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Biol. Psychiatry, Feb 06 2025, S0006-3223(25)00072-1 | https://doi.org/10.1016/j.biopsych.2025.01.026
Empirical classification of neuropsychiatric symptoms and association of classes with diagnostic progression and cognitive decline in MCI and AD populations
Jong-Il Park 1,2,3,4, Seonjoo Lee 4,5,6, Benjamin Huber 6, Davangere P. Devanand 3,4,7, Hyun Kim 3,4, Terry E. Goldberg 3,4,8 on behalf of the Alzheimer’s Disease Neuroimaging Initiative 9
1Department of Psychiatry, Jeonbuk National University Medical School, Jeonju, Korea
2Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
3Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
4Division of Geriatric Psychiatry, New York State Psychiatric Institute, New York, NY, USA
5Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
6Division of Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
7Department of Neurology, Taub Institute for Research in Alzheimer's Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
8Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
9Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf
Corresponding: Terry E. Goldberg, PhD, Professor
1Department of Psychiatry, Jeonbuk National University Medical School, Jeonju, Korea
2Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
3Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
4Division of Geriatric Psychiatry, New York State Psychiatric Institute, New York, NY, USA
5Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
6Division of Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
7Department of Neurology, Taub Institute for Research in Alzheimer's Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
8Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
9Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf
Corresponding: Terry E. Goldberg, PhD, Professor
Abstract
Background: To identify classes of cognitively impaired older individuals based on their neuropsychiatric symptoms(NPS) and to investigate the contribution of NPS class to cognitive decline and Alzheimer's disease(AD) risk in mild cognitive impairment(MCI).
Methods: Our study included 1,472 participants(age range 55-91) from the Alzheimer's Disease Neuroimaging Initiative(ADNI) who were diagnosed with MCI or mild AD and had a complete neuropsychiatric Inventory at their baseline visit. We employed latent class analysis to categorize groups by NPS patterns. Linear mixed models of repeated measures(LMMRMs) were used to compare changes in cognitive performance across 5years as a function of NPS class. Subsequently, the Cox proportional hazards model was employed in individuals with MCI to assess whether rate of conversion to AD differed across the NPS groups.
Results: We identified three latent classes of NPS: No NPS (n=799, 51.7%), Apathy/Affective (n=572, 39.8%), Complex (n=108, 8.5%) NPS. In longitudinal analyses we observed interactions between class and time, indicating accelerated cognitive decline in memory and executive function in the Apathy/Affective class. In MCI, hazard ratios for conversion to AD were 1.39(95% CI: 1.10-1.76) for the Apathy/Affective class and 2.03(95% CI: 1.33-3.10) for the Complex class compared to the No NPS group after adjusting for age, sex, education, global cognition, and ApoE4 positivity.
Conclusions: Among cognitively impaired elderly, empirically derived clusters of NPS profiles were associated with cognitive decline and risk of conversion from MCI to AD. Such NPS classes may reflect specific neurobiological mechanisms within or related to AD-related neurodegeneration. Further studies with biological markers are needed to clarify these neurobiological mechanisms.
Methods: Our study included 1,472 participants(age range 55-91) from the Alzheimer's Disease Neuroimaging Initiative(ADNI) who were diagnosed with MCI or mild AD and had a complete neuropsychiatric Inventory at their baseline visit. We employed latent class analysis to categorize groups by NPS patterns. Linear mixed models of repeated measures(LMMRMs) were used to compare changes in cognitive performance across 5years as a function of NPS class. Subsequently, the Cox proportional hazards model was employed in individuals with MCI to assess whether rate of conversion to AD differed across the NPS groups.
Results: We identified three latent classes of NPS: No NPS (n=799, 51.7%), Apathy/Affective (n=572, 39.8%), Complex (n=108, 8.5%) NPS. In longitudinal analyses we observed interactions between class and time, indicating accelerated cognitive decline in memory and executive function in the Apathy/Affective class. In MCI, hazard ratios for conversion to AD were 1.39(95% CI: 1.10-1.76) for the Apathy/Affective class and 2.03(95% CI: 1.33-3.10) for the Complex class compared to the No NPS group after adjusting for age, sex, education, global cognition, and ApoE4 positivity.
Conclusions: Among cognitively impaired elderly, empirically derived clusters of NPS profiles were associated with cognitive decline and risk of conversion from MCI to AD. Such NPS classes may reflect specific neurobiological mechanisms within or related to AD-related neurodegeneration. Further studies with biological markers are needed to clarify these neurobiological mechanisms.
논문정보
- Research article
- 2025년 02월 (BRIC 등록일 2025-02-11)
- 국내+국외 연구진
- 의약학 > 의약학 기타
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