A framework of biomarkers for brain aging: a consensus statement by the Aging Biomarker Consortium
Aging Biomarker Consortium,Yu-Juan Jia, Jun Wang, Jun-Rong Ren, Piu Chan, Shengdi Chen, Xiao-Chun Chen, Jagadish K Chhetri, Junhong Guo, Qihao Guo, Lingjing Jin, Qiang Liu, Qiang Liu, Wenlin Ma, Zhiyong Mao, Moshi Song, Weihong Song, Yi Tang, Difei Wang, Peijun Wang, Lize Xiong, Keqiang Ye, Junjian Zhang, Weiqi Zhang, Xiaoqing Zhang, Yunwu Zhang, Zhanjun Zhang, Zhuohua Zhang, Jialin Zheng, Guang-Hui Liu, Yi Eve Sun, Yan-Jiang Wang, Gang Pei
A framework of biomarkers for brain aging: a consensus statement by the Aging Biomarker Consortium
China and the world are facing severe population aging and an increasing burden of age-related diseases. Aging of the brain causes major age-related brain diseases, such as neurodegenerative diseases and stroke. Identifying biomarkers for the effective assessment of brain aging and establishing a brain aging assessment system could facilitate the development of brain aging intervention strategies and the effective prevention and treatment of aging-related brain diseases. Thus, experts from the Aging Biomarker Consortium (ABC) have combined the latest research results and practical experience to recommend brain aging biomarkers and form an expert consensus, aiming to provide a basis for assessing the degree of brain aging and conducting brain-aging-related research with the ultimate goal of improving the brain health of elderly individuals in both China and the world.
[1] |
Cai Y, Song W, Li J, et al. The landscape of aging. Sci China Life Sci 2022;65:2354–454.
CrossRef
Google scholar
|
[2] |
Aging Biomarker C et al. Biomarkers of aging. Sci China Life Sci 2023;66(5):893–1066.
CrossRef
Google scholar
|
[3] |
Sousa-Uva M, Head SJ, Thielmann M, et al. Methodology manual for European Association for Cardio-Thoracic Surgery (EACTS) clinical guidelines. Eur J Cardiothorac Surg 2015;48:809–16.
CrossRef
Google scholar
|
[4] |
Li H, Lv C, Zhang T, et al. Trajectories of age-related cognitive decline and potential associated factors of cognitive function in senior citizens of Beijing. Curr Alzheimer Res 2014;11:806–16.
CrossRef
Google scholar
|
[5] |
Tromp D, Dufour A, Lithfous S, et al. Episodic memory in normal aging and Alzheimer disease: insights from imaging and behavioral studies. Ageing Res Rev 2015;24:232–62.
CrossRef
Google scholar
|
[6] |
Nyberg L. Functional brain imaging of episodic memory decline in ageing. J Intern Med 2017;281:65–74.
CrossRef
Google scholar
|
[7] |
Lempert KM, Cohen MS, MacNear KA, et al. Aging is associated with maladaptive episodic memory-guided social decision-making. Proc Natl Acad Sci U S A 2022;119:e2208681119.
CrossRef
Google scholar
|
[8] |
Pelletier A, Bernard C, Dilharreguy B, et al. Patterns of brain atrophy associated with episodic memory and semantic fluency decline in aging. Aging (Albany NY) 2017;9:741–52.
CrossRef
Google scholar
|
[9] |
Persson J, Nyberg L, Lind J, et al. Structure-function correlates of cognitive decline in aging. Cereb Cortex 2006;16:907–15.
CrossRef
Google scholar
|
[10] |
Charlton RA, Barrick TR, Markus HS, et al. The relationship between episodic long-term memory and white matter integrity in normal aging. Neuropsychologia 2010;48:114–22.
CrossRef
Google scholar
|
[11] |
Ousdal OT, Kaufmann T, Kolskår K, et al. Longitudinal stability of the brain functional connectome is associated with episodic memory performance in aging. Hum Brain Mapp 2020;41:697–709.
CrossRef
Google scholar
|
[12] |
Nyberg L, Sandblom J, Jones S, et al. Neural correlates of training-related memory improvement in adulthood and aging. Proc Natl Acad Sci U S A 2003;100:13728–33.
CrossRef
Google scholar
|
[13] |
Dennis NA, Daselaar S, Cabeza R. Effects of aging on transient and sustained successful memory encoding activity. Neurobiol Aging 2007;28:1749–58.
CrossRef
Google scholar
|
[14] |
Grady CL, McIntosh AR, Horwitz B, et al. Age-related reductions in human recognition memory due to impaired encoding. Science 1995;269:218–21.
CrossRef
Google scholar
|
[15] |
Wang L, Li H, Liang Y, et al. Amnestic mild cognitive impairment: topological reorganization of the default-mode network. Radiology 2013;268:501–14.
CrossRef
Google scholar
|
[16] |
Benejam B, Aranha MR, Videla L, et al. Neural correlates of episodic memory in adults with Down syndrome and Alzheimer’s disease. Alzheimers Res Ther 2022;14:123.
CrossRef
Google scholar
|
[17] |
Moradi E, Hallikainen I, Hänninen T, et al; Alzheimer’s Disease Neuroimaging Initiative. Rey’s Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer’s disease. Neuroimage Clin 2017;13:415–27.
CrossRef
Google scholar
|
[18] |
Lacreuse A, Raz N, Schmidtke D, et al. Age-related decline in executive function as a hallmark of cognitive ageing in primates: an overview of cognitive and neurobiological studies. Philos Trans R Soc Lond B Biol Sci 2020;375:20190618.
CrossRef
Google scholar
|
[19] |
Geerligs L, Saliasi E, Maurits NM, et al. Brain mechanisms underlying the effects of aging on different aspects of selective attention. Neuroimage 2014;91:52–62.
CrossRef
Google scholar
|
[20] |
Zelinski EM, Burnight KP. Sixteen-year longitudinal and time lag changes in memory and cognition in older adults. Psychol Aging 1997;12:503–13.
CrossRef
Google scholar
|
[21] |
Park DC, Lautenschlager G, Hedden T, et al. Models of visuospatial and verbal memory across the adult life span. Psychol Aging 2002;17:299–320.
CrossRef
Google scholar
|
[22] |
Hoogendam YY, Hofman A, van der Geest JN, et al. Patterns of cognitive function in aging: the Rotterdam Study. Eur J Epidemiol 2014;29:133–40.
CrossRef
Google scholar
|
[23] |
Verhaeghen P, Salthouse TA. Meta-analyses of age-cognition relations in adulthood: estimates of linear and nonlinear age effects and structural models. Psychol Bull 1997;122:231–49.
CrossRef
Google scholar
|
[24] |
Elliott ML, Belsky DW, Knodt AR, et al. Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Mol Psychiatry 2021;26:3829–38.
CrossRef
Google scholar
|
[25] |
Beker N, Ganz A, Hulsman M, et al. Association of cognitive function trajectories in centenarians with postmortem neuropathology, physical health, and other risk factors for cognitive decline. JAMA Netw Open 2021;4:e2031654.
CrossRef
Google scholar
|
[26] |
Llinàs-Reglà J, Vilalta-Franch J, López-Pousa S, et al. The trail making test. Assessment 2017;24:183–96.
CrossRef
Google scholar
|
[27] |
Fett AJ, Velthorst E, Reichenberg A, et al. Long-term changes in cognitive functioning in individuals with psychotic disorders: findings from the Suffolk county mental health project. JAMA Psychiatry 2020;77:387–96.
CrossRef
Google scholar
|
[28] |
Hoogendam YY, van der Lijn F, Vernooij MW, et al. Older age relates to worsening of fine motor skills: a population-based study of middle-aged and elderly persons. Front Aging Neurosci 2014;6:259–265.
CrossRef
Google scholar
|
[29] |
Quandt F, Bönstrup M, Schulz R, et al. Spectral variability in the aged brain during fine motor control. Front Aging Neurosci 2016;8:305.
CrossRef
Google scholar
|
[30] |
Ferdon S, Murphy C. The cerebellum and olfaction in the aging brain: a functional magnetic resonance imaging study. Neuroimage 2003;20:12–21.
CrossRef
Google scholar
|
[31] |
Dintica CS, Marseglia A, Rizzuto D, et al. Impaired olfaction is associated with cognitive decline and neurodegeneration in the brain. Neurology 2019;92:e700–9.
CrossRef
Google scholar
|
[32] |
Dong Y, Li Y, Liu K, et al. Anosmia, mild cognitive impairment, and biomarkers of brain aging in older adults. Alzheimers Dement 2023;19:589–601.
CrossRef
Google scholar
|
[33] |
Volkert J, Schulz H, Härter M, et al. The prevalence of mental disorders in older people in Western countries—a meta-analysis. Ageing Res Rev 2013;12:339–53.
CrossRef
Google scholar
|
[34] |
AM NM et al. Frailty, depression, and anxiety in later life. Int Psychogeriatr 2012;24:1265–74.
CrossRef
Google scholar
|
[35] |
Prenderville JA, Kennedy PJ, Dinan TG, et al. Adding fuel to the fire: the impact of stress on the ageing brain. Trends Neurosci 2015;38:13–25.
CrossRef
Google scholar
|
[36] |
Shafto MA, Tyler LK. Language in the aging brain: the network dynamics of cognitive decline and preservation. Science 2014;346:583–7.
CrossRef
Google scholar
|
[37] |
Fabricio DM, Chagas MHN, Diniz BS. Frailty and cognitive decline. Transl Res 2020;221:58–64.
CrossRef
Google scholar
|
[38] |
Chu NM, Xue Q-L, McAdams-DeMarco MA, et al. Frailty-a risk factor of global and domain-specific cognitive decline among a nationally representative sample of community-dwelling older adult U.S. Medicare beneficiaries. Age Ageing 2021;50:1569–77.
CrossRef
Google scholar
|
[39] |
Huang P, Zhang M. Magnetic resonance imaging studies of neurodegenerative disease: from methods to translational research. Neurosci Bull 2023;39:99–112.
CrossRef
Google scholar
|
[40] |
Kakimoto A, Ito S, Okada H, et al. Age-related sex-specific changes in brain metabolism and morphology. J Nucl Med 2016;57:221–5.
CrossRef
Google scholar
|
[41] |
Pini L, Pievani M, Bocchetta M, et al. Brain atrophy in Alzheimer’s disease and aging. Ageing Res Rev 2016;30:25–48.
CrossRef
Google scholar
|
[42] |
Habes M, Pomponio R, Shou H, et al; iSTAGING consortium, the Preclinical AD consortium, the ADNI, and the CARDIA studies. The Brain Chart of Aging: machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans. Alzheimers Dement 2021;17:89–102.
CrossRef
Google scholar
|
[43] |
Habes M, Erus G, Toledo JB, et al. White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain 2016;139:1164–79.
CrossRef
Google scholar
|
[44] |
Huang CC, Chou K-H, Lee W-J, et al. Brain white matter hyperintensities-predicted age reflects neurovascular health in middle-to-old aged subjects. Age Ageing 2022;51:1–10.
CrossRef
Google scholar
|
[45] |
Montandon ML, Herrmann FR, Garibotto V, et al. Microbleeds and medial temporal atrophy determine cognitive trajectories in normal aging: a longitudinal PET-MRI study. J Alzheimers Dis 2020;77:1431–42.
CrossRef
Google scholar
|
[46] |
Lim JS, Hong K-S, Kim G-M, et al. Cerebral microbleeds and early recurrent stroke after transient ischemic attack: results from the Korean Transient Ischemic Attack Expression Registry. JAMA Neurol 2015;72:301–8.
CrossRef
Google scholar
|
[47] |
Leal SL, Yassa MA. Perturbations of neural circuitry in aging, mild cognitive impairment, and Alzheimer’s disease. Ageing Res Rev 2013;12:823–31.
CrossRef
Google scholar
|
[48] |
Sala-Llonch, R, Bartrés-Faz D, Junqué C. Reorganization of brain networks in aging: a review of functional connectivity studies. Front Psychol 2015;6:663.
CrossRef
Google scholar
|
[49] |
Marstaller L, Williams M, Rich A, et al. Aging and large-scale functional networks: white matter integrity, gray matter volume, and functional connectivity in the resting state. Neuroscience 2015;290:369–78.
CrossRef
Google scholar
|
[50] |
Li X, Kehoe EG, McGinnity TM, et al. Modulation of effective connectivity in the default mode network at rest and during a memory task. Brain Connect 2015;5:60–7.
CrossRef
Google scholar
|
[51] |
Loessner A, Alavi A, Lewandrowski KU, et al. Regional cerebral function determined by FDG-PET in healthy volunteers: normal patterns and changes with age. J Nucl Med 1995;36:1141–9.
|
[52] |
Yoshizawa H, Gazes Y, Stern Y, et al. Characterizing the normative profile of 18F-FDG PET brain imaging: sex difference, aging effect, and cognitive reserve. Psychiatry Res 2014;221:78–85.
CrossRef
Google scholar
|
[53] |
Yanase D, Matsunari I, Yajima K, et al. Brain FDG PET study of normal aging in Japanese: effect of atrophy correction. Eur J Nucl Med Mol Imaging 2005;32:794–805.
CrossRef
Google scholar
|
[54] |
Pagani M, Giuliani A, Öberg J, et al. Progressive disintegration of brain networking from normal aging to Alzheimer disease: analysis of independent components of (18)F-FDG PET Data. J Nucl Med 2017;58:1132–9.
CrossRef
Google scholar
|
[55] |
Li Y, Choi WJ, Wei W, et al. Aging-associated changes in cerebral vasculature and blood flow as determined by quantitative optical coherence tomography angiography. Neurobiol Aging 2018;70:148–59.
CrossRef
Google scholar
|
[56] |
Huang S, Wang YJ, Guo J. Biofluid biomarkers of Alzheimer’s disease: progress, problems, and perspectives. Neurosci Bull 2022;38:677–91.
CrossRef
Google scholar
|
[57] |
Chiu MJ, Fan L-Y, Chen T-F, et al. Plasma tau levels in cognitively normal middle-aged and older adults. Front Aging Neurosci 2017;9:51.
CrossRef
Google scholar
|
[58] |
Cantero JL, Atienza M, Ramos-Cejudo J, et al. Plasma tau predicts cerebral vulnerability in aging. Aging (Albany NY) 2020;12:21004–22.
CrossRef
Google scholar
|
[59] |
Cavedo, E, Lista S, Houot M, et al., Alzheimer Precision Medicine Initiative; INSIGHT-preAD Study Group. Plasma tau correlates with basal forebrain atrophy rates in people at risk for Alzheimer disease. Neurology 2020;94:e30–41.
CrossRef
Google scholar
|
[60] |
Kaeser S et al. A neuronal blood marker is associated with mortality in old age. Nature Aging 2021;1:218–225.
CrossRef
Google scholar
|
[61] |
Dittrich A, Ashton NJ, Zetterberg H, et al. Plasma and CSF NfL are differentially associated with biomarker evidence of neurodegeneration in a community-based sample of 70-year-olds. Alzheimers Dement (Amst) 2022;14:e12295.
CrossRef
Google scholar
|
[62] |
Khalil M, Pirpamer L, Hofer E, et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat Commun 2020;11:812.
CrossRef
Google scholar
|
[63] |
Henjum K, Almdahl IS, Årskog V, et al. Cerebrospinal fluid soluble TREM2 in aging and Alzheimer’s disease. Alzheimers Res Ther 2016;8:17.
CrossRef
Google scholar
|
[64] |
Tsai HH, Chen Y-F, Yen R-F, et al. Plasma soluble TREM2 is associated with white matter lesions independent of amyloid and tau. Brain 2021;144:3371–80.
CrossRef
Google scholar
|
[65] |
Park SH, Lee E-H, Kim H-J, et al. The relationship of soluble TREM2 to other biomarkers of sporadic Alzheimer’s disease. Sci Rep 2021;11:13050.
CrossRef
Google scholar
|
[66] |
Zhao A, Jiao Y, Ye G, et al. Soluble TREM2 levels associate with conversion from mild cognitive impairment to Alzheimer’s disease. J Clin Invest 2022;132:1–11.
CrossRef
Google scholar
|
[67] |
Abdelhak A, Hottenrott T, Morenas-Rodríguez E, et al. Glial activation markers in CSF and serum from patients with primary progressive multiple sclerosis: potential of serum GFAP as disease severity marker? Front Neurol 2019;10:280.
CrossRef
Google scholar
|
[68] |
Abdelhak A, Foschi M, Abu-Rumeileh S, et al. Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Rev Neurol 2022;18:158–72.
CrossRef
Google scholar
|
[69] |
Korley FK, Jain S, Sun X, et al; TRACK-TBI Study Investigators. Prognostic value of day-of-injury plasma GFAP and UCH-L1 concentrations for predicting functional recovery after traumatic brain injury in patients from the US TRACK-TBI cohort: an observational cohort study. Lancet Neurol 2022;21:803–13.
CrossRef
Google scholar
|
[70] |
Krekoski CA, Parhad IM, Fung TS, et al. Aging is associated with divergent effects on Nf-L and GFAP transcription in rat brain. Neurobiol Aging 1996;17:833–41.
CrossRef
Google scholar
|
[71] |
Anderson CP, Rozovsky I, Stone DJ, et al. Aging and increased hypothalamic glial fibrillary acid protein (GFAP) mRNA in F344 female rats. Dissociation of GFAP inducibility from the luteinizing hormone surge. Neuroendocrinology 2002;76:121–30.
CrossRef
Google scholar
|
[72] |
Wruck W, Adjaye J. Meta-analysis of human prefrontal cortex reveals activation of GFAP and decline of synaptic transmission in the aging brain. Acta Neuropathol Commun 2020;8:26.
CrossRef
Google scholar
|
[73] |
Bettcher BM, Olson KE, Carlson NE, et al. Astrogliosis and episodic memory in late life: higher GFAP is related to worse memory and white matter microstructure in healthy aging and Alzheimer’s disease. Neurobiol Aging 2021;103:68–77.
CrossRef
Google scholar
|
[74] |
Han X, Lei Q, Xie J, et al. Potential regulators of the senescence-associated secretory phenotype during senescence and aging. J Gerontol A Biol Sci Med Sci 2022;77:2207–18.
CrossRef
Google scholar
|
[75] |
Wang P, Yu Le, Gong J, et al. An activity-based fluorescent probe for imaging fluctuations of peroxynitrite (ONOO(-)) in the Alzheimer’s disease brain. Angew Chem Int Ed Engl 2022;61:e202206894.
CrossRef
Google scholar
|
[76] |
Ono T, Uehara Y, Kurishita A, et al. Biological significance of DNA methylation in the ageing process. Age Ageing 1993;22:S34–43.
CrossRef
Google scholar
|
[77] |
Junnila RK, List EO, Berryman DE, et al. The GH/IGF-1 axis in ageing and longevity. Nat Rev Endocrinol 2013;9:366–76.
CrossRef
Google scholar
|
[78] |
Liu XM, Chan HC, Ding G-L, et al. FSH regulates fat accumulation and redistribution in aging through the Gαi/Ca(2+)/CREB pathway. Aging Cell 2015;14:409–20.
CrossRef
Google scholar
|
[79] |
Schafer MJ, Atkinson EJ, Vanderboom PM, et al. Quantification of GDF11 and myostatin in human aging and cardiovascular disease. Cell Metab 2016;23:1207–15.
CrossRef
Google scholar
|
[80] |
Crunkhorn S. Aging: promoting NAD(+) production. Nat Rev Drug Discov 2018;17:864.
CrossRef
Google scholar
|
[81] |
Paixao L, Sikka P, Sun H, et al. Excess brain age in the sleep electroencephalogram predicts reduced life expectancy. Neurobiol Aging 2020;88:150–5.
CrossRef
Google scholar
|
[82] |
Baecker L, Garcia-Dias R, Vieira S, et al. Machine learning for brain age prediction: introduction to methods and clinical applications. EBioMedicine 2021;72:103600.
CrossRef
Google scholar
|
[83] |
Yook S, Park HR, Park C, et al. Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders. Neuroimage 2022;264:119753.
CrossRef
Google scholar
|
[84] |
Liem F, Varoquaux G, Kynast J, et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 2017;148:179–88.
CrossRef
Google scholar
|
[85] |
Millar PR, Gordon BA, Luckett PH, et al. Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. Elife 2023;12:1–25.
CrossRef
Google scholar
|
/
〈 | 〉 |