Rate of Cognitive Decline in Alzheimer’s Disease Stratified by Age

Stanley, Karen; Whitfield, Tim; Kuchenbaecker, Karoline; Sanders, Oliver; Stevens, Tim; Walker, Zuzana

doi:10.3233/jad-181047

Cited by 26 publications

(20 citation statements)

References 31 publications

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“…According to Canevelli et al [28], slow progression of cognitive decline is the most common pattern among AD patients, with a prevalence of 52′. On the contrary, in a recent study by Stanley et al [29], the rate of cognitive decline was measured between 1.4 to 2.8 MMSE points per year. We suggest that in our group of patients the high cognitive reserve protected them from an increased rate of cognitive decline [30].…”

Section: Discussionmentioning

confidence: 99%

Donepezil Treatment for Alzheimer’s Disease in Chronic Dialysis Patients

Yiannopoulou

Anastasiou

Kyrozis

et al. 2019

Case Rep Nephrol Dial

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Donepezil is one of the cholinesterase inhibitors that are indicated for the treatment of mild to moderate Alzheimer’s disease (AD). Pharmacokinetic analysis has shown that donepezil is primarily eliminated by renal excretion rather than biliary excretion in humans. Therefore, patients with impaired renal function are at high risk of toxicity caused by accumulation of this drug. It is also well known that dialysis patients have very often cholinergic disorders. On the other hand, with the increasing number of long-term chronic dialysis patients, the prevalence of cognitive disorders is increasing in elderly dialysis patients. Because of the above-mentioned special risks of these patients, acetylcholinesterase inhibitors, such as donepezil, are avoided to be prescribed for them. We studied 5 cases of chronic hemodialysis outpatients (3 men [70, 72, and 86 years old] and 2 women [65 and 71 years old]) who were diagnosed as having moderate AD. We administered donepezil at 2.5 mg/day orally to the patients. After 1 month’s treatment, their behavioral symptoms were improved, without them having any adverse events. We enhanced the dose to 5 mg/day without the patients experiencing any episodes of drug toxicity. After 3 months of treatment with the higher dose, their cognitive and executive functions were slightly improved and their behavioral disorders were remarkably milder, without them experiencing any episodes of drug toxicity. The patients’ condition remained stable for 6 months after the initial administration of the drug. All of them were followed for the 10 following years, showing a mild cognitive decline per year for the first 5 years and more severe decline for the remaining years of the follow-up. Our cases indicate that donepezil treatment under prudent use may be well tolerated and have a beneficial impact on chronic hemodialysis patients with AD.

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Section: Discussionmentioning

confidence: 99%

Donepezil Treatment for Alzheimer’s Disease in Chronic Dialysis Patients

Yiannopoulou

Anastasiou

Kyrozis

et al. 2019

Case Rep Nephrol Dial

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“…When considered individually, these factors have been observed to result in differences in disease progression. While age is typically considered the major risk factor for developing AD, higher age at AD onset has been observed to be associated with a slower rate of cognitive decline [33,34]. Similarly, female sex has been identified as a major risk factor with almost two-thirds of AD cases being women [35].…”

Section: Discussionmentioning

confidence: 99%

Disease progression modeling in Alzheimer’s disease: insights from the shape of cognitive decline

Rakêt

2019

Preprint

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Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration will not directly reflect the disease state, but rather the effect of the cognitive decline on the patient's predisease cognitive capability. Patients with high predisease cognitive capabilities tend to score better on cognitive tests relative to patients with low predisease cognitive capabilities at the same disease stage. Thus, a single assessment with a cognitive test is not adequate for determining the stage of an AD patient. Methods and Findings: I developed a joint statistical model that explicitly modeled disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. The developed model takes the form of a nonlinear mixed-effects model. Maximum-likelihood estimation in this model induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline in AD that spans approximately 15 years from the earliest subjective cognitive deficits to severe AD dementia. It was demonstrated how direct modeling of latent factors that modify the observed data patterns provide a scaffold for understanding disease progression, biomarkers and treatment effects along the continuous time progression of disease. Conclusions: The suggested framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.

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“…When considered individually, these factors have been observed to result in differences in disease progression. While age is typically considered the major risk factor for developing AD, higher age at AD onset has been observed to be associated with a slower rate of cognitive decline (Gardner et al, 2013;Stanley et al, 2019). Similarly, female sex has been identified as a major risk factor, with almost two-thirds of AD cases being women (Alzheimer's Association., 2018).…”

Section: Age Sex Education and Cognitive Declinementioning

confidence: 99%

Statistical Disease Progression Modeling in Alzheimer Disease

Rakêt

2020

Front. Big Data

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Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration reflects not only the disease state, but also the effect of the cognitive decline on the patient's pre-disease cognitive capability. Patients with high pre-disease cognitive capabilities tend to score better on cognitive tests that are sensitive early in disease relative to patients with low pre-disease cognitive capabilities at a similar disease stage. Thus, a single assessment with a cognitive test is often not adequate for determining the stage of an AD patient. Repeated evaluation of patients' cognition over time may improve the ability to stage AD patients, and such longitudinal assessments in combinations with biomarker assessments can help elucidate the time dynamics of biomarkers. In turn, this can potentially lead to identification of markers that are predictive of disease stage and future cognitive decline, possibly before any cognitive deficit is measurable. Methods and Findings: This article presents a class of statistical disease progression models and applies them to longitudinal cognitive scores. These non-linear mixed-effects disease progression models explicitly model disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline that spans ∼15 years from the earliest subjective cognitive deficits to severe AD dementia. Subsequent analyses demonstrated how direct modeling of latent factors that modify the observed data patterns provides a scaffold for understanding disease progression, biomarkers, and treatment effects along the continuous time progression of disease. Conclusions: The presented framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.

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Rate of Cognitive Decline in Alzheimer’s Disease Stratified by Age

Cited by 26 publications

References 31 publications

Donepezil Treatment for Alzheimer’s Disease in Chronic Dialysis Patients

Donepezil Treatment for Alzheimer’s Disease in Chronic Dialysis Patients

Disease progression modeling in Alzheimer’s disease: insights from the shape of cognitive decline

Statistical Disease Progression Modeling in Alzheimer Disease

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