Mapping the Neuropsychiatric Symptoms in Alzheimer’s Disease Using Biomarkers, Cognitive Abilities, and Personality Traits: A Systematic Review
Abstract
:1. Introduction
Objectives
2. Materials and Methods
2.1. Search Strategy
2.2. Eligibility Criteria
2.3. Study Selection
2.4. Data Synthesis
3. Results
3.1. The Included Papers
3.2. Characteristics of the Included Studies
3.3. Findings Related to NPS in General and to Specific Ones
3.3.1. Findings Related to NPS in General
3.3.2. Depression
3.3.3. Apathy
3.3.4. Anxiety
3.3.5. Agitation and Other Frontal Symptoms
3.3.6. Νight-Time Behavior
3.3.7. Psychotic Symptoms
3.3.8. Appetite Disorders
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Selection | Comparability | Outcome | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Study | Representative of the Exposed Cohort | Selection of Non-Exposed Cohort | Ascertainment of Exposure | Outcome of the Interest Not Present at the Start of the Study | Main Factor | Additional Factor | Assessment of Outcomes | Sufficient Follow-Up Time | Adequacy of Follow-Up | Total |
Liguori, 2018 [24] | * | * | * | * | * | * | * | * | * | 9/9 |
Ruthirakuhan, 2019 [25] | - | - | * | * | * | * | * | * | * | 7/9 |
Banning, 2020 [26] | * | * | * | * | * | * | * | * | * | 9/9 |
Burhanullah, 2020 [27] | * | * | * | * | * | * | * | * | * | 9/9 |
Huang, 2020 [28] | * | * | * | * | * | * | * | * | * | 9/9 |
Almdahl, 2023 [29] | * | * | * | * | * | * | * | * | * | 9/9 |
Binette, 2021 [30] | * | * | * | * | * | * | * | * | - | 8/9 |
Babulal, 2022 [31] | * | * | * | * | * | - | * | * | * | 8/9 |
Chan, 2022 [32] | * | * | * | * | * | * | * | * | * | 9/9 |
Clark, 2022 [33] | - | - | * | * | * | * | * | * | * | 7/9 |
Johansson, 2022 [34] | * | * | * | * | * | * | * | * | * | 9/9 |
Kim, 2022 [35] | * | * | * | - | * | * | * | * | - | 7/9 |
Babulal, 2023 [36] | * | * | * | * | * | * | * | * | - | 8/9 |
Li, 2023 [37] | * | * | * | * | * | * | * | * | * | 9/9 |
Marquié, 2023 [38] | * | * | * | * | * | * | * | * | * | 9/9 |
Pink, 2023 [39] | * | - | * | * | * | * | * | * | * | 8/9 |
Burling, 2024 [40] | * | * | * | * | * | * | * | * | * | 9/9 |
Guan, 2024 [41] | * | * | * | * | * | * | * | * | * | 9/9 |
Ronat, 2024 [42] | * | * | * | * | * | * | * | * | * | 9/9 |
Ronat, 2024 [43] | * | * | * | * | * | * | * | * | * | 9/9 |
Rabl, 2022 [44] | - | - | * | * | * | * | * | * | * | 7/9 |
Ismail, 2023 [45] | * | * | * | * | * | * | * | * | * | 9/9 |
Ghahremani, 2023 [46] | * | * | * | * | * | * | * | * | * | 9/9 |
Jiang, 2024 [47] | * | * | * | * | * | * | * | * | * | 9/9 |
Wang, 2019 [48] | * | - | * | - | * | * | * | - | - | 5/9 |
Banning, 2020 [49] | * | - | * | - | * | * | * | - | - | 5/9 |
Sannermann, 2020 [50] | * | * | * | - | * | * | * | - | - | 6/9 |
Cotta Ramusino, 2021 [51] | * | - | * | - | * | * | * | - | - | 5/9 |
De Oliveira, 2021 [52] | * | * | * | - | * | * | * | - | - | 6/9 |
Jacobs, 2021 [53] | * | * | * | - | * | * | * | - | - | 6/9 |
Siafarikas, 2021 [54] | * | * | * | - | * | * | * | - | - | 6/9 |
Dang, 2022 [55] | * | * | * | - | * | * | * | - | - | 6/9 |
Henjum, 2022 [56] | * | - | * | - | * | * | * | - | - | 5/9 |
Kan, 2022 [57] | * | * | * | - | * | - | * | - | - | 5/9 |
Krell-Roesch, 2022 [58] | * | - | * | - | * | * | * | - | - | 5/9 |
Manca, 2022 [59] | * | * | * | - | * | * | * | - | - | 6/9 |
Miao, 2022 [60] | * | * | * | - | * | * | * | - | - | 6/9 |
Waschkies, 2022 [61] | * | * | * | - | * | * | * | - | - | 6/9 |
Aguzzoli, 2023 [62] | * | * | * | - | * | * | * | - | - | 6/9 |
De Lucia, 2023 [63] | * | - | * | - | * | * | * | - | - | 5/9 |
Greig Custo, 2023 [64] | * | * | * | - | * | * | * | - | - | 6/9 |
Jiang, 2023 [65] | * | - | * | - | * | * | * | - | - | 5/9 |
Kan, 2023 [66] | * | * | * | - | * | * | * | - | - | 6/9 |
Kim, 2023 [67] | * | * | * | - | * | * | * | - | - | 6/9 |
Krell-Roesch, 2023 [68] | * | * | * | - | * | * | * | - | - | 5/9 |
Ozaki, 2023 [69] | - | * | * | - | * | * | * | - | - | 5/9 |
Falgas, 2024 [70] | * | * | * | - | * | * | * | - | - | 6/9 |
Frank, 2024 [71] | * | - | * | - | * | * | * | - | - | 5/9 |
Huang, 2024 [72] | * | * | * | - | * | * | * | - | - | 5/9 |
Hsu, 2024 [73] | * | * | * | - | * | * | * | - | - | 6/9 |
References
- Ng, K.P.; Chiew, H.; Rosa-Neto, P.; Kandiah, N.; Ismail, Z.; Gauthier, S. Associations of AT (N) biomarkers with neuropsychiatric symptoms in preclinical Alzheimer’s disease and cognitively unimpaired individuals. Transl. Neurodegener. 2021, 10, 11. [Google Scholar] [CrossRef] [PubMed]
- Jack, C.; Bennet, D.; Blennow, K.; Carillo, M.; Feldman, H.; Frisoni, G.; Hampel, H.; Jagust, W.; Johnson, K.; Knopman, D.; et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 2016, 87, 539–547. [Google Scholar] [CrossRef]
- Jack, C.; Bennett, D.; Blennow, K.; Carrillo, M.; Dunn, B.; Haeberlein, S.; Holtzman, D.; Jagust, W.; Jenssen, F.; Karlawish, J.; et al. NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018, 14, 535–562. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. WHO Global Strategy and Action Plan on Ageing and Health; WHO: Geneva, Switzerland, 2016. [Google Scholar]
- American Psychiatric Association. Washington American Psychiatric Association. In Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Washington, DC, USA, 2013. [Google Scholar]
- Winblad, B.; Palmer, K.; Kivipelto, M.; Jelic, V.; Fratiglioni, L.; Wahlund, L.O.; Petersen, R.C.; Nordberg, A.; Bäckman, L.; Albert, M.; et al. Mild cognitive impairment—Beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 2004, 256, 240–246. [Google Scholar] [CrossRef]
- Mitchell, A.J.; Shiri-Feshki, M. Rate of progression of mild cognitive impairment to dementia-meta-analysis of 41 robust inception cohort studies. Acta Neurol. Scand. 2009, 119, 252–265. [Google Scholar] [CrossRef] [PubMed]
- Davis, M.; O’Connell, T.; Johnson, S.; Cline, S.; Merikle, E.; Martenyi, F.; Simpson, K. Estimating Alzheimer’s disease progression rates from normal cognition through mild cognitive impairment and stages of dementia. Curr. Alzheimer Res. 2018, 15, 777–788. [Google Scholar] [CrossRef] [PubMed]
- Geda, Y.E.; Schneider, L.S.; Gitlin, L.N.; Miller, D.S.; Smith, G.S.; Bell, J.; Evans, J.; Lee, M.; Porsteinsson, A.; Lanktot, K. Neuropsychiatric symptoms in Alzheimer’s disease: Past progress and anticipation of the future. Alzheimers Dement. 2013, 9, 602–608. [Google Scholar] [CrossRef]
- Kales, H.C.; Gitlin, L.N.; Lyketsos, C.G. Management of neuropsychiatric symptoms of dementia in clinical settings: Recommendations from a multidisciplinary expert panel. Am. Geriatr. Soc. 2014, 62, 762–769. [Google Scholar] [CrossRef]
- Cooper, C.; Sommerlad, A.; Lyketsos, C.G.; Livingston, G. Modifiable predictors of dementia in mild cognitive impairment: A systematic review and meta-analysis. Am. J. Psychiatry 2015, 172, 323–334. [Google Scholar] [CrossRef]
- Zhao, Q.F.; Tan, L.; Wang, H.F.; Jiang, T.; Tan, M.S.; Tan, L.; Xu, W.; Li, J.Q.; Wang, J.; Lai, T.J. The prevalence of neuropsychiatric symptoms in Alzheimer’s disease: Systematic review and meta-analysis. J. Affect. Disord. 2016, 190, 264–271. [Google Scholar] [CrossRef]
- Tascone, L.S.; Payne, M.E.; MacFall, J.; Azevedo, D.; de Castro, C.C.; Steffens, D.C.; Busatto, G.F.; Bottino, C. Cortical brain volume abnormalities associated with few or multiple neuropsychiatric symptoms in Alzheimer’s disease. PLoS ONE 2017, 12, e0177169. [Google Scholar] [CrossRef]
- Wise, E.A.; Rosenberg, P.B.; Lyketsos, C.G.; Leoutsakos, J.M. Time course of neuropsychiatric symptoms and cognitive diagnosis in National Alzheimer’s Coordinating Centers volunteers. Alzheimers Dement. Diagn. 2019, 11, 333–339. [Google Scholar] [CrossRef] [PubMed]
- Guan, D.X.; Mortby, M.E.; Pike, G.B.; Ballard, C.; Creese, B.; Corbett, A.; Pickering, E.; Hampshire, A.; Roach, P.; Smith, E.; et al. Linking cognitive and behavioral reserve: Evidence from the CAN-PROTECT study. Alzheimers Dement. 2024, 10, e12497. [Google Scholar] [CrossRef]
- Martin, E.; Velayudhan, L. Neuropsychiatric symptoms in mild cognitive impairment: A literature review. Dement. Geriatr. Cogn. Disord. 2020, 49, 146–155. [Google Scholar] [CrossRef] [PubMed]
- Porsteinsson, A.P.; Antonsdottir, I.M. Neuropsychiatric symptoms in dementia: A cause or consequence? Am. J. Geriatr. Psychiatry 2015, 172, 410–411. [Google Scholar] [CrossRef]
- Young, J.J.; Balachandran, S.; Garg, G.; Balasubramaniam, M.; Gupta, A.; Tampi, D.J.; Tampi, R.R. Personality and the risk factors for developing behavioral and psychological symptoms of dementia: A narrative review. Neurodegener. Dis. Manag. 2019, 9, 107–118. [Google Scholar] [CrossRef] [PubMed]
- Gerlach, L.B.; Kales, H.C. Managing behavioral and psychological symptoms of dementia. Psychiatr. Clin. 2018, 41, 127–139. [Google Scholar] [CrossRef]
- Hodgson, N.A.; Gitlin, L.N.; Winter, L.; Czekanski, K. Undiagnosed illness and neuropsychiatric behaviors in community residing older adults with dementia. Alzheimer Dis. Assoc. Disord. 2011, 25, 109–115. [Google Scholar] [CrossRef]
- Gerlach, L.B.; Kales, H.C. Learning their language: The importance of detecting and managing pain in dementia. Am. J. Geriatr. Psychiatry 2017, 25, 155–157. [Google Scholar] [CrossRef]
- Khan, Z.; Da Silva, M.V.; Nunez, K.M.; Kalafatis, C.; Nowicki, S.; Walker, Z.; Testad, I.; Francis, P.; Ballard, C. Investigating the effects of impairment in non-verbal communication on neuropsychiatric symptoms and quality of life of people living with dementia. Alzheimers Dement. 2021, 7, e12172. [Google Scholar] [CrossRef]
- Miranda-Castillo, C.; Woods, B.; Galboda, K.; Oomman, S.; Olojugba, C.; Orrell, M. Unmet needs, quality of life and support networks of people with dementia living at home. Health Qual. Life Outcomes 2010, 8, 132. [Google Scholar] [CrossRef] [PubMed]
- Liguori, C.; Pierantozzi, M.; Chiaravalloti, A.; Sancesario, G.M.; Mercuri, N.B.; Franchini, F.; Sancesario, G.M.; Mercuri, N.; Franchini, F.; Schillaci, O.; et al. When cognitive decline and depression coexist in the elderly: CSF biomarkers analysis can differentiate Alzheimer’s disease from late-life depression. Front. Aging Neurosci. 2018, 10, 38. [Google Scholar] [CrossRef] [PubMed]
- Ruthirakuhan, M.; Herrmann, N.; Andreazza, A.C.; Verhoeff, N.P.; Gallagher, D.; Black, S.E.; Kiss, A.; Lanctôt, K.L. Agitation, oxidative stress, and cytokines in Alzheimer disease: Biomarker analyses from a clinical trial with nabilone for agitation. J. Geriatr. Psychiatry Neurol. 2019, 33, 175–184. [Google Scholar] [CrossRef]
- Banning, L.C.; Ramakers, I.H.; Köhler, S.; Bron, E.E.; Verhey, F.R.; De Deyn, P.P.; Claasen, J.; Koek, H.; Middelkoop, H.; van der Flier, W.; et al. The association between biomarkers and neuropsychiatric symptoms across the Alzheimer’s disease spectrum. Am. J. Geriatr. Psychiatry 2020, 28, 735–744. [Google Scholar] [CrossRef]
- Burhanullah, M.H.; Tschanz, J.T.; Peters, M.E.; Leoutsakos, J.M.; Matyi, J.; Lyketsos, C.G.; Nowrangi, M.; Rosenberg, P.B. Neuropsychiatric symptoms as risk factors for cognitive decline in clinically normal older adults: The cache county study. Am. J. Geriatr. Psychiatry 2020, 28, 64–71. [Google Scholar] [CrossRef]
- Huang, M.F.; Lee, W.J.; Yeh, Y.C.; Liao, Y.C.; Wang, S.J.; Yang, Y.H.; Chen, C.S.; Fuh, J.L. Genetics of neuropsychiatric symptoms in patients with Alzheimer’s disease: A 1-year follow-up study. Psychiatry Clin. Neurosci. 2020, 74, 645–651. [Google Scholar] [CrossRef]
- Almdahl, I.S.; Agartz, I.; Hugdahl, K.; Korsnes, M.S.; Alzheimer’s Disease Neuroimaging Initiative. Brain pathology and cognitive scores prior to onset of late-life depression. Int. J. Geriatr. Psychiatry 2022, 37, 1–15. [Google Scholar] [CrossRef]
- Binette, A.P.; Vachon-Presseau, É.; Morris, J.; Bateman, R.; Benzinger, T.; Collins, D.L.; Poirier, J.; Breitner, J.; Villeneuve, S. Amyloid and tau pathology associations with personality traits, neuropsychiatric symptoms, and cognitive lifestyle in the preclinical phases of sporadic and autosomal dominant Alzheimer’s disease. Biol. Psychiatry 2021, 89, 776–785. [Google Scholar] [CrossRef] [PubMed]
- Babulal, G.M.; Chen, L.; Doherty, J.M.; Murphy, S.A.; Johnson, A.M.; Roe, C.M. Longitudinal changes in anger, anxiety, and fatigue are associated with cerebrospinal fluid biomarkers of Alzheimer’s disease. J. Alzheimers Dis. 2022, 87, 141–148. [Google Scholar] [CrossRef]
- Chan, C.K.; Pettigrew, C.; Soldan, A.; Zhu, Y.; Wang, M.C.; Albert, M.; Rosenberg, P.; BIOCARD Research Team. Association between late-life neuropsychiatric symptoms and cognitive decline in relation to white matter hyperintensities and amyloid burden. J. Alzheimers Dis. 2022, 86, 1415–1426. [Google Scholar] [CrossRef]
- Clark, C.; Richiardi, J.; Maréchal, B.; Bowman, G.L.; Dayon, L.; Popp, J. Systemic and central nervous system neuroinflammatory signatures of neuropsychiatric symptoms and related cognitive decline in older people. J. Neuroinflamm. 2022, 19, 127. [Google Scholar] [CrossRef]
- Johansson, M.; Stomrud, E.; Johansson, P.M.; Svenningsson, A.; Palmqvist, S.; Janelidze, S.; van Westen, D.; Mattsson-Carlgren, N.; Hansson, O. Development of apathy, anxiety, and depression in cognitively unimpaired older adults: Effects of Alzheimer’s disease pathology and cognitive decline. Biol. Psychiatry 2022, 92, 34–43. [Google Scholar] [CrossRef] [PubMed]
- Kim, H.; Levine, A.; Cohen, D.; Gehrman, P.; Zhu, X.; Devanand, D.P. The role of amyloid, tau, and APOE genotype on the relationship between informant-reported sleep disturbance and Alzheimer’s disease risks. J. Alzheimers Dis. 2022, 87, 1567–1580. [Google Scholar] [CrossRef]
- Babulal, G.M.; Chen, L.; Murphy, S.A.; Doherty, J.M.; Johnson, A.M.; Morris, J.C. Neuropsychiatric symptoms and Alzheimer disease biomarkers independently predict progression to incident cognitive impairment. Am. J. Geriatr. Psychiatry 2023, 31, 1190–1199. [Google Scholar] [CrossRef]
- Li, K.; Zeng, Q.; Luo, X.; Qi, S.; Xu, X.; Fu, Z.; Hong, L.; Li, Z.; Fu, Y.; Chen, Y. Neuropsychiatric symptoms associated multimodal brain networks in Alzheimer’s disease. Hum. Brain Mapp. 2023, 44, 119–130. [Google Scholar] [CrossRef] [PubMed]
- Marquié, M.; García-Gutiérrez, F.; Orellana, A.; Montrreal, L.; de Rojas, I.; García-González, P.; Puerta, R.; Olive, C.; Cano, A.; Hernandez, I.; et al. The synergic effect of AT (N) profiles and depression on the risk of conversion to dementia in patients with mild cognitive impairment. Int. J. Mol. Sci. 2023, 24, 1371. [Google Scholar] [CrossRef] [PubMed]
- Pink, A.; Krell-Roesch, J.; Syrjanen, J.A.; Christenson, L.R.; Lowe, V.J.; Vemuri, P.; Fields, J.A.; Stokin, J.; Kremers, W.K.; Scharf, E.L.; et al. Interactions Between Neuropsychiatric Symptoms and Alzheimer’s Disease Neuroimaging Biomarkers in Predicting Longitudinal Cognitive Decline. Psychiatr. Res. Clin. Pract. 2023, 5, 4–15. [Google Scholar] [CrossRef]
- Burling, J.E.; Katz, Z.; Yuan, Z.; Munro, C.; Mimmack, K.; Ma, G.; Hanseeuw, B.J.; Papp, K.V.; Amariglio, R.E.; Vannini, P.; et al. Study partner report of apathy in older adults is associated with AD biomarkers: Findings from the Harvard aging brain study. Am. J. Geriatr. Psychiatry 2024, 32, 909–919. [Google Scholar] [CrossRef]
- Guan, D.X.; Rehman, T.; Nathan, S.; Durrani, R.; Potvin, O.; Duchesne, S.; Pike, G.B.; Smith, E.; Ismail, Z. Neuropsychiatric symptoms: Risk factor or disease marker? A study of structural imaging biomarkers of Alzheimer’s disease and incident cognitive decline. Hum. Brain Mapp. 2024, 45, e70016. [Google Scholar] [CrossRef]
- Ronat, L.; Hanganu, A.; Chylinski, D.; Van Egroo, M.; Narbutas, J.; Besson, G.; Muto, V.; Schmidt, C.; Bahri, M.A.; Phillips, C.; et al. Prediction of cognitive decline in healthy aging based on neuropsychiatric symptoms and PET-biomarkers of Alzheimer’s disease. J. Neurol. 2024, 271, 2067–2077. [Google Scholar] [CrossRef]
- Ronat, L.; Rönnlund, M.; Adolfsson, R.; Hanganu, A.; Pudas, S. Revised Temperament and Character Inventory factors predict neuropsychiatric symptoms and aging-related cognitive decline across 25 years. Front. Aging Neurosci. 2024, 16, 1335336. [Google Scholar] [CrossRef] [PubMed]
- Rabl, M.; Clark, C.; Dayon, L.; Bowman, G.L.; Popp, J. Blood plasma protein profiles of neuropsychiatric symptoms and related cognitive decline in older people. J. Neurochem. 2023, 164, 242–254. [Google Scholar] [CrossRef] [PubMed]
- Ismail, Z.; Leon, R.; Creese, B.; Ballard, C.; Robert, P.; Smith, E.E. Optimizing detection of Alzheimer’s disease in mild cognitive impairment: A 4-year biomarker study of mild behavioral impairment in ADNI and MEMENTO. Mol. Neurodegener. 2023, 18, 50. [Google Scholar] [CrossRef]
- Ghahremani, M.; Wang, M.; Chen, H.Y.; Zetterberg, H.; Smith, E.; Ismail, Z. Plasma phosphorylated tau at threonine 181 and neuropsychiatric symptoms in preclinical and prodromal Alzheimer disease. Neurology 2023, 100, 683–693. [Google Scholar] [CrossRef]
- Jiang, J.; Wang, A.; Shi, H.; Jiang, S.; Li, W.; Jiang, T.; Wang, L.; Zhang, X.; Sun, M.; Zhao, M.; et al. Clinical and neuroimaging association between neuropsychiatric symptoms and nutritional status across the Alzheimer’s disease continuum: A longitudinal cohort study. J. Nutr. Health Aging 2024, 28, 100182. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Ren, P.; Mapstone, M.; Conwell, Y.; Porsteinsson, A.P.; Foxe, J.J.; Rajeev, R.; Feng, L.; Alzheimer’s Disease Neuroimaging Initiative. Identify a shared neural circuit linking multiple neuropsychiatric symptoms with Alzheimer’s pathology. Brain Imaging Behav. 2019, 13, 53–64. [Google Scholar] [CrossRef]
- Banning, L.C.; Ramakers, I.H.; Rosenberg, P.B.; Lyketsos, C.G.; Leoutsakos, J.M.; Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s disease biomarkers as predictors of trajectories of depression and apathy in cognitively normal individuals, mild cognitive impairment, and Alzheimer’s disease dementia. Int. J. Geriatr. Psychiatry 2020, 36, 224–234. [Google Scholar] [CrossRef]
- Sannemann, L.; Schild, A.K.; Altenstein, S.; Bartels, C.; Brosseron, F.; Buerger, K.; Cosma, N.C.; Freiesleben, S.D.; Glanz, W.; Heneka, M.T. Neuropsychiatric symptoms in at-risk groups for AD dementia and their association with worry and AD biomarkers—Results from the DELCODE study. Alzheimers Res. Ther. 2020, 12, 131. [Google Scholar] [CrossRef]
- Cotta Ramusino, M.; Perini, G.; Vaghi, G.; Dal Fabbro, B.; Capelli, M.; Picascia, M.; Franciotta, D.; Farina, L.; Ballante, E.; Costa, A. Correlation of frontal atrophy and CSF tau levels with neuropsychiatric symptoms in patients with cognitive impairment: A memory clinic experience. Front. Aging Neurosci. 2021, 13, 595758. [Google Scholar] [CrossRef]
- de Oliveira, F.F.; Miraldo, M.C.; de Castro-Neto, E.F.; de Almeida, S.S.; Matas, S.L.; Bertolucci, P.H.F.; Naffah-Mazzacoratti, M.D.G. Associations of neuropsychiatric features with cerebrospinal fluid biomarkers of amyloidogenesis and neurodegeneration in dementia with Lewy bodies compared with Alzheimer’s disease and cognitively healthy people. J. Alzheimers Dis. 2021, 81, 1295–1309. [Google Scholar] [CrossRef]
- Jacobs, H.I.; Riphagen, J.M.; Ramakers, I.H.; Verhey, F.R. Alzheimer’s disease pathology: Pathways between central norepinephrine activity, memory, and neuropsychiatric symptoms. Mol. Psychiatry 2021, 26, 897–906. [Google Scholar] [CrossRef] [PubMed]
- Siafarikas, N.; Kirsebom, B.E.; Srivastava, D.P.; Eriksson, C.M.; Auning, E.; Hessen, E.; Salbaek, G.; Blennow, K.; Aarsland, D.; Fladby, T. Cerebrospinal fluid markers for synaptic function and Alzheimer type changes in late life depression. Sci. Rep. 2021, 11, 20375. [Google Scholar] [CrossRef]
- Dang, M.; Chen, Q.; Zhao, X.; Chen, K.; Li, X.; Zhang, J.; Zhang, Z. Tau as a biomarker of cognitive impairment and neuropsychiatric symptoms in Alzheimer’s disease. Hum. Brain Mapp. 2023, 44, 327–340. [Google Scholar] [CrossRef]
- Henjum, K.; Watne, L.O.; Godang, K.; Halaas, N.B.; Eldholm, R.S.; Blennow, K.; Zetterberg, H.; Saltvedt, I.; Bollerslev, J.; Knapskog, A.B. Cerebrospinal fluid catecholamines in Alzheimer’s disease patients with and without biological disease. Transl. Psychiatry 2022, 12, 151. [Google Scholar] [CrossRef]
- Kan, C.N.; Xu, X.; Schmetterer, L.; Venketasubramanian, N.; Chen, C.; Tan, C.H. Interactions of comorbid neuropsychiatric subsyndromes with neurodegenerative and cerebrovascular pathologies on cognition. Neurobiol. Aging 2022, 109, 239–246. [Google Scholar] [CrossRef] [PubMed]
- Krell-Roesch, J.; Zaniletti, I.; Syrjanen, J.A.; Kremers, W.K.; Algeciras-Schimnich, A.; Dage, J.L.; van Harten, A.; Fields, J.; Petersen, R.; Vassilaki, M.; et al. Plasma-derived biomarkers of Alzheimer’s disease and neuropsychiatric symptoms: A community-based study. Alzheimers Dement. 2022, 15, e12461. [Google Scholar] [CrossRef] [PubMed]
- Manca, R.; Jones, S.A.; Venneri, A. Macrostructural and microstructural white matter alterations are associated with apathy across the clinical Alzheimer’s disease spectrum. Brain Sci. 2022, 12, 1383. [Google Scholar] [CrossRef]
- Miao, R.; Chen, H.Y.; Gill, S.; Naude, J.; Smith, E.E.; Ismail, Z. Plasma β-amyloid in mild behavioural impairment–neuropsychiatric symptoms on the Alzheimer’s continuum. J. Geriatr. Psychiatry Neurol. 2022, 35, 434–441. [Google Scholar] [CrossRef]
- Waschkies, K.F.; Soch, J.; Darna, M.; Richter, A.; Altenstein, S.; Beyle, A.; Brosseron, F.; Buchholz, F.; Butryn, M.; Dobisch, L.; et al. Machine learning-based classification of Alzheimer’s disease and its at-risk states using personality traits, anxiety, and depression. Int. J. Geriatr. Psychiatry 2023, 38, e6007. [Google Scholar] [CrossRef]
- Aguzzoli, C.S.; Ferreira, P.C.; Povala, G.; Ferrari-Souza, J.P.; Bellaver, B.; Katz, C.S.; Zalzale, H.; Lussier, F.; Rohden, F.; Abbas, F.; et al. Neuropsychiatric symptoms and microglial activation in patients with Alzheimer disease. JAMA Netw. Open 2023, 6, 2345175. [Google Scholar] [CrossRef]
- De Lucia, N.; Carbone, G.; Muzii, B.; Ferrara, N.; Rengo, G.; Maldonato, N.M.; Femminella, G.D. Neuropsychiatric symptoms and their neural correlates in individuals with mild cognitive impairment. Int. Psychogeriatr. 2023, 35, 623–632. [Google Scholar] [CrossRef] [PubMed]
- Greig Custo, M.T.; Lang, M.K.; Barker, W.W.; Gonzalez, J.; Vélez-Uribe, I.; Arruda, F.; Conniff, J.; Rodriguez, M.J.; Loewenstein, D.A.; Duara, R.; et al. The association of depression and apathy with Alzheimer’s disease biomarkers in a cross-cultural sample. Appl. Neuropsychol. Adult. 2024, 31, 849–865. [Google Scholar] [CrossRef]
- Jiang, J.; Hong, Y.; Li, W.; Wang, A.; Jiang, S.; Jiang, T.; Wang, W.; Yang, S.; Ren, Q.; Zou, X. Chain Mediation Analysis of the Effects of Nutrition and Cognition on the Association of Apolipoprotein E ɛ4 with neuropsychiatric symptoms in Alzheimer’s Disease. J. Alzheimers Dis. 2023, 96, 669–681. [Google Scholar] [CrossRef]
- Kan, C.N.; Huang, X.; Zhang, L.; Hilal, S.; Reilhac, A.; Tanaka, T.; Venketasubramanian, N.; Chen, C.; Xu, X. Comorbid amyloid with cerebrovascular disease in domain-specific cognitive and neuropsychiatric disturbances: A cross-sectional memory clinic study. Neurobiol. Aging 2023, 132, 47–55. [Google Scholar] [CrossRef] [PubMed]
- Kim, H.; Zhu, X.; Zhao, Y.; Bell, S.A.; Gehrman, P.R.; Cohen, D. Resting-state functional connectivity changes in older adults with sleep disturbance and the role of amyloid burden. Mol. Psychiatry 2023, 28, 4399–4406. [Google Scholar] [CrossRef]
- Krell-Roesch, J.; Rakusa, M.; Syrjanen, J.A.; van Harten, A.C.; Lowe, V.J.; Jack, C.R., Jr. Association between CSF biomarkers of Alzheimer’s disease and neuropsychiatric symptoms: Mayo Clinic Study of Aging. Alzheimers Dement. 2023, 19, 4498–4506. [Google Scholar] [CrossRef]
- Ozaki, T.; Hashimoto, N.; Udo, N.; Narita, H.; Nakagawa, S.; Kusumi, I. Neurobiological correlation between phosphorylated tau and mood symptoms in memory clinic patients. Psychogeriatrics 2023, 23, 954–962. [Google Scholar] [CrossRef] [PubMed]
- Frank, B.; Walsh, M.; Hurley, L.; Groh, J.; Blennow, K.; Zetterberg, H.; Tripodis, Y.; Budson, A.E.; Maureen, K.O.; Martin, B. Cognition Mediates the Association Between Cerebrospinal Fluid Biomarkers of Amyloid and P-Tau and Neuropsychiatric Symptoms. J. Alzheimers Dis. 2024, 100, 1055–1073. [Google Scholar] [CrossRef]
- Falgàs, N.; Peña-González, M.; Val-Guardiola, A.; Pérez-Millan, A.; Guillén, N.; Sarto, J.; Esteller, D.; Bosch, B.; Fernández-Villullas, G.; Tort-Merino, A.; et al. Locus coeruleus integrity and neuropsychiatric symptoms in a cohort of early-and late-onset Alzheimer’s disease. Alzheimers Dement. 2024, 20, 6351–6364. [Google Scholar] [CrossRef]
- Huang, L.; Huang, Q.; Xie, F.; Guo, Q. Neuropsychiatric symptoms in Alzheimer’s continuum and their association with plasma biomarkers. J. Affect Disord. 2024, 348, 200–206. [Google Scholar] [CrossRef]
- Hsu, C.C.; Wang, S.I.; Lin, H.C.; Lin, E.S.; Yang, F.P.; Chang, C.M.; Wei, J.C.C. Difference of Cerebrospinal Fluid Biomarkers and Neuropsychiatric Symptoms Profiles among Normal Cognition, Mild Cognitive Impairment, and Dementia Patient. Int. J. Mol. Sci. 2024, 25, 3919. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Cummings, J. The neuropsychiatric inventory: Development and applications. J. Geriatr. Psychiatry Neurol. 2020, 33, 73–84. [Google Scholar] [CrossRef]
- Körner, A.; Czajkowska, Z.; Albani, C.; Drapeau, M.; Geyer, M.; Braehler, E. Efficient and valid assessment of personality traits: Population norms of a brief version of the NEO Five-Factor Inventory (NEO-FFI). Arch. Psychiatry Psychother. 2015, 17, 21–32. [Google Scholar] [CrossRef]
- Showraki, A.; Murari, G.; Ismail, Z.; Barfett, J.J.; Fornazzari, L.; Munoz, D.G.; Schweizer, T.; Fischer, C.E. Cerebrospinal fluid correlates of neuropsychiatric symptoms in patients with Alzheimer’s disease/mild cognitive impairment: A systematic review. J. Alzheimers Dis. 2019, 71, 477–501. [Google Scholar] [CrossRef] [PubMed]
- Onyango, I.G.; Jauregui, G.V.; Čarná, M.; Bennett Jr, J.P.; Stokin, G.B. Neuroinflammation in Alzheimer’s disease. Biomedicines 2021, 9, 524. [Google Scholar] [CrossRef]
First Author, Year | Participants | Research Design | Objective of Interest | Measures | Results |
---|---|---|---|---|---|
Liguori, 2018 [24] | N = 256 (251 with AD and 55 with late life depression) | Longitudinal: 2-year follow up | To evaluate the usefulness of CSF biomarkers and FDG-PET in early differentiating late life depression and AD. | -MMSE -Patient Health Questionnaire-9 Biomarkers -18-FDG-PET -CSF Aβ42, tau, p-tau | -AD patients had lower levels of Aβ42 and reduction of 18-FDG-PET uptake in temporo-parietal regions, and the cognitive deficits were correlated with depression. |
Ruthirakuhan, 2019 [25] | N = 38 with diagnosis of Major Neurocognitive Disorder due to AD | 14-week double-blind crossover trial | To examine if serum markers of oxidative stress and neuroinflammation were associated with cannabinoid nabilone in agitated patients with AD. | -MMSE -Cohen–Mansfield Agitation Inventory Biomarkers -Lipid hydroperoxides -4-hydroxinonenal -IL-1β, IL-6, IL-8, IL-10 -Tumor necrosis factor-a (TNF-a) -Interferon | -The markers 4-hydroxynonenal and TNF-a were associated with the severity of agitation. |
Banning, 2020 [26] | N = 24.493 | Longitudinal: 5 years follow up | To examine the five-year progression of depression and apathy and their relationship to AD biomarkers. | -NPI -MMSE Biomarkers CSF Aβ42, tau, p-tau | -Lower Aβ42 and higher tau were associated with increased probability of depression and apathy over time. |
Burhanullah, 2020 [27] | N = 470 (clinically normal older adults) | Longitudinal: follow up for approximately 5 years | To examine the relationship between NPS and subsequent cognitive decline in population-based sample. | -NPI -MMSE -Word list memory -Wechsler Logical Memory Subtest -Constructional praxis -Benton Visual Retention Test -Animal fluency -Symbol Digit Modalities Test | -Baseline NPI score predicted faster decline in word list memory, praxis recall, and animal fluency. -Baseline NPI-anxiety was associated with decline in Symbol Digit Modalities. |
Huang, 2020 [28] | N = 793 with AD | Longitudinal: 1-year follow up | To examine how candidate gene variants relate to NPS domains. | -NPI -CDR Biomarkers -Genes | -APOE ε4 carriers displayed increased psychotic symptoms. -CD33 and EPHA1 were associated with mood symptoms. -SORL1 was associated with frontal symptoms. |
Almdahl, 2021 [29] | N = 241 healthy older adults | Longitudinal: follow up after 4 years | To investigate potential predictors of late life depression using cognitive scores and neurodegenerative and vascular biomarkers in healthy older adults. | -NPI -MMSE -Wechsler Memory Scale, Revised Biomarkers -ΜRI -PET -CSF Aβ levels | Amyloid pathology and white matter hyperintensity can predict future development of late life depression in cognitively unimpaired individuals. |
Binette, 2021 [30] | N = 115 older adults with family history N = 117 mutation carriers | Longitudinal: assessment every year | To examine the combinations of personality traits, NPS, and cognitive lifestyle related to Aβ and tau depositions. | -International Personality Item Pool NEO -NPI Biomarkers -Amyloid PET -Tau PET | -Lower neuroticism, higher openness and extraversion, lower NPS burden, and higher education were associated with less Aβ and tau depositions. |
Babulal, 2022 [31] | N = 248 cognitively normal older adults | Longitudinal: Follow up after 3 years | To assess if baseline CSF biomarkers can predict changes in non-depressed mood states. | -The Profile of Mood States, Short Form -MMSE Biomarkers -CSF Aβ40, Aβ42, tau, and p-tau181 | Participants with higher levels of CSF biomarkers developed more anxiety, anger, and fatigue over time. |
Chan, 2022 [32] | N = 193 cognitively unimpaired older adults | Longitudinal: follow up after 3 years | To determine if amyloid burden and white matter hyperintensities modulate the association between NPS and the rate of cognitive decline. | -NPI -Geriatric Depression Scale Biomarkers -Amyloid PET | -The effect of NPS on executive dysfunction may occur through mechanisms outside of amyloid burden and white matter hyperintensity. |
Clark, 2022 [33] | N = 87 cognitively normal or with MCI | Longitudinal: Follow up after 18–36 months | To identify systematic and central nervous system inflammatory alterations associated with NPS and their relationship with AD pathology and disease progression. | -NPI -MMSE -CDR -Buschke Double Memory Test -Stroop -Trail Making Test A and B -Activities of Daily Living Biomarkers 38 neuroinflammation and vascular injury markers in serum and CSF | -NPS were associated with eotaxin-3, IL-6, and C-reactive protein (CRP) in serum and with soluble intracellular cell adhesion molecule-1 (sICAM-1), IL-8, interferon-γ-induced protein, and CRP in CSF. |
Johansson, 2022 [34] | N = 356 cognitively unimpaired older adults | Longitudinal: assessment biennially for up to 8 years | To examine how AD pathology biomarkers, white matter lesions, and cognitive deficits contribute to the development of apathy, anxiety, and depression. | -Apathy Evaluation Scale -Hospital Anxiety and Depression Scale -MMSE -Color/form task in a quick test Biomarkers -CSF Aβ42, Aβ40, Nfl, p-tau 217 -MRI | -Aβ pathology was associated with increasing levels of apathy and anxiety longitudinally. -More rapid decline of cognition was related to increasing levels of apathy. |
Kim, 2022 [35] | N = 351 (with normal cognition or MCI) | Longitudinal: followed for approximately 5 years | To investigate how sleep disturbance, in combination with Aβ, tau, and APOE ε4, contributes to brain atrophy and cognitive decline. | -NPI -Informant-reported sleep disturbance (IRSD) -MMSE -Wechsler Logical Memory Subscale -Rey Auditory Verbal Learning Test -AD Assessment Schedule-Cognition -Verbal fluency -Trail Making Test A and B -Digit Span WAIS Digit Symbol -5 Clock Drawing Test -Boston Naming Test Biomarkers -CSF Aβ42 and p-tau -APOE -MRI | -Significant interaction between IRSD and AD biomarkers in multiple brain regions. -Aβ and p-tau/Aβ predicted faster decline in IRSD. -Significant interaction between IRSD and APOE for brain atrophy rate but not for cognition. |
Babulal, 2023 [36] | N = 286 cognitively normal older adults | Longitudinal: follow up after 7 years | To investigate the effects of NPS and AD biomarkers on the progression to incident cognitive impairment among cognitively normal older adults. | -NPI -Geriatric Depression Scale -CDR Biomarkers -CSF Aβ42, Aβ40, tau, and p-tau -Amyloid PET | -Changes in NPS increase the risk of progression to cognitive impairment independently from biomarkers. |
Li, 2023 [37] | N = 337 (167 healthy controls, 34 with SCD, 118 with MCI, 18 with AD) | Longitudinal: 1 year follow-up | To examine multimodal brain patterns associated with NPS in AD continuum. | -NPI -MMSE -MoCA -ADAS-Cog -Rey Auditory Verbal Learning Test -Categorical fluency Biomarkers -Amyloid and Tau PET -MRI and fMRI | -NPS were associated with a distinct multimodal brain network involving amyloid and tau deposition, gray matter atrophy, and functional connectivity alterations. |
Marquié, 2023 [38] | N = 500 individuals with MCI | Longitudinal: 1 year follow up | To explore the predictive value of the combination of the AT(N) profile and NPS using survival analysis to determine the conversion ratio to dementia. | -NPI -MMSE Biomarkers -CSF Aβ, tau and p-tau protein -APOE | Pathological ATN groups and the presence of depression and apathy were associated with a higher risk of conversion to dementia. |
Pink, 2023 [39] | N = 1581 cognitively unimpaired | Longitudinal: follow up after 15 months | To examine interactions between NPS, FDG-PET, and PiB PET. | -NPI -Auditory Verbal Learning Test -Wechsler Memory Scale -Boston Naming Test -Category fluency -Wechsler Adult Intelligence Scale -Trail Making Test A and B Biomarkers -FDG-PET -PiB PET -APOE | The combined effect of NPS and high brain amyloid leads to a faster decline in overall and specific cognitive functions. |
Burling, 2024 [40] | N = 156 cognitively unimpaired | Longitudinal: follow up after 6–9 years | To examine the relationship between apathy and AD biomarkers in older adults. | -Apathy Evaluation Scale (Self) -Apathy Evaluation Scale (Informant) Biomarkers -PiB PET | Apathy Evaluation Scale (Informant) was significantly associated with Aβ and temporal lobe tau. |
Guan, 2024 [41] | N = 1273 (852 with no NPS, 272 with non-MBI NPS, 147 with MBI) | Longitudinal: 1-year follow up | To investigate the association of NPS with AD structural imaging biomarkers and incident cognitive decline. | -NPI Biomarkers -MRI -APOE | -NPS were linked with hippocampal atrophy. -NPS in later life were associated with AD patterns. -MBI predicted faster progression to dementia. |
Ronat, 2024 [42] | N = 101 healthy older adults | Longitudinal: 2-year follow up | To assess the relationship between longitudinal cognitive changes, depression, anxiety, and AD biomarkers. | -Beck Anxiety Inventory -Beck Depression Inventory -Free and Cued Selective Reminding -Trail Making Test B -3-back task -Stroop Biomarkers -Amyloid PET | -Association between anxiety and prefrontal amyloid burden classified episodic memory decline. -Depression and prefrontal and hippocampal tau burden was associated with decline in memory. |
Ronat, 2024 [43] | N = 1286 | Longitudinal: 25-year follow-up | To examine personality factors as predictors of neuropsychiatric, cognitive, and brain trajectories of participants from a population-based aging study. | -Temperament and Character Inventory -Patient Health Questionnaire-9 -Center for Epidemiologic Studies -Perceived Stress Questionnaire -Karolinska Sleep Questionnaire -Cognitive battery including 5 episodic memory scores, the WAIS-R block design test, and verbal fluency Biomarkers -MRI -APOE | -Closeness to experience and tendence to liabilities were associated with higher levels of depression, stress, sleep disturbance, and cognitive decline. -Closeness to experience was associated with faster right hippocampal volume reduction. |
Rabl, 2022 [44] | N = 85 (with MCI or mild demntia) | Cross-sectional and longitudinal: follow up after 18–36 months | To identify blood-based biomarkers associated with NPS using untargeted plasma proteomics. | -NPI -MMSE -CDR Biomarkers -CSF Aβ42, tau, p-tau -284 plasma proteins -APOE | -The identified 15 proteins predicted both persisting NPS and cognitive decline. |
Ismail, 2023 [45] | Ν = 510 MCI participants | Cross-sectional and longitudinal | To determine if adding MBI to biomarkers would improve the performance of biomarkers model. | -NPI -MMSE Biomarkers CSF Aβ42, tau, p-tau | -MBI was associated with lower Aβ42 and higher p-tau, tau, p-tau/Aβ42. -NPS were associated with lower Aβ42/Aβ40. |
Ghahremani, 2023 [46] | N = 571 with ΜΒΙ | Cross-sectional and longitudinal (follow up after 1 year) | To investigate the associations of MBI with ptau-181, neuropsychological test performance, and incident AD. | -NPI -MMSE -Rey Auditory Verbal Learning Test -Trail Making Test B Biomarkers -Plasma p-tau181 | -MBI was associated with higher plasma p-tau181 levels in addition to a decline in memory and executive functions. -Greater dementia incidence in MBI. |
Jiang, 2024 [47] | Ν = 432 on Ad continuum | Cross-sectional and longitudinal: follow up after 10 months | To investigate the association between NPS and nutritional status and explore their brain regions on AD continuum. | -NPI -Mini Nutritional Assessment -MMSE -Montreal Cognitive Assessment -Activities of Daily Living -Pittsburgh Sleep Quality Index Biomarkers -APOE -Arterial spin labeling | -Increased cerebral blood flow in the left putamen was associated with malnutrition, NPS, affective symptoms, and hyperactivity. -Longitudinally, higher NPI score was associated with lower scores in Mini Nutritional Assessment. |
Wang, 2019 [48] | N = 98 (70 with amnestic MCI, 28 with AD | Cross-sectional | To explore the neural circuits of NPS in AD. | -NPI -MMSE -Wechsler Logical Memory Subtest -CDR Biomarkers -CSF Aβ, p-tau -Resting state fMRI | A fronto-limbic circuit connects various NPS to AD pathology. |
Banning, 2020 [49] | (N = 650) MCI (N = 887) AD (N = 626) | Cross-sectional | To investigate the relationship between AD biomarkers and NPS. | -MMSE -NPI Biomarkers -CSF Aβ42, tau protein, p-tau -MRI | -Lower levels of Aβ42 and higher levels of tau and p-tau protein were associated with anxiety. -Lower level of Aβ42 and smaller hippocampal volume were associated with apathy. -Mediation of cognitive impairment. |
Sannermann, 2020 [50] | N = 687 (242 with SCD, 115 with MCI, 77 with AD, 209 healthy controls) | Cross-sectional | To investigate the frequency of NPS in AD subgroups and to test the association of NPS with AD biomarkers. | -NPI -Geriatric Depression Scale -Geriatric Anxiety Inventory Biomarkers -CSF Aβ42, Aβ42/Aβ40, tau, p-tau | -SCD group had less NPS compared to MCI and AD group. -In cognitively unimpaired, low Aß42 was associated with higher rates of reporting two or more NPS. |
Cotta Ramusino, 2021 [51] | N = 100 (with MCI or dementia) | Cross-sectional | To investigate the potential correlations between NPS and CSF tau protein and brain atrophy. | -NPI -MMSE -Verbal and Digit Span -Corsi Test -15-Item Memory Test -Story Recall Test -Rey Complex Figure -Raven’s Colored Matrices -Frontal Assessment Battery -Trail Making Test A and B -Stroop Test -Verbal fluency Biomarkers -MRI -CSF Aβ42 tau, p-tau | -Negative correlation between NPI score and tau levels. -Positive correlation of cortical frontal atrophy with delusions, apathy, hallucinations, agitation, and night-time disturbances. |
De Oliveira, 2021 [52] | N = 81(27 with Dementia with Lewy Bodies (DLB), 27 with AD and 27 controls) | Cross-sectional | To investigate associations of CSF biomarkers with neuropsychiatric features in DLB compared with late-onset AD. | -MMSE -NPI Biomarkers -Apolipoprotein E (APOE) -CSF Aβ42, Aβ40, Aβ38, tau, p-tau-181 -a-synuclein -ubiquitin -NfL | -In AD, associations of agitation with tau, tau/p-tau181, tau/Aβ42, and tau/α-synuclein. -Associations of delusions with p-tau181/Aβ42 and a-synuclein/Aβ42. -Associations of night-time behavior with tau, tau/p-tau181, tau/Aβ 42, and tau/a-synuclein. |
Jacobs, 2021 [53] | N = 111 (60 with subjective cognitive decline, 36 with MCI, and 19 with AD) | Cross-sectional | To investigate relationships between central norepinephrine metabolism, tau and beta-amyloid, blood–brain barrier dysfunction, NPS, and memory. | -NPI -MMSE -CDR -Categorical fluency -Letter–Digit Substitution Test -Word Learning Task Biomarkers -CSF Aβ42, p-tau181 protein, albumin and 3-methoxy-4-hydroxyphenylethyleneglycol (MHPG) -Plasma (IL)1β, IL-6, IL12p70 -CSF/plasma albumin ratio | -NPS were strongly associated with MHPG and p-tau. -Memory impairment was linked to MHPG, mediated by p-tau and inflammatory amyloidosis. |
Siafarikas, 2021 [54] | N = 145 (41 cognitively healthy, 38 late life depression, 66 predementia AD | Cross-sectional | To examine markers for synaptic function and AD pathology in late life depression. | -MMSE -Clock Drawing Test -CERAD word list test -Trail Making Test -Control Oral Speed Association Test -Visual Object and Space perception -NPI Geriatric Depression Scale Biomarkers -CSF Aβ42, Aβ40, tau, p-tau protein -Neurogranin (Ng) - Aβ precursor protein cleaving enzyme 1 (BACE 1) | -Late life depression was associated with amyloid dysmetabolism and poorer cognitive performance. -Higher Ng and BACE1 in late life depression depending on AD status. |
Dang, 2022 [55] | N = 121 (83 with AD, 38 cognitively unimpaired) | Cross-sectional | To explore the relationship between tau, Aβ, cognition, and NPS. | -NPI -MMSE Biomarkers -FDG-PET -Amyloid PET -Tau PET | -Tau pathology is superior to Aβ and glucose metabolism to identify cognitive impairment and NPS. |
Henjum, 2022 [56] | N = 407 (54 with MCI, 240 with AD, 113 cognitively unimpaired) | Cross-sectional | To investigate if CSF catecholamines relate to AD clinical presentation or neuropathology. | -NPI -MMSE -Clock Drawing Test -Trail Making Test A and B Biomarkers -CSF Aβ42, tau, and p-tau181 -CSF noradrenaline, adrenaline, dopamine, neurogranin, | -CSF catecholamine concentrations are altered in AD. -CSF noradrenaline and adrenaline concentrations were higher among AD patients, but their temporal dynamics may be non-linear. |
Kan, 2022 [57] | N= 773 (cognitively healthy, MCI, dementia) | Cross-sectional | To examine the association between NPS and the burden of neurodegeneration and cerebrovascular disease and cognition. | -Frontal Assessment Battery -Digit Span -Visual Memory Span -Boston Naming Test -Verbal fluency -Word List Recall -Story Recall -Clock Drawing Test -WAIS-R block design -Symbol Digit Modality -Digit Cancellation -Maze Test -NPI Biomarkers -Quantitive MRI | -Robust association of neurodegenerative and cerebrovascular pathologies with NPS (hyperactivity and apathy) and cognitive impairment. |
Krell-Roesch, 2022 [58] | N = 1005 (118 cognitively impaired) | Cross-sectional | To examine the associations between plasma-derived biomarkers of AD and neuropsychiatric symptoms in community-dwelling older adults. | -NPI -Short Test of Mental Status -Auditory Verbal Learning Test -Wechsler Memory Scale -Boston Naming Test -Category fluency -Trail Making Test -Wechsler Intelligence Scale -Beck Depression Inventory -Beck Anxiety Inventory Biomarkers -Plasma Aβ42/Aβ40, p-tau181, p-tau217, tau, and NfL | -p-tau181, p-tau217 and tau were associated with appetite changes. -p-tau181 and p-tau217 were associated with agitation and disinhibition. |
Manca, 2022 [59] | N = 183 (61 with apathy, 61 with no apathy, 61 cognitively unimpaired) | Cross-sectional | To investigate the relationship between white matter damage and apathy in AD. | -NPI -MMSE -Logical Memory Test -Clock Drawing Test -Auditory Verbal Learning Test -Category fluency -Trail Making Test -Boston Naming Test Biomarkers -CSF Aβ, p-tau and ratio -MRI | -Patients with apathy have more severe NPS. -They showed signs of extensive white matter damage, especially in associative tracts in the frontal lobes, fornix, and cingulum. |
Miao, 2022 [60] | Ν = 139 (86 with normal cognition and 53 with MCI) | Cross-sectional | To examine the associations between Mild Behavioral Impairment and plasma Aβ42/Aβ40. | -NPI Biomarkers -Plasma Aβ40, Aβ42 | -Lower plasma Aβ42/Aβ40 was associated with higher NPI score and greater affective dysregulation. |
Waschkies, 2022 [61] | N = 733 (189 healthy controls, 132 with amnestic MCI, and 74 with mild AD) | Cross-sectional | To evaluate the predictive value of personality traits (Big Five), anxiety and depression scores, resting-state fMRI activity of the default mode network, APOE and CSF biomarkers. | -MMSE -Geriatric Depression Scale -Geriatric Anxiety Inventory -CERAD neuropsychological battery -Big Five Inventory Biomarkers -MRI and fMRI -APOE -CSF Aβ42/40, tau, and p-tau. | CSF biomarkers, personality, depression, anxiety, and APOE show significant predictive value for classification of AD and its stages. |
Aguzzoli, 2023 [62] | N = 109 (70 cognitively normal and 39 with cognitive impairment) | Cross-sectional | To evaluate if glial markers are associated with NPS in individuals across the AD continuum. | -NPI -MMSE -CDR Biomarkers -MRI -Amyloid PET -Tau PET -Plasma GFAP | -The NPI score and irritability were associated with microglial activation in the frontal, temporal, and parietal cortices. |
De Lucia, 2023 [63] | N = 538 (233 with MCI, 305 healthy controls) | Cross-sectional | To evaluate the association between NPS, cognitive function, regional tau deposition, and brain volumes in MCI subjects. | -NPI -MMSE -ADAS-Cog -Trail Making Test A and B Biomarkers -MRI -Tau PET | NPS occur early in the AD trajectory and are mainly related to deficits of executive functions and to reduction of gray matter volume in the orbitofrontal and posterior cingulate cortex. |
Greig Custo, 2023 [64] | N = 284 (55 cognitively normal, 92 with MCI and 28 with dementia) | Cross-sectional and cross-cultural | To evaluate the relationship between depression and apathy with Aβ deposition and brain atrophy. | -NPI -Geriatric Depression Scale Biomarkers -MRI -Amyloid PET | -Reduced volume in the rostral anterior cingulate cortex significantly correlated with apathy. -Apathy corresponded with higher Aβ levels. |
Jiang, 2023 [65] | N = 310 (with MCI and AD) | Cross-sectional | To investigate the association of APOE ε4 with NPS and explore nutritional status and cognition as joint mediators. | -NPI -MMSE -Mini Nutritional Assessment Biomarkers -APOE | Chain-mediating effects of MNA and MMSE scores on the association of APOE ε4 with hallucinations, apathy, and aberrant motor activity. |
Kan, 2023 [66] | N = 216 memory clinic participants (healthy, MCI, AD) | Cross-sectional | To examine the comorbidity of amyloid and cerebrovascular pathology with cognitive impairment and NPS. | -Wechsler Memory Scale -Frontal Assessment Battery -Digit and Visual Span -Auditory Detection Test -Boston Naming Test -Verbal fluency -Clock Drawing Test -Digit Cancellation Task -Symbol Digit Modalities Task -Maze Task Biomarkers -Structural MRI -Amyloid PET | -Negative effect of Aβ on memory and apathy. -Negative effects of cerebrovascular disease on language and hyperactivity. |
Kim, 2023 [67] | N = 489 (53,6% cognitively normal, 32,5 with MCI, 13,9% with AD) | Cross-sectional | To examine the association between sleep disturbance, Aβ burden, and resting state functional connectivity in older adults. | -NPI -MMSE -Wechsler Logical Memory Subtest -CDR Biomarkers -Amyloid PET -Resting State fMRI | -Sleep disturbance was associated with salience hyperconnectivity, only with the presence of Aβ burden. |
Krell-Roesch, 2023 [68] | N = 784 (699 cognitively unimpaired and 85 with MCI) | Cross-sectional | To examine the association between CSF biomarkers and NPS in older non-demented adults. | -NPI -Beck Depression Inventory -Beck Depression Anxiety Biomarkers CSF Aβ42, tau, p-tau181 | Lower CSF Aβ42 and higher tau/Aβ42 and p-tau/Aβ42 ratios were associated with depression and anxiety, as well as with NPI-assessed anxiety, apathy, and night-time behavior. |
Ozaki, 2023 [69] | N = 122 (12 cognitively unimpaired, 46 with MCI and 64 with AD) | Cross-sectional | To analyze the relationship between p-tau protein and depression, anxiety, and apathy. | -NPI -MMSE Biomarkers -CSF p-tau protein -APOE | -Association between p-tau accumulation and decreased incidence of depression and apathy. -In APOE ε4 non-carriers: negative association between p-tau and depression. |
Frank, 2024 [70] | N = 781 older adults (218 with dementia) | Cross-sectional | To analyze cross-sectional mediation pathways between CSF biomarkers, cognitive function, and NPS. | -NPI -MMSE MoCA -Logical Memory Recall -Boston Naming Test -Semantic fluency -Trail Making Test B Biomarkers CSF Aβ42, tau, p-tau181 | Higher p-tau181/Aβ42 ratio predicted higher NPI score, which was partially mediated by the MMSE and MoCA. |
Falgas, 2024 [71] | Ν = 136 (104 with AD and 32 healthy controls) | Cross-sectional | To determine the differences in the severity of NPS and locus coeruleus. | -NPI Biomarkers -CSF Aβ42, tau, p-tau, and noradrenaline -Plasma p-tau181 -MRI | -Early onset AD was associated with higher NPI score. -Locus coeruleus integrity was negatively associated with NPS. -Noradrenaline levels increased in AD. |
Huang, 2024 [72] | N = 305 (53 normal controls, 75 with subjective cognitive decline, 74 with MCI, 103 with dementia) | Cross-sectional | To explore the prevalence of NPS and their association with plasma biomarkers throughout the Alzheimer’s continuum. | -NPI Biomarkers Plasma Aβ42, Aβ40, tau, p-tau181, NfL, APOE | -NPS can be early manifestations of preclinical AD. - Higher plasma NfL levels seem to be associated with NPS and especially psychosis. |
Hsu, 2024 [73] | Ν = 1896 (977 cognitively unimpaired, 270 with MCI and 649 with dementia) | Cross-sectional | To investigate the association of NPS with CSF biomarkers across a spectrum of cognitive states. | -NPI Biomarkers CSF Aβ42, tau, and p-tau181 protein | -The notable disparities in NPI and CSF biomarkers among normal, MCI, and AD patients underscore their diagnostic potential. |
Symptom | Key Biomarkers | Main Cognitive Domains | Imaging Findings | Personality Traits |
---|---|---|---|---|
Apathy | ↑ p-tau, ↑ p-tau/Aβ42 | Executive dysfunction | Atrophy: anterior cingulate, prefrontal cortex; white matter loss in cingulum, uncinate | ↓ extraversion, ↓ conscientiousness |
Depression | ↑ p-tau, ↑ cortisol, ↓ Aβ42 | Memory, language, global cognition | Atrophy: hippocampus, medial prefrontal cortex; ↑ tau in hippocampus | ↑ neuroticism, ↓ conscientiousness |
Anxiety | ↑ NfL, ↑ IL-6, ↑ CRP | Attention, processing speed | Hyperconnectivity in salience network; amyloid in prefrontal cortex | ↑ neuroticism |
Agitation/Frontal Symptoms | ↑ p-tau181, ↑ TNF-α, ↑ oxidative stress | Executive control | Thinning: dorsolateral prefrontal cortex, anterior cingulate; microglial activation in frontal cortex | |
Sleep Disturbance | ↑ p-tau/Aβ42, APOE ε4 x Aβ interaction | Arousal regulation, attention | Altered default mode network, ↑ tau in precuneus; frontal cortical thinning | |
Psychosis | ↑ a-syn/Aβ42, ↑ p-tau, ↑ NfL | Working memory, attention | Atrophy: prefrontal cortex, limbic structures; connectivity disruption | |
Appetite Disturbance | ↑ p-tau217, ↓ Aβ42, ↑ inflammatory markers | Executive disinhibition | Atrophy: hypothalamus, orbitofrontal cortex |
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Chatzikostopoulos, A.; Moraitou, D.; Papaliagkas, V.; Tsolaki, M. Mapping the Neuropsychiatric Symptoms in Alzheimer’s Disease Using Biomarkers, Cognitive Abilities, and Personality Traits: A Systematic Review. Diagnostics 2025, 15, 1082. https://doi.org/10.3390/diagnostics15091082
Chatzikostopoulos A, Moraitou D, Papaliagkas V, Tsolaki M. Mapping the Neuropsychiatric Symptoms in Alzheimer’s Disease Using Biomarkers, Cognitive Abilities, and Personality Traits: A Systematic Review. Diagnostics. 2025; 15(9):1082. https://doi.org/10.3390/diagnostics15091082
Chicago/Turabian StyleChatzikostopoulos, Athanasios, Despina Moraitou, Vasileios Papaliagkas, and Magda Tsolaki. 2025. "Mapping the Neuropsychiatric Symptoms in Alzheimer’s Disease Using Biomarkers, Cognitive Abilities, and Personality Traits: A Systematic Review" Diagnostics 15, no. 9: 1082. https://doi.org/10.3390/diagnostics15091082
APA StyleChatzikostopoulos, A., Moraitou, D., Papaliagkas, V., & Tsolaki, M. (2025). Mapping the Neuropsychiatric Symptoms in Alzheimer’s Disease Using Biomarkers, Cognitive Abilities, and Personality Traits: A Systematic Review. Diagnostics, 15(9), 1082. https://doi.org/10.3390/diagnostics15091082