Excess brain age in the sleep electroencephalogram predicts reduced life expectancy

The brain age index (BAI) measures the difference between an individual's apparent "brain age" (BA; estimated by comparing EEG features during sleep from an individual with age norms), and their chronological age (CA); that is BAI = BA-CA. Here, we evaluate whether BAI predicts life expectancy. Brain age was quantified using a previously published machine learning algorithm for a cohort of participants ≥40 years old who underwent an overnight sleep electroencephalogram (EEG) as part of the Sleep Heart Health Study (n = 4877). Excess brain age (BAI >0) was associated with reduced life expectancy (adjusted hazard ratio: 1.12, [1.03, 1.21], p = 0.002). Life expectancy decreased by -0.81 [-1.44, -0.24] years per standard-deviation increase in BAI. Our findings show that BAI, a sleep EEG-based biomarker of the deviation of sleep microstructure from patterns normal for age, is an independent predictor of life expectancy.

[1]  Stefan Klöppel,et al.  BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.

[2]  M. Levine,et al.  DNA methylation-based measures of biological age: meta-analysis predicting time to death , 2016, Aging.

[3]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[4]  Haoqi Sun,et al.  Expert-level sleep scoring with deep neural networks , 2018, J. Am. Medical Informatics Assoc..

[5]  Tarun Saxena,et al.  Pathophysiology of essential hypertension: an update , 2018, Expert review of cardiovascular therapy.

[6]  Julio Acosta-Cabronero,et al.  Brain-predicted age in Down syndrome is associated with beta amyloid deposition and cognitive decline , 2017, Neurobiology of Aging.

[7]  Haoqi Sun,et al.  Brain age from the electroencephalogram of sleep , 2018, Neurobiology of Aging.

[8]  Daniel J Buysse,et al.  Healthy Older Adults’ Sleep Predicts All-Cause Mortality at 4 to 19 Years of Follow-Up , 2003, Psychosomatic medicine.

[9]  P. Achermann,et al.  Nature and Nurture: Brain Region-Specific Inheritance of Sleep Neurophysiology in Adolescence , 2018, The Journal of Neuroscience.

[10]  A. Mebazaa,et al.  EEG power spectral density under Propofol and its association with burst suppression, a marker of cerebral fragility , 2019, Clinical Neurophysiology.

[11]  J. Samet,et al.  The Sleep Heart Health Study: design, rationale, and methods. , 1997, Sleep.

[12]  Daniel J Buysse,et al.  Multidimensional Sleep and Mortality in Older Adults: A Machine-Learning Comparison with Other Risk Factors. , 2019, The journals of gerontology. Series A, Biological sciences and medical sciences.

[13]  S. Horvath.  DNA methylation age of human tissues and cell types , 2013, Genome Biology.

[14]  Christian Gaser,et al.  Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2012 .

[15]  Steve Horvath,et al.  Epigenetic Predictor of Age , 2011, PloS one.

[16]  P. Kruk,et al.  DNA damage and repair in telomeres: relation to aging. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[17]  M. Siemiński,et al.  Higher nocturnal systolic blood pressure in patients with restless legs syndrome compared with patients with insomnia. , 2017, Sleep medicine.

[18]  H. Yaggi,et al.  Sleep Apnea Heterogeneity, Phenotypes, and Cardiovascular Risk. Implications for Trial Design and Precision Sleep Medicine , 2019, American journal of respiratory and critical care medicine.

[19]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[20]  M. Siemiński,et al.  “Non-dipping” is equally frequent in narcoleptic patients and in patients with insomnia , 2015, Sleep and Biological Rhythms.

[21]  E. van Cauter,et al.  Sleep influences on obesity, insulin resistance, and risk of type 2 diabetes. , 2018, Metabolism: clinical and experimental.

[22]  J. Cole,et al.  Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers , 2017, Trends in Neurosciences.

[23]  Mathias Basner,et al.  Inter-individual Differences in the Effects of Aircraft Noise on Sleep Fragmentation. , 2016, Sleep.

[24]  M. Ferrara,et al.  The heritability of the human K-complex: a twin study. , 2019, Sleep.

[25]  Hazem Refai,et al.  Predicting Age From Brain EEG Signals—A Machine Learning Approach , 2018, Front. Aging Neurosci..

[26]  Olga Sourina,et al.  Large-Scale Automated Sleep Staging , 2017, Sleep.

[27]  E N Brown,et al.  The Ageing Brain: Age-dependent changes in the electroencephalogram during propofol and sevoflurane general anaesthesia. , 2015, British journal of anaesthesia.

[28]  James H. Cole,et al.  Neuroimaging-derived brain-age: an ageing biomarker? , 2017, Aging.

[29]  Christian Gaser,et al.  The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2016, PloS one.

[30]  Stuart J. Ritchie,et al.  Brain age predicts mortality , 2017, Molecular Psychiatry.

[31]  Stephen F. Smagula,et al.  Actigraphy- and Polysomnography-Measured Sleep Disturbances, Inflammation, and Mortality Among Older Men , 2016, Psychosomatic medicine.