Mild behavioral impairment is associated with β‐amyloid but not tau or neurodegeneration in cognitively intact elderly individuals

Lussier, Firoza Z; Pascoal, Tharick A.; Chamoun, Mira; Therriault, Joseph; Tissot, Cécile; Savard, Mélissa; Kang, Min Su; Mathotaarachchi, Sulantha; Parsons, Marlee; Qureshi, Muhammad Naveed Iqbal; Thomas, Émilie; Shin, Monica; Dion, Laurie-Anne; Massarweh, Gassan; Soucy, Jean‐Paul; Tsai, I‐Huang; Vitali, Paolo; Ismail, Zahinoor; Rosa‐Neto, Pedro; Gauthier, Serge

doi:10.1002/alz.12007

Cited by 129 publications

(182 citation statements)

References 41 publications

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“…However, validation studies of MBI using the full criteria (including the proper 6-month reference range, and using the appropriate MBI case ascertainment tool-the MBI checklist [13]) gave lower estimates of prevalence, 5.8% in subjective cognitive decline [35] and 14.2% in MCI [36], which are probably more specific for persistent changes related to neurodegeneration. This smaller and higher risk group offers an opportunity for more targeted and efficient use of imaging and biomarker studies to capture preclinical disease [37]. Thus, the MBI requirement for later life emergent and sustained NPS may better reflect true NPS that result from neurodegeneration and proteinopathies associated with dementia, removing symptoms that may be a result of non-dementia etiologies [38].…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Gill

Mouchès

et al. 2020

JAD

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Background: Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used for this purpose. Objectives: To investigate if baseline mild behavioral impairment (MBI) status used for NPS quantification along with brain morphology features are predictive of follow-up diagnosis, median 40 months later in patients with normal cognition (NC) or MCI. Method: Baseline neuroimaging, neuropsychiatric, and clinical data from 102 individuals with NC and 239 with MCI were extracted from the Alzheimer's Disease Neuroimaging Initiative database. Neuropsychiatric inventory questionnaire items were transformed to MBI domains using a published algorithm. Diagnosis at latest follow-up was used as the outcome variable and ground truth classification. A logistic model tree classifier combined with information gain feature selection was trained to predict follow-up diagnosis. Results: In the binary classification (NC versus MCI/AD), the optimal ML model required only two features from over 200, MBI total score and left hippocampal volume. These features correctly classified participants as remaining normal or developing cognitive impairment with 84.4% accuracy (area under the receiver operating characteristics curve [ROC-AUC] = 0.86). Seven features were selected for the three-class model (NC versus MCI versus dementia) achieving an accuracy of 58.8% (ROC-AUC=0.73).

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Section: Discussionmentioning

confidence: 99%

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Gill

Mouchès

et al. 2020

JAD

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“…Elderly people with coexisting MBI and MCI have a higher risk of developing dementia than those with MCI alone [38] [39]. Furthermore, biomarker studies have suggested that patients with MBI share similar genetic pro les, brain betaamyloid imaging, and plasma neuro lament light with those with AD [40][41][42]. In addition to conversion to AD, our ndings highlight the clinical signi cance of NPS in MCI in terms of mortality and support the MBI concept, which allows for early identi cation and facilitates new possibilities for therapeutic intervention.…”

Section: Discussionmentioning

confidence: 99%

Neuropsychiatric Symptoms and Mortality Among Patients With Mild Cognitive Impairment and Dementia Due to Alzheimer’s Disease

Huang¹,

Lee²,

Yeh³

et al. 2020

Preprint

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BackgroundNeuropsychiatric symptoms (NPS) could increase mortality risk in people with dementia due to Alzheimer’s disease (AD). However, whether NPS affects mortality risk in people with mild cognitive impairment (MCI) and whether any specific syndrome of NPS influences this risk are still unclear.MethodsIn total, 984 participants with dementia due to AD, 338 with MCI, and 365 controls were enrolled. Over a mean of 5-year follow-up, cause of death data were obtained from the Ministry of Health and Welfare in Taiwan. NPS were assessed using Neuropsychiatric Inventory Questionnaire (NPI-Q), and psychosis, mood, and frontal domain scores were determined. Survival analyses were conducted to determine the hazard ratio (HR) of death.ResultsIn controlled analyses, HR of death for AD was 2.19 (95% confidence interval [CI] = 1.29–3.71) compared with the control group, whereas no statistical significance was noted for the MCI group. A high NPI-Q score (above the median score) increased mortality risk for both the MCI and AD groups, with HRs of 2.32 (95% CI = 1.07–5.03) and 2.60 (95% CI = 1.51–4.47), respectively. Among NPI-Q domain scores, only high mood domain, but not psychosis or frontal domain, scores increased death risk for both the MCI (HR = 2.89, 95% CI = 1.00–8.51) and AD (HR = 2.59, 95% CI = 1.47–4.55) groups.ConclusionsMortality risk is high for patients with AD. Not only for AD, patients with MCI presenting with NPS, particularly mood symptoms, have high death risk.

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“…Secondly, while we examined the associations of NPS with metabolic decline in a cohort of DIAD mutation carriers who are destined to develop AD, we did not speci cally study the associations between NPS with AD biomarkers such as beta-amyloid and tau. Given the emerging evidence showing that NPS are associated with AD pathophysiology in preclinical and MCI individuals [4,55], future longitudinal studies should further determine this relationship. Thirdly, there may be potential hazards of interpreting statistical constructs as theoretical constructs in the factor analytic literature [56].…”

Section: Discussionmentioning

confidence: 99%

Neuropsychiatric symptoms are early indicators of an upcoming metabolic decline in Alzheimer’s disease

Pascoal

Mathotaarachchi

et al. 2020

Preprint

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Background Neuropsychiatric symptoms (NPS) are increasingly recognized as early non-cognitive manifestations in the Alzheimer’s disease (AD) continuum. However, the role of NPS as an early marker of pathophysiological progression in AD remains unclear. Dominantly inherited AD (DIAD) mutation carriers are young individuals who are destined to develop AD in future due to the full penetrance of the genetic mutation. Hence, the study of DIAD mutation carriers enables the evaluation of the associations between pure AD pathophysiology and metabolic correlates of NPS without the confounding effects of co-existing pathologies. In this longitudinal study, we aim to identify regional brain metabolic dysfunctions associated with NPS in cognitively intact DIAD mutation carriers. Methods We stratified 221 cognitively intact participants from the Dominantly Inherited Alzheimer’s Network according to their mutation carrier status. Regression mixed effect models with family-level random effects evaluated the interactions of NPS measured by the Neuropsychiatric Inventory-Questionnaire (NPI-Q), age and estimated years of onset (EYO) as a function of metabolism measured by [ 18 F]flurodeoxyglucose ([ 18 F]FDG) positron emission tomography in DIAD mutation and non-mutation carriers. An exploratory factor analysis was performed to identify the neuropsychiatric subsyndromes in DIAD mutation carriers using the NPI-Q sub-components. Regression mixed effect models then evaluated the interactions of specific neuropsychiatric subsyndromes with EYO on metabolism.Results 119 mutation and 102 non-mutation carriers were studied. The interaction of higher NPI-Q and shorter EYO was associated with more rapid global and regional [ 18 F]FDG uptake decline in the posterior cingulate and ventromedial prefrontal cortices, bilateral parietal lobes and right insula in DIAD mutation carriers. The neuropsychiatric subsyndrome of agitation, disinhibition, irritability and depression interacted with EYO to drive [ 18 F]FDG uptake decline in the DIAD mutation carriers. The interaction of NPI and EYO was not associated with [ 18 F]FDG uptake in DIAD non-mutation carriers. Conclusions NPS in cognitively intact DIAD mutation carriers may be clinical indicators of subsequent metabolic decline in brain networks vulnerable to AD, which support the emerging conceptual framework that NPS represent early manifestations of neuronal injury in AD. Further studies using different methodological approaches to identify NPS in preclinical AD are needed to validate our findings.

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Mild behavioral impairment is associated with β‐amyloid but not tau or neurodegeneration in cognitively intact elderly individuals

Cited by 129 publications

References 41 publications

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data

Neuropsychiatric Symptoms and Mortality Among Patients With Mild Cognitive Impairment and Dementia Due to Alzheimer’s Disease

Neuropsychiatric symptoms are early indicators of an upcoming metabolic decline in Alzheimer’s disease

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