MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China.
Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Centre for Computational Systems Biology, Fudan University, Shanghai 200433, PR China.
Prog Neuropsychopharmacol Biol Psychiatry. 2022 Mar 8;113:110471. doi: 10.1016/j.pnpbp.2021.110471. Epub 2021 Nov 3.
Smoking accelerates the ageing of multiple organs. However, few studies have quantified the association between smoking, especially smoking cessation, and brain ageing. Using structural magnetic resonance imaging data from the UK Biobank (n = 33,293), a brain age predictor was trained using a machine learning technique in the non-smoker group (n = 14,667) and then tested in the smoker group (n = 18,626) to determine the relationships between BrainAge Gap (predicted age - true age) and smoking parameters. Further, we examined whether smoking was associated with poorer cognition and whether this relationship was mediated by brain age. The predictor achieved an appreciable performance in training data (r = 0.712, mean-absolute-error [MAE] = 4.220) and test data (r = 0.725, MAE = 4.160). On average, smokers showed a larger BrainAge Gap (+0.304 years, Cohens'd = 0.083) than controls, more explicitly, the extents vary depending on their smoking characteristic that active regular smokers had the largest BrainAge Gap (+1.190 years, Cohens'd = 0.321), and light smokers had a moderate BrainAge Gap (+0.478, Cohens'd = 0.129). The increased smoking amount was associated with a larger BrainAge Gap (β = 0.035, p = 1.72 × 10) while a longer duration of quitting smoking in ex-smokers was associated with a smaller BrainAge Gap (β = -0.015, p = 2.14 × 10). Furthermore, smoking was associated with poorer cognition, and this relationship was partially mediated by BrainAge Gap. The study provides insight into the association between smoking, brain ageing, and cognition, which provide more publicly acceptable propaganda against smoking.
吸烟会加速多个器官的老化。然而,很少有研究量化吸烟(尤其是戒烟)与大脑老化之间的关联。利用英国生物银行(UK Biobank)的结构磁共振成像数据(n=33293),使用机器学习技术在不吸烟者组(n=14667)中训练大脑年龄预测器,然后在吸烟者组(n=18626)中进行测试,以确定大脑年龄差距(预测年龄-真实年龄)与吸烟参数之间的关系。此外,我们还研究了吸烟是否与认知能力下降有关,以及这种关系是否通过大脑年龄来介导。该预测器在训练数据(r=0.712,平均绝对误差 [MAE]=4.220)和测试数据(r=0.725,MAE=4.160)中均取得了相当不错的性能。平均而言,吸烟者的大脑年龄差距较大(+0.304 岁,Cohens'd=0.083),更确切地说,这种差异程度取决于他们的吸烟特征,活跃的经常吸烟者的大脑年龄差距最大(+1.190 岁,Cohens'd=0.321),而轻度吸烟者的大脑年龄差距适中(+0.478,Cohens'd=0.129)。吸烟量的增加与大脑年龄差距的增大有关(β=0.035,p=1.72×10),而戒烟时间较长的前吸烟者的大脑年龄差距较小(β=-0.015,p=2.14×10)。此外,吸烟与认知能力下降有关,这种关系部分通过大脑年龄差距来介导。该研究深入了解了吸烟、大脑老化和认知之间的关联,为更广泛地宣传反对吸烟提供了更多可接受的论据。