Measurement of progression in Alzheimer's disease: a clinician's perspective

Relationship between Performance on the Mini-Mental State Examination Sub-Items and Activities of Daily Living in Patients with Alzheimer's Disease

Mini-mental state examination (MMSE) subitems provide useful information about the cognitive status of patients with Alzheimer's disease (AD). If the relationship between MMSE subitems and activities of daily living (ADL) can be shown, the performance of sub-items can predict ADL status and may provide useful information for early ADL intervention. Therefore, the purpose of this study was to investigate the relationship between MMSE subitem scores and ADL. The study sample consisted of 718 patients with AD. Logistic regression analysis using the Physical Self-maintenance Scale (PSMS) and Lawton's Instrumental ADL (L-IADL) was performed with each of the subitems as the dependent variables and the MMSE subitem as the independent variable. As a result, the subitems of MMSE, which are strongly related to each item in PSMS differed (e.g., toilet: registration odds ratio 3.00, grooming: naming 3.66). In the case of L-IADL, most items were strongly associated with "writing" (e.g., shopping: odds ratio 4.29, laundry 3.83). In clinical practice, we often focus only on the total MMSE score in patients with AD. However, the relationship between each MMSE subitem and ADL suggested in this study may be useful information that can be linked to ADL care from the performance of the MMSE subitem.

Analysis of conversion of Alzheimer’s disease using a multi-state Markov model

With rapid aging of world population, Alzheimer’s disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer’s disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer’s disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer’s Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer’s disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849.

Describing the Sequence of Cognitive Decline in Alzheimer's Disease Patients: Results from an Observational Study

Background: Improved understanding of the pattern of cognitive decline in Alzheimer’s disease (AD) would be useful to assist primary care physicians in explaining AD progression to patients and caregivers.

Objective: To identify the sequence in which cognitive abilities decline in community-dwelling patients with AD.

Methods: Baseline data were analyzed from 1,495 patients diagnosed with probable AD and a Mini-Mental State Examination (MMSE) score ≤ 26 enrolled in the 18-month observational GERAS study. Proportional odds logistic regression models were applied to model MMSE subscores (orientation, registration, attention and concentration, recall, language, and drawing) and the corresponding subscores of the cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-cog), using MMSE total score as the index of disease progression. Probabilities of impairment start and full impairment were estimated at each MMSE total score level.

Results: From the estimated probabilities for each MMSE subscore as a function of the MMSE total score, the first aspect of cognition to start being impaired was recall, followed by orientation in time, attention and concentration, orientation in place, language, drawing, and registration. For full impairment in subscores, the sequence was recall, drawing, attention and concentration, orientation in time, orientation in place, registration, and language. The sequence of cognitive decline for the corresponding ADAS-cog subscores was remarkably consistent with this pattern.

Conclusion: The sequence of cognitive decline in AD can be visualized in an animation using probability estimates for key aspects of cognition. This might be useful for clinicians to set expectations on disease progression for patients and caregivers.

Demographic and clinical characteristics related to cognitive decline in Alzheimer disease in China: A multicenter survey from 2011 to 2014

Alzheimer disease (AD) is the most frequent cause of dementia. AD diagnosis, progression, and treatment have not been analyzed nationwide in China. The primary aim of this study was to analyze demographic and clinical characteristics related to cognitive decline in AD patients treated at outpatient clinics in China.We performed a retrospective study of 1993 AD patients at 10 cognitive centers across 8 cities in China from March 2011 to October 2014. Of these, 891 patients were followed for more than 1 year.The mean age at diagnosis was 72.0 ± 10.0 years (range 38-96 years), and the mean age at onset of AD was 69.8 ± 9.5 years. Most patients (65.1%) had moderate to severe symptoms at the time of diagnosis, and mean Mini-Mental State Examination at diagnosis was 15.7 ± 7.7. AD patients showed significant cognitive decline at 12 months after diagnosis. Having more than 9 years of formal education was an independent risk factor related to rapid cognitive decline [odds ratio (OR) = 1.80; 95% confidence interval (95% CI): 1.11-2.91]. Early-onset AD patients experienced more rapid cognitive decline than late-onset patients (OR = 1.83; 95% CI: 1.09-3.06).Most AD patients in China had moderate to severe symptoms at the time of diagnosis and experienced significant cognitive decline within 1 year. Rapid cognitive decline in AD was related to having a higher educational level and younger age of onset.

Biochemical and Radiological Markers of Alzheimer's Disease Progression

Alzheimer's disease (AD) is a neurodegenerative, inevitably progressive disease with a rate of cognitive, functional, and behavioral decline that varies highly from patient to patient. Although several clinical predictors of AD progression have been identified, to our mind in clinical practice there is a lack of a reliable biomarker that enables one to stratify the risk of deterioration. Identification of biomarkers that allow the monitoring of AD progression could change the way physicians and caregivers make treatment decisions. This review summarizes the results of studies on potential biochemical and radiological markers related to AD progression.

Low-dose total-body carbon-ion irradiations induce early transcriptional alteration without late Alzheimer's disease-like pathogenesis and memory impairment in mice

The cause and risk factors of Alzheimer's disease (AD) are largely unknown. Studies on possible radiation-induced AD-like pathogenesis and behavioral consequences are important because humans are exposed to ionizing radiation (IR) from various sources. It was reported that total-body irradiations (TBI) at 10 cGy of low linear energy transfer (LET) X-rays to mice triggered acute transcriptional alterations in genes associated with cognitive dysfunctions. However, it was unknown whether low doses of IR could induce AD-like changes late after exposure. We reported previously that 10 cGy X-rays induced early transcriptional response of several AD-related genes in hippocampi without late AD-like pathogenesis and memory impairment in mice. Here, further studies on two low doses (5 or 10 cGy) of high LET carbonion irradiations are reported. On expression of 84 AD-related genes in hippocampi, at 4 hr after TBI, 5 cGy induced a significant upregulation of three genes (Abca1, Casp3, and Chat) and 10 cGy led to a marked upregulation of one gene (Chat) and a downregulation of three genes (Apoe, Ctsd, and Il1α), and, at 1 year after TBI, one gene (Il1α) was significantly downregulated in 10 cGy-irradiated animals. Changes in spatial learning ability and memory and induction of AD-like pathogenesis were not detected by in vivo brain imaging for amyloid-β peptide accumulation and by immunohistochemical staining of amyloid precursor protein, amyloid-β protein, tau, and phosphorylated tau protein. These findings indicate that low doses of carbon-ion irradiations did not cause behavioral impairment or AD-like pathological change in mice.

Language impairment in Alzheimer's disease and benefits of acetylcholinesterase inhibitors

Alzheimer’s disease is characterized by progressively worsening deficits in several cognitive domains, including language. Language impairment in Alzheimer’s disease primarily occurs because of decline in semantic and pragmatic levels of language processing. Given the centrality of language to cognitive function, a number of language-specific scales have been developed to assess language deficits throughout progression of the disease and to evaluate the effects of pharmacotherapy on language function. Trials of acetylcholinesterase inhibitors, used for the treatment of clinical symptoms of Alzheimer’s disease, have generally focused on overall cognitive effects. However, in the current report, we review data indicating specific beneficial effects of acetylcholinesterase inhibitors on language abilities in patients with Alzheimer’s disease, with a particular focus on outcomes among patients in the moderate and severe disease stages, during which communication is at risk and preservation is particularly important.

Global clinical dementia rating of 0.5 in MCI masks variability related to level of function

OBJECTIVE

To evaluate whether ratings on Clinical Dementia Rating (CDR) items related to instrumental activities of daily living (IADL) are associated with cognitive or brain morphometric characteristics of participants with mild cognitive impairment (MCI) and global CDR scores of 0.5.

METHODS

Baseline cognitive and morphometric data were analyzed for 283 individuals with MCI who were divided into 2 groups (impaired and intact) based on their scores on the 3 CDR categories assessing IADL. Rates of progression to Alzheimer disease (AD) over 2 years were also compared in the 2 groups.

RESULTS

The impaired IADL MCI group showed a more widespread pattern of gray matter loss involving frontal and parietal regions, worse episodic memory and executive functions, and a higher percentage of individuals progressing to AD than the relatively intact IADL MCI group.

CONCLUSIONS

The results demonstrate the importance of considering functional information captured by the CDR when evaluating individuals with MCI, even though it is not given equal weight in the assignment of the global CDR score. Worse impairment on IADL items was associated with greater involvement of brain regions beyond the mesial temporal lobe. The conventional practice of relying on the global CDR score as currently computed underutilizes valuable IADL information available in the scale, and may delay identification of an important subset of individuals with MCI who are at higher risk of clinical decline.

Rate of decline in Alzheimer disease measured by a Dementia Severity Rating Scale

OBJECTIVE

To measure clinically relevant change in Alzheimer disease (AD) using a family member-completed Dementia Severity Rating Scale (DSRS) questionnaire.

BACKGROUND

Measuring rate of change provides important clinical information. Most neuropsychologic scores change nonlinearly, complicating their use as a predictor of change throughout the illness.

METHODS

DSRS and Mini Mental State scores were prospectively collected on 702 patients with AD from first evaluation until they became too impaired to return to clinic.

RESULTS

DSRS score increased an average of 4.48 points/y [95% confidence interval (CI): 4.14-4.82] throughout the entire range of severity. In contrast, the Mini Mental State declined an average of 2.15 points/y (95% CI: 1.85-2.46) during the first 2 years, accelerated to 3.83 points/y (95% CI: 3.28-4.38) during the subsequent 3 years, and then slowed to an annual decline of 1.63 points during the last 2 years (95% CI: 0.21-3.05). A younger age of symptom onset was associated with an increased rate of DSRS change (P=0.03).

CONCLUSIONS

The DSRS provides a clinical measure of functional impairment in AD that increases about 4.48 points/y from the earliest symptomatic stage until patients become too severely impaired to return to clinic.

Neural networks for longitudinal studies in Alzheimer's disease

OBJECTIVE

Alzheimer's disease affects a growing population of elderly people today. The predictions about the course of the disease is a key component of health care decision making for patients with Alzheimer's. The physician's prognosis and predicted trajectory of cognitive decline often form the basis of treatment and health care decisions taken by patients and their families. These predictions are difficult to make because of the high variability and non-linearity exhibited by individual patterns of cognitive decline. This paper presents a new method of predicting the course of a disease using longitudinal data collected through multiple clinic visits. Longitudinal databases are similar to temporal databases, with some important differences--data is collected at irregular time intervals that are patient specific and also a varying number of observations are made for each patient, depending upon the number of times the patient visited the clinic. We propose a new type of neural network called the mixed effects neural network (MENN) model that can incorporate this type of longitudinal information.

MATERIAL AND METHODS

We have used longitudinal data on 704 subjects enrolled at the Layton aging and research center (LAARC) at Oregon Health and Science University. A back-propagation algorithm, modified for longitudinal data is used to obtain the weight parameters of the MENN. The modified back-propagation algorithm is further embedded in an iterative procedure that estimates the noise variance and the parameters that capture the longitudinal (temporal) correlation structure.

RESULTS

We have compared the performance of the MENN with linear mixed effects models and standard neural networks (NN). MENN show better performance (misclassification rate = 0.13 and relative MSE = 0.35) as compared to standard NN (misclassification rate = 0.34 and relative MSE = 2.74) and linear mixed effects models (misclassification rate = 0.14 and relative MSE = 0.4).

CONCLUSION

The results show that this method can be a useful tool for predicting non-linear disease trajectories and uncovering significant prognostic factors in longitudinal databases.

Progress in clinical neurosciences: cognitive markers of progression in Alzheimer's disease

The objective of this review is to summarize the literature on Alzheimer's disease progression utilizing cognitive batteries to track change over time. Studies published in English and obtained through PubMed searches (1983-2004) were included (i) if they had a longitudinal design and followed probable Alzheimer's patients diagnosed by National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's Disease and Related Disorders Association or Diagnostic and Statistical Manual III/IV criteria, and (ii) if the techniques used for serial assessment were well-established in terms of validity and reliability. Longitudinal studies examining Alzheimer's disease progression report highly variable annual rates of change in decline rate. It remains unclear if this reflects disease subgroups or stage-related rate of decline. In conclusion a combination of stage-appropriate cognitive tests such as the Mattis Dementia Rating Scale and the Severe Impairment Battery, along with appropriate statistical methods to account for individual variability in decline rates, can capture the progression of Alzheimer disease and may be useful in further investigation.