Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Older molecular brain age in severe mental illness


Psychiatric disorders are associated with accelerated aging and enhanced risk for neurodegenerative disorders. Brain aging is associated with molecular, cellular, and structural changes that are robust on the group level, yet show substantial inter-individual variability. Here we assessed deviations in gene expression from normal age-dependent trajectories, and tested their validity as predictors of risk for major mental illnesses and neurodegenerative disorders. We performed large-scale gene expression and genotype analyses in postmortem samples of two frontal cortical brain regions from 214 control subjects aged 20–90 years. Individual estimates of “molecular age” were derived from age-dependent genes, identified by robust regression analysis. Deviation from chronological age was defined as “delta age”. Genetic variants associated with deviations from normal gene expression patterns were identified by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic risk scores (PRScis-eQTL and PRSGWAS), and tested for predicting brain disorders or pathology in independent postmortem expression datasets and clinical cohorts. In these validation datasets, molecular ages, defined by 68 and 76 age-related genes for two brain regions respectively, were positively correlated with chronological ages (r = 0.88/0.91), elevated in bipolar disorder (BP) and schizophrenia (SCZ), and unchanged in major depressive disorder (MDD). Exploratory analyses in independent clinical datasets show that PRSs were associated with SCZ and MDD diagnostics, and with cognition in SCZ and pathology in Alzheimer’s disease (AD). These results suggest that older molecular brain aging is a common feature of severe mental illnesses and neurodegeneration.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Study flow chart.
Fig. 2: Age-related gene expresion in two related regions of the human frontal cortex.
Fig. 3: Delta age may index an older or younger brain at the molecular level compared to chronological age.
Fig. 4: Heatmap for polygenic risk score associations with clinical diagnostics and functional outcomes in external cohorts (IRL-Grey, ROSMAP, Health ABC, and Lieber center).


  1. 1.

    Vaupel JW. Biodemography of human ageing. Nature. 2010;464:536–42.

    CAS  Article  Google Scholar 

  2. 2.

    Erraji-Benchekroun L, Underwood MD, Arango V, Galfalvy H, Pavlidis P, Smyrniotopoulos P, et al. Molecular aging in human prefrontal cortex is selective and continuous throughout adult life. Biol Psychiatry. 2005;57:549–58.

    CAS  Article  Google Scholar 

  3. 3.

    Glorioso C, Oh S, Douillard GG, Sibille E. Brain molecular aging, promotion of neurological disease and modulation by sirtuin 5 longevity gene polymorphism. Neurobiol Dis. 2011;41:279–90.

    CAS  Article  Google Scholar 

  4. 4.

    Lu T, Pan Y, Kao SY, Li C, Kohane I, Chan J, et al. Gene regulation and DNA damage in the ageing human brain. Nature. 2004;429:883–91.

    CAS  Article  Google Scholar 

  5. 5.

    Berchtold NC, Cribbs DH, Coleman PD, Rogers J, Head E, Kim R, et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc Natl Acad Sci USA. 2008;105:15605–10.

    CAS  Article  Google Scholar 

  6. 6.

    Oh H, Lewis DA, Sibille E. The role of BDNF in age-dependent changes of excitatory and inhibitory synaptic markers in the human prefrontal cortex. Neuropsychopharmacology. 2016;41:3080–91.

    CAS  Article  Google Scholar 

  7. 7.

    Sibille E. Molecular aging of the brain, neuroplasticity, and vulnerability to depression and other brain-related disorders. Dialogues Clin Neurosci. 2013;15:53–65.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Baldessarini RJ. Epidemiology of suicide: recent developments. Epidemiol Psychiatr Sci. 2019;29:e71.

    Article  Google Scholar 

  9. 9.

    Laursen TM, Nordentoft M, Mortensen PB. Excess early mortality in schizophrenia. Annu Rev Clin Psychol. 2014;10:425–48.

    Article  Google Scholar 

  10. 10.

    Gilman SE, Sucha E, Kingsbury M, Horton NJ, Murphy JM, Colman I. Depression and mortality in a longitudinal study: 1952-2011. CMAJ. 2017;189:E1304–10.

    Article  Google Scholar 

  11. 11.

    Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.

    Article  Google Scholar 

  12. 12.

    Koutsouleris N, Davatzikos C, Borgwardt S, Gaser C, Bottlender R, Frodl T, et al. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr Bull. 2014;40:1140–53.

    Article  Google Scholar 

  13. 13.

    Hajek T, Franke K, Kolenic M, Capkova J, Matejka M, Propper L, et al. Brain age in early stages of bipolar disorders or schizophrenia. Schizophr Bull. 2019;45:190–8.

    Article  Google Scholar 

  14. 14.

    Yohai VJ. High breakdown-point and high efficiency robust estimates for regression. Ann Stat. 1987;15:642–56.

    Article  Google Scholar 

  15. 15.

    Chen CY, Logan RW, Ma T, Lewis DA, Tseng GC, Sibille E, et al. Effects of aging on circadian patterns of gene expression in the human prefrontal cortex. Proc Natl Acad Sci USA. 2016;113:206–11.

    CAS  Article  Google Scholar 

  16. 16.

    Lissemore JI, Bhandari A, Mulsant BH, Lenze EJ, Reynolds CF, Karp JF, et al. Reduced GABAergic cortical inhibition in aging and depression. Neuropsychopharmacology. 2018;43:2277–84.

    CAS  Article  Google Scholar 

  17. 17.

    Li J, Tseng GC. An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies. Ann Appl Stat. 2011;5(NO. 2A):994–1019.

    Article  Google Scholar 

  18. 18.

    Liang L, Morar N, Dixon AL, Lathrop GM, Abecasis GR, Moffatt MF, et al. A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines. Genome Res. 2013;23:716–26.

    CAS  Article  Google Scholar 

  19. 19.

    Nikolova YS, Iruku SP, Lin CW, Conley ED, Puralewski R, French B, et al. FRAS1-related extracellular matrix 3 (FREM3) single-nucleotide polymorphism effects on gene expression, amygdala reactivity and perceptual processing speed: An accelerated aging pathway of depression risk. Front Psychol. 2015;6:1377.

    Article  Google Scholar 

  20. 20.

    Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci. 2019;22:1617–23.

    CAS  Article  Google Scholar 

  21. 21.

    McKinney BC, Lin H, Ding Y, Lewis DA, Sweet RA. DNA methylation evidence against the accelerated aging hypothesis of schizophrenia. NPJ Schizophr. 2017;3:13.

    Article  Google Scholar 

  22. 22.

    McKinney BC, Lin H, Ding Y, Lewis DA, Sweet RA. DNA methylation age is not accelerated in brain or blood of subjects with schizophrenia. Schizophr Res. 2018;196:39–44.

    Article  Google Scholar 

  23. 23.

    Voisey J, Lawford BR, Morris CP, Wockner LF, Noble EP, Young RM, et al. Epigenetic analysis confirms no accelerated brain aging in schizophrenia. NPJ Schizophr. 2017;3:26.

    Article  Google Scholar 

  24. 24.

    Fries GR, Bauer IE, Scaini G, Wu MJ, Kazimi IF, Valvassori SS, et al. Accelerated epigenetic aging and mitochondrial DNA copy number in bipolar disorder. Transl Psychiatry. 2017;7:1283.

    Article  Google Scholar 

  25. 25.

    Li Z, He Y, Ma X, Chen X. Epigenetic age analysis of brain in major depressive disorder. Psychiatry Res. 2018;269:621–4.

    Article  Google Scholar 

  26. 26.

    Han LKM, Aghajani M, Clark SL, Chan RF, Hattab MW, Shabalin AA, et al. Epigenetic aging in major depressive disorder. Am J Psychiatry. 2018;175:774–82.

    Article  Google Scholar 

  27. 27.

    McKinney BC, Lin CW, Rahman T, Oh H, Lewis DA, Tseng G, et al. DNA methylation in the human frontal cortex reveals a putative mechanism for age-by-disease interactions. Transl Psychiatry. 2019;9:39.

    Article  Google Scholar 

  28. 28.

    Revelas M, Thalamuthu A, Oldmeadow C, Evans TJ, Armstrong NJ, Kwok JB, et al. Review and meta-analysis of genetic polymorphisms associated with exceptional human longevity. Mech Ageing Dev. 2018;175:24–34.

    CAS  Article  Google Scholar 

  29. 29.

    Glorioso C, Sibille E. Between destiny and disease: genetics and molecular pathways of human central nervous system aging. Prog Neurobiol. 2011;93:165–81.

    CAS  Article  Google Scholar 

  30. 30.

    Shukla R, Prevot TD, French L, Isserlin R, Rocco BR, Banasr M, et al. The relative contributions of cell-dependent cortical microcircuit aging to cognition and anxiety. Biol Psychiatry. 2019;85:257–67.

    Article  Google Scholar 

  31. 31.

    Benjamini Y, Yosef H. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.

    Google Scholar 

Download references


This work was supported by grants from the National Institutes of Health (MH093723 to ES and NIH R01CA190766 for CL and GCT), the Campbell Family Mental Health Research Institute (to ES). YSN is supported by a NARSAD Young Investigator Award from the Brain & Behavior Research Foundation, a Koerner New Scientist Award, and a Paul Garfinkel Catalyst Award administered by the CAMH Foundation. We thank Kurt Lohman and Yongmei Liu for help with Health ABC analyses.

Data generated as part of the CommonMind Consortium were supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881, AG02219, AG05138, MH06692, R01MH110921, R01MH109677, R01MH109897, U01MH103392, and contract HHSN271201300031C through IRP NIMH. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories, and the NIMH Human Brain Collection Core. CMC Leadership: Panos Roussos, Joseph Buxbaum, Andrew Chess, Schahram Akbarian, Vahram Haroutunian (Icahn School of Medicine at Mount Sinai), Bernie Devlin, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Enrico Domenici (University of Trento), Mette A. Peters, Solveig Sieberts (Sage Bionetworks), Thomas Lehner, Stefano Marenco, Barbara K. Lipska (NIMH).

ROSMAP study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, the Illinois Department of Public Health, and the Translational Genomics Research Institute.

The Health ABC study was supported by National Institute on Aging (NIA) Contracts N01-AG-6-2101; N01-AG-6-2103; N01-AG-6-2106; NIA grant R01-AG028050, and NINR grant R01-NR012459. This research was also funded in part by the Intramural Research Program of the NIH, National Institute on Aging and R01 AG028288.

Author information



Corresponding authors

Correspondence to George C. Tseng or Etienne Sibille.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lin, CW., Chang, LC., Ma, T. et al. Older molecular brain age in severe mental illness. Mol Psychiatry (2020).

Download citation


Quick links