Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression.
Alzheimer's disease
amnestic mild cognitive impairment
biomarker
cerebrospinal fluid
fMRI
machine learning
personality
resting-state
subjective cognitive decline
support vector machine
Journal
International journal of geriatric psychiatry
ISSN: 1099-1166
Titre abrégé: Int J Geriatr Psychiatry
Pays: England
ID NLM: 8710629
Informations de publication
Date de publication:
10 2023
10 2023
Historique:
received:
30
11
2022
accepted:
07
09
2023
medline:
30
10
2023
pubmed:
6
10
2023
entrez:
6
10
2023
Statut:
ppublish
Résumé
Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.
Sections du résumé
BACKGROUND
Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages.
METHODS
In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE).
RESULTS
Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets.
CONCLUSION
Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.
Substances chimiques
Amyloid beta-Peptides
0
Apolipoproteins E
0
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e6007Informations de copyright
© 2023 The Authors. International Journal of Geriatric Psychiatry published by John Wiley & Sons Ltd.
Références
Albert MS , DeKosky ST , Dickson D , et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):270-279. https://doi.org/10.1016/j.jalz.2011.03.008
Jessen F , Amariglio RE , Boxtel M , et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s & Dementia. 2014;10(6):844-852. https://doi.org/10.1016/j.jalz.2014.01.001
Blennow K , Hampel H , Weiner M , Zetterberg H . Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol. 2010;6(3):131-144. https://doi.org/10.1038/nrneurol.2010.4
Sperling RA , Aisen PS , Beckett LA , et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia. 2011;7(3):280-292. https://doi.org/10.1016/j.jalz.2011.03.003
Binnewijzend MAA , Schoonheim MM , Sanz-Arigita E , et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging. 2012;33(9):2018-2028. https://doi.org/10.1016/j.neurobiolaging.2011.07.003
Buchhave P , Minthon L , Zetterberg H . Cerebrospinal fluid levels of -Amyloid 1-42, but not of tau, are fully changed already 5 to 10 Years before the onset of alzheimer dementia. ARCH GEN PSYCHIATRY. 2012;69:9.
Badhwar A , Tam A , Dansereau C , Orban P , Hoffstaedter F , Bellec P . Resting-state network dysfunction in Alzheimer’s disease: a systematic review and meta-analysis. Alzheimer’s & Dementia Diagnosis, Assess & Dis Monit. 2017;8(1):73-85. https://doi.org/10.1016/j.dadm.2017.03.007
Jessen F , Spottke A , Boecker H , et al. Design and first baseline data of the DZNE multicenter observational study on predementia Alzheimer’s disease (DELCODE). Alzheimer’s Res & Ther. 2018;10(1):15. https://doi.org/10.1186/s13195-017-0314-2
Olsson B , Lautner R , Andreasson U , et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurology. 2016;15(7):673-684. https://doi.org/10.1016/s1474-4422(16)00070-3
McCrae RR , Costa PT . Validation of the five-factor model of personality across instruments and observers. J Pers Soc Psychol. 1987;52(1):81-90. https://doi.org/10.1037/0022-3514.52.1.81
Mendez Rubio M , Antonietti JP , Donati A , Rossier J , von Gunten A . Personality traits and behavioural and psychological symptoms in patients with mild cognitive impairment. Dement Geriatr Cogn Disord. 2013;35(1-2):87-97. https://doi.org/10.1159/000346129
Yoneda T , Rush J , Berg AI , Johansson B , Piccinin AM . Trajectories of personality traits preceding dementia diagnosis. GERONB. 2016:gbw006. https://doi.org/10.1093/geronb/gbw006
Terracciano A , Stephan Y , Luchetti M , Albanese E , Sutin AR . Personality traits and risk of cognitive impairment and dementia. J Psychiatr Res. 2017;89:22-27. https://doi.org/10.1016/j.jpsychires.2017.01.011
Caselli RJ , Langlais BT , Dueck AC , et al. Personality changes during the transition from cognitive health to mild cognitive impairment. J Am Geriatr Soc. 2018;66(4):671-678. https://doi.org/10.1111/jgs.15182
Duchek JM , Balota DA , Storandt M , Larsen R . The power of personality in discriminating between healthy aging and early-stage Alzheimer’s disease. Journals Gerontology Ser Bibliogr. 2007;62(6):P353-P361. https://doi.org/10.1093/geronb/62.6.p353
Robins Wahlin T.-B , Byrne GJ . Personality changes in Alzheimer’s disease: a systematic review. Int J Geriatr Psychiatr. 2011;26(10):1019-1029. https://doi.org/10.1002/gps.2655
Kotov R , Gamez W , Schmidt F , Watson D . Linking “big” personality traits to anxiety, depressive, and substance use disorders: a meta-analysis. Psychol Bull. 2010;136(5):768-821. https://doi.org/10.1037/a0020327
Klein DN , Kotov R , Bufferd SJ . Personality and depression: explanatory models and review of the evidence. Annu Rev Clin Psychol. 2011;7(1):269-295. https://doi.org/10.1146/annurev-clinpsy-032210-104540
Hakulinen C , Elovainio M , Pulkki-Råback L , Virtanen M , Kivimäki M , Jokela M . Personality and depressive symptoms: individual participant meta-analysis of 10 cohort studies. Depress Anxiety. 2015;32(7):461-470. https://doi.org/10.1002/da.22376
Costa PT, Jr. , McCrae RR . The revised NEO personality inventory (NEO-PI-R). In: The SAGE handbook of personality theory and assessment, Vol 2: Personality measurement and testing. Sage Publications, Inc; 2008:179-198.
Soto CJ , John OP . Ten facet scales for the Big Five Inventory: convergence with NEO PI-R facets, self-peer agreement, and discriminant validity. J Res Pers. 2009;43(1):84-90. https://doi.org/10.1016/j.jrp.2008.10.002
Hill NL , Mogle J , Wion R , et al. Subjective cognitive impairment and affective symptoms: a systematic review. Gerontologist. 2016;56(6):e109-e127. https://doi.org/10.1093/geront/gnw091
Ismail Z , Elbayoumi H , Fischer CE , et al. Prevalence of depression in patients with mild cognitive impairment: a systematic review and meta-analysis. JAMA Psychiatr. 2017;74(1):58-67. https://doi.org/10.1001/jamapsychiatry.2016.3162
Mirza SS , Ikram MA , Bos D , Mihaescu R , Hofman A , Tiemeier H . Mild cognitive impairment and risk of depression and anxiety: a population-based study. Alzheimer’s & Dementia. 2017;13(2):130-139. https://doi.org/10.1016/j.jalz.2016.06.2361
Leung DKY , Chan WC , Spector A , Wong GHY . Prevalence of depression, anxiety, and apathy symptoms across dementia stages: a systematic review and meta-analysis. Int J Geriatr Psychiatr. 2021;36(9):1330-1344. https://doi.org/10.1002/gps.5556
Palmer K , Berger AK , Monastero R , Winblad B , Bäckman L , Fratiglioni L . Predictors of progression from mild cognitive impairment to Alzheimer disease. Neurology. 2007;68(19):1596[-1602. https://doi.org/10.1212/01.wnl.0000260968.92345.3f
Li J.-Q , Tan L , Wang H.-F , et al. Risk factors for predicting progression from mild cognitive impairment to Alzheimer’s disease: a systematic review and meta-analysis of cohort studies. J Neurol Neurosurg Psychiatry. 2016;87(5):476-484. https://doi.org/10.1136/jnnp-2014-310095
Li XX , Li Z . The impact of anxiety on the progression of mild cognitive impairment to dementia in Chinese and English data bases: a systematic review and meta-analysis. Int J Geriatr Psychiatr. 2018;33(1):131-140. https://doi.org/10.1002/gps.4694
Peakman G , Karunatilake N , Seynaeve M , et al. Clinical factors associated with progression to dementia in people with late-life depression: a cohort study of patients in secondary care. BMJ Open. 2020;10(S10):e035147. https://doi.org/10.1002/alz.039147
Cooper C , Sommerlad A , Lyketsos CG , Livingston G . Modifiable predictors of dementia in mild cognitive impairment: a systematic review and meta-analysis. Aust J Pharm. 2015;172(4):323-334. https://doi.org/10.1176/appi.ajp.2014.14070878
Ly M , Karim HT , Becker JT , et al. Late-life depression and increased risk of dementia: a longitudinal cohort study. Transl Psychiatry. 2021;11(1):147. https://doi.org/10.1038/s41398-021-01269-y
Panza F , Frisardi V , Capurso C , et al. Late-life depression, mild cognitive impairment, and dementia: possible continuum? Am J Geriatric Psychiatry. 2010;18(2):98-116. https://doi.org/10.1097/jgp.0b013e3181b0fa13
Singh-Manoux A , Dugravot A , Fournier A , et al. Trajectories of depressive symptoms before diagnosis of dementia: a 28-year follow-up study. JAMA Psychiatr. 2017;74(7):712-718. https://doi.org/10.1001/jamapsychiatry.2017.0660
Invernizzi S , Simoes Loureiro I , Kandana Arachchige KG , Lefebvre L . Late-life depression, cognitive impairment, and relationship with Alzheimer’s disease. Dementia Geriatric Cognitive Disord. 2021;50(5):414-424. https://doi.org/10.1159/000519453
Raichle ME , MacLeod AM , Snyder AZ , Powers WJ , Gusnard DA , Shulman GL . A default mode of brain function. Proc Natl Acad Sci USA. 2001;98(2):676-682. https://doi.org/10.1073/pnas.98.2.676
Andrews-Hanna JR , Smallwood J , Spreng RN . The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014;1316(1):29-52. https://doi.org/10.1111/nyas.12360
Jia X.-Z , Sun J.-W , Ji G.-J , et al. Percent amplitude of fluctuation: a simple measure for resting-state fMRI signal at single voxel level. PLOS ONE. 2020;15(1):e0227021. https://doi.org/10.1371/journal.pone.0227021
Akansha M , Roberto AJ , Abhishek M , et al. The significance of the default mode network (DMN) in neurological and neuropsychiatric disorders: a review. Yale J Biol Med. 2016;89:49-57.
Mevel K , Chételat G , Eustache F , Desgranges B . The default mode network in healthy aging and Alzheimer’s disease. Int J Alzheimer's Dis. 2011;2011:1-9. https://doi.org/10.4061/2011/535816
Cha J , Jo HJ , Kim HJ , et al. Functional alteration patterns of default mode networks: comparisons of normal aging, amnestic mild cognitive impairment and Alzheimer’s disease. Eur J Neurosci. 2013;37(12):1916-1924. https://doi.org/10.1111/ejn.12177
Dubois B , Feldman HH , Jacova C , et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurology. 2014;13(6):614-629. https://doi.org/10.1016/s1474-4422(14)70090-0
Jansen WJ , Ossenkoppele R , Knol DL , et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA. 2015;313(19):1924. https://doi.org/10.1001/jama.2015.4668
Hansson O , Seibyl J , Stomrud E , et al. CSF biomarkers of Alzheimer’s disease concord with amyloid-β PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimer’s & Dementia. 2018;14(11):1470-1481. https://doi.org/10.1016/j.jalz.2018.01.010
Leuzy A , Ashton NJ , Mattsson-Carlgren N , et al. 2020 update on the clinical validity of cerebrospinal fluid amyloid, tau, and phospho-tau as biomarkers for Alzheimer’s disease in the context of a structured 5-phase development framework. Eur J Nucl Med Mol Imag. 2021;48(7):2121-2139. https://doi.org/10.1007/s00259-021-05258-7
Ramzan F , Khan MUG , Rehmat A , et al. A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. J Med Syst. 2019;44(2):37. https://doi.org/10.1007/s10916-019-1475-2
McKhann GM , Knopman DS , Chertkow H , et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia. 2011;7(3):263-269. https://doi.org/10.1016/j.jalz.2011.03.005
Düzel E , Acosta-Cabronero J , Berron D , et al. European ultrahigh-field imaging network for neurodegenerative diseases (EUFIND). Alzheimer’s & dementia: diagnosis. Assessment & Disease Monitoring. 2019;11(1):538-549. https://doi.org/10.1016/j.dadm.2019.04.010
Jia X.-Z , Wang J , Sun HY , et al. RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing. Sci Bull. 2019;64(14):953-954. https://doi.org/10.1016/j.scib.2019.05.008
Kizilirmak JM , Soch J , Schütze H , et al. The Relationship between Resting-State Amplitude Fluctuations and Memory-Related Deactivations of the Default Mode Network in Young and Older Adults; 2022.
Shirer WR , Ryali S , Rykhlevskaia E , Menon V , Greicius MD . Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebr Cortex. 2012;22(1):158-165. https://doi.org/10.1093/cercor/bhr099
Sheikh JI , Yesavage JA . Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol J Aging Ment Health. 1986.
Byrne GJ , Pachana NA . Development and validation of a short form of the geriatric anxiety inventory - the GAI-SF. Int Psychogeriatr. 2011;23(1):125-131. https://doi.org/10.1017/s1041610210001237
Rammstedt B , John OP . Measuring personality in one minute or less: a 10-item short version of the Big Five Inventory in English and German. J Res Pers. 2007;41(1):203-212. https://doi.org/10.1016/j.jrp.2006.02.001
Rammstedt B , Kemper CJ , Klein MC , Beierlein C , Kovaleva A . A short scale for assessing the big five dimensions of personality: 10 item big five inventory (BFI-10). methods data. 2017:17.
Puechmaille SJ . The program structure does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem. Molecular Ecology Resources. 2016;16(3):608-627. https://doi.org/10.1111/1755-0998.12512
Schouten TM , Koini M , de Vos F , et al. Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer’s disease. Neuroimage Clinical. 2016;11:46-51. https://doi.org/10.1016/j.nicl.2016.01.002
Gill S , Mouches P , Hu S , et al. Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data. J Alzheim Dis. 2020;75(1):277-288. https://doi.org/10.3233/jad-191169
Jo T , Nho K , Saykin AJ . Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci. 2019;11:220. https://doi.org/10.3389/fnagi.2019.00220
Hansen N , Singh A , Bartels C , et al. Hippocampal and hippocampal-subfield volumes from early-onset major depression and bipolar disorder to cognitive decline. Front Aging Neurosci. 2021;13. https://doi.org/10.3389/fnagi.2021.626974
Lleó A , Parnetti L , Belbin O , Wiltfang J . Has the time arrived for cerebrospinal fluid biomarkers in psychiatric disorders? Clin Chim Acta. 2019;491:81-84. https://doi.org/10.1016/j.cca.2019.01.019
Düzel E , Ziegler G , Berron D , et al. Amyloid pathology but not APOE ε4 status is permissive for tau-related hippocampal dysfunction. Brain. 2022;145(4):1473-1485. https://doi.org/10.1093/brain/awab405
Chételat G , Landeau B , Eustache F , et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage. 2005;27(4):934-946. https://doi.org/10.1016/j.neuroimage.2005.05.015
Johns EK , Phillips NA , Belleville S , et al. The profile of executive functioning in amnestic mild cognitive impairment: disproportionate deficits in inhibitory control. J Int Neuropsychological Soc. 2012;18(03):541-555. https://doi.org/10.1017/s1355617712000069
Bessi V , Mazzeo S , Padiglioni S , et al. From subjective cognitive decline to Alzheimer’s disease: the predictive role of neuropsychological assessment, personality traits, and cognitive reserve. A 7-year follow-up study. JAD. 2018;63(4):1523-1535. https://doi.org/10.3233/jad-171180
Leicht H , Berwig M , Gertz H.-J . Anosognosia in Alzheimer’s disease: the role of impairment levels in assessment of insight across domains. J Int Neuropsychological Soc. 2010;16(3):463-473. https://doi.org/10.1017/s1355617710000056
Orfei MD , Blundo C , Celia E , et al. Anosognosia in mild cognitive impairment and mild Alzheimer’s disease: frequency and neuropsychological correlates. Am J Geriatric Psychiatry. 2010;18(12):1133-1140. https://doi.org/10.1097/jgp.0b013e3181dd1c50
de Ruijter NS , Schoonbrood AMG , van Twillert B , Hoff EI . Anosognosia in dementia: a review of current assessment instruments. Alzheimer’s & Dementia Diagnosis, Assess & Dis Monit. 2020;12(1):e12079. https://doi.org/10.1002/dad2.12079
Starkstein SE . Anosognosia in Alzheimer’s disease: diagnosis, frequency, mechanism and clinical correlates. Cortex. 2014;61:64-73. https://doi.org/10.1016/j.cortex.2014.07.019
Agϋera-Ortiz L , Lyketsos C , Ismail Z . Comment on “personality changes during the transition from cognitive health to mild cognitive impairment”. J Am Geriatr Soc. 2019;67(1):190-191. https://doi.org/10.1111/jgs.15615
Terracciano A , Sutin AR . Personality and Alzheimer’s disease: an integrative review. Personality Disord Theory, Research, and Treatment. 2019;10(1):4-12. https://doi.org/10.1037/per0000268
Holm S . A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6:65-70.