Abstract： Objective:To explore the potential of brain age gap (BAG) as a biomarker of brain health and analyze its causal relationship with common brain diseases.Methods:Brain structural magnetic resonance imaging (sMRI) data from public databases (UK Biobank, ADNI, PPMI) were selected and input into a simple fully convolutional network (SFCN) to estimate BAG. The disease group (with corresponding codes or labels, n=6 796) and healthy control group (without corresponding codes or labels, n=9 660) were defined according to the presence or absence of ICD-10 codes and corresponding brain disease labels. The two-sample t-test was used to compare the BAG differences between the disease and healthy control group; genome-wide association study (GWAS) was used to find genomic regions significantly associated with BAG in 31 520 people in the UK Biobank. The causal effects between BAG and 14 brain diseases were analyzed by Mendelian randomization (MR). Results:The mean absolute error (MAE) between the subject′s chronological age and estimated brain age for the 1 932 subjects in the healthy control group used for model testing was 2.364 years. Compared with the healthy control group, Alzheimer′s disease ( t=33.42), anxiety disorders ( t=2.38), bipolar disorder ( t=3.76), stroke ( t=2.75), demyelinating disease ( t=7.45), major depressive disorder ( t=3.49), Parkinson′s disease ( t=17.69), and post-traumatic stress disorder ( t=2.34) BAG was significantly increased ( PFDR<0.05). There were 8 independent genome-wide risk regions associated with BAG in the GWAS ( P<5×10 -8), 4 of which were novel(related genes: PICK1, TBC1D9, SIAH3, and TMEM98). In MR analysis, a strong causal association between Alzheimer′s disease and BAG was observed (β=0.23,95% CI=0.08-0.38, PFDR=0.030). Conclusion:BAG can be used as a biomarker that reflects brain health information. The occurrence of Alzheimer′s disease will lead to an increase in BAG.