Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20–39%). An increase from −10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1–11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.

睡眠障碍随着年龄的增长而增加，是死亡率的预测因素。在这里，我们提出了通过多导睡眠图（PSG）估计年龄和死亡风险的深度神经网络。使用 2500 个 PSG 进行建模，并在 7 个年龄在 20 岁至 90 岁之间的不同队列中的男性和女性的 10,699 个 PSG 中进行测试。年龄估计的平均绝对误差为 5.8 ± 1.6 岁，而基本睡眠评分测量的误差为 14.9 ± 6.29年。在控制人口统计、睡眠和健康协变量后，年龄估计误差 (AEE) 每增加 10 年，全因死亡率就会增加 29%（95% 置信区间：20-39%）。 AEE 从-10 年增加到+10 年意味着预期寿命预计减少8.7 年（95% 置信区间：6.1-11.4 年）。更大的 AEE 主要反映在睡眠碎片化增加，表明这是独立于睡眠呼吸暂停的未来健康的重要生物标志物。