-1·3 [-2·4 to -0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD.
Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility.
Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association.
一种基于神经影像学的生物标志物，称为大脑年龄，被认为反映了大脑老化过程的可变性，并可预测寿命。利用 Insight 46 这一独特的窄年龄出生队列，我们旨在研究大脑年龄的潜在驱动因素和相关性。
参与者于 1946 年在英国大陆的同一周出生，迄今为止已经进行了 24 次前瞻性数据收集，包括大约 70 岁时的 MRI 和淀粉样 PET 成像。使用先前从该队列中定义的选择中获得的 MRI 数据，我们从一个成熟的机器学习模型（在 18-90 岁的 2001 名健康成年人中进行了训练）中得出大脑预测年龄；从（评估时的）实际年龄中减去这个值得到大脑预测年龄差（brain-PAD）。我们测试了与早期、中期和晚期生活的数据以及 MRI 衍生的脑萎缩率的关联。
2015 年 5 月 28 日至 2018 年 1 月 10 日，Insight 46 有 502 人参与评估。我们纳入了 456 名参与者（225 名女性），平均实际年龄为 70.7 岁（标准差 0.7；范围 69.2 至 71.9）。平均大脑预测年龄为 67.9 岁（8.2，46.3 至 94.3）。与男性相比，女性的大脑预测年龄差小 5.4 岁（95%CI 4.1 至 6.8）。大脑预测年龄差增加与 36 岁时心血管风险增加（β=2.3[95%CI 1.5 至 3.0]）和 69 岁时心血管风险增加（β=2.6[1.9 至 3.3]）有关；与脑血管疾病负担增加（1.9[1.3 至 2.6]）；认知能力下降（-1.3[-2.4 至-0.2]）；以及血清神经丝轻链浓度升高（1.2[0.6 至 1.9]）有关。在随后的 2 年中，大脑预测年龄差每增加 5 岁，海马体萎缩的速度就会增加 0.003 毫升/年（0.000 至 0.006），这与大脑预测年龄差增加有关。早期生活因素与大脑预测年龄差无关。在一个分层分区模型中结合 12 项指标，可以解释大脑预测年龄差 33%的方差。
大脑预测年龄差与心血管风险以及神经退行性病变的影像学和生物化学标志物有关。这些发现支持大脑预测年龄差作为大脑健康的综合综合指标，反映了对病理性大脑老化的多种贡献，并且可能具有预后效用。
英国阿尔茨海默病研究协会、医学研究委员会痴呆症平台英国、塞尔弗里奇集团基金会、沃尔夫森基金会、威康信托基金会、英国大脑研究协会、英国阿尔茨海默病协会。
