Sid E. O'Bryant

Detecting Dementia With the Mini-Mental State Examination in Highly Educated Individuals

OBJECTIVE To evaluate the utility of Mini-Mental State Examination (MMSE) scores in detecting cognitive dysfunction in a sample of highly educated individuals. DESIGN Archival data were reviewed on 4248 participants enrolled in the Mayo Clinic Alzheimer Disease Research Center and Alzheimer Disease Patient Registry. PATIENTS A total of 1141 primarily white (93%) individuals with 16 or more years of self-reported education were identified. These included 307 (164 men and 143 women) patients with dementia (any type), 176 (106 men and 70 women) patients with mild cognitive impairment, and 658 (242 men and 416 women) control participants without dementia. SETTING Mayo Clinic Alzheimer Disease Research Center and Alzheimer Disease Patient Registry cohort. MAIN OUTCOME MEASURES Diagnostic accuracy estimates (sensitivity, specificity, and positive and negative predictive power) of MMSE cut scores in detecting cognitive dysfunction. RESULTS In this sample of highly educated, largely white older adults, the standard MMSE cut score of 24 (23 or below) yielded a sensitivity of 0.66, a specificity of 0.99, and an overall correct classification rate of 89% in detecting dementia. A cut score of up to 27 (26 or below) resulted in an optimal balance of sensitivity and specificity (0.89 and 0.91, respectively) with an overall correct classification rate of 90%. In a cognitively impaired group (dementia and mild cognitive impairment), a cut score of 27 (sensitivity, 0.69; specificity, 0.91) or 28 (sensitivity and specificity, 0.78) might be more appropriate. CONCLUSION Older patients with a college education who present with complaints of cognitive decline (reported by themselves or others) and score less than 27 on the MMSE are at a greater risk of being diagnosed with dementia and should be referred for a comprehensive dementia evaluation, including formal neuropsychological testing.

Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria

During the past decade, a conceptual shift occurred in the field of Alzheimers disease (AD) considering the disease as a continuum. Thanks to evolving biomarker research and substantial discoveries, it is now possible to identify the disease even at the preclinical stage before the occurrence of the first clinical symptoms. This preclinical stage of AD has become a major research focus as the field postulates that early intervention may offer the best chance of therapeutic success. To date, very little evidence is established on this “silent” stage of the disease. A clarification is needed about the definitions and lexicon, the limits, the natural history, the markers of progression, and the ethical consequence of detecting the disease at this asymptomatic stage. This article is aimed at addressing all the different issues by providing for each of them an updated review of the literature and evidence, with practical recommendations.

Utility of the RBANS in detecting cognitive impairment associated with Alzheimer's disease : Sensitivity, specificity, and positive and negative predictive powers

Although initially developed as a brief dementia battery, the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has not yet demonstrated its sensitivity, specificity, and positive and negative predictive powers in detecting cognitive impairment in patients with Alzheimers disease (AD). Therefore, the current study examined the clinical utility of the RBANS by comparing two age-, education-, and gender-matched groups: patients with AD (n=69) and comparators (n=69). Significant differences (p<0.001) were observed on the RBANS Total score, all 5 Indexes, and all 12 subtests, with patients performing worse than the comparison participants. An optimal balance between sensitivity and specificity on RBANS scores was obtained when cutoffs of one and one and a half standard deviations below the mean of the comparison sample were implemented. Areas under the Receiver Operating Characteristic curves for all RBANS Indexes were impressive though Immediate and Delayed Memory Indexes were excellent (0.96 and 0.98, respectively). Results suggest that RBANS scores yield excellent estimates of diagnostic accuracy and that the RBANS is a useful screening tool in detection of cognitive deficits associated with AD.

A Blood-Based Screening Tool for Alzheimer's Disease That Spans Serum and Plasma: Findings from TARC and ADNI

Context There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimers disease (AD) at the population level. Objective To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma. Design, Setting, Participants Analysis of serum biomarker proteins were conducted on 197 Alzheimers disease (AD) participants and 199 control participants from the Texas Alzheimers Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data. Major Outcome Measures Alzheimers disease. Results 11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49–14.47), the likelihood ratio of not having AD based on the algorithm (LR−) = 3.55 (SE = 1.15; 2.22–5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86–69.47). Conclusions It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.

Validation of the new interpretive guidelines for the clinical dementia rating scale sum of boxes score in the national Alzheimer's coordinating center database.

BACKGROUND It was recently demonstrated that the Clinical Dementia Rating scale Sum of Boxes (CDR-SB) score can be used to accurately stage severity of Alzheimer dementia and mild cognitive impairment (MCI). However, to our knowledge, the utility of those interpretive guidelines has not been cross-validated or applied to a heterogeneous sample of dementia cases. OBJECTIVE To cross-validate the staging guidelines proposed in a previous study using the National Alzheimers Coordinating Center (NACC) database. DESIGN The previously published cut scores were applied to the NACC sample and diagnostic accuracy estimates obtained. Next, analyses were restricted to NACC participants with a CDR global score (CDR-GS) of 0.5 and receiver operating characteristic curves generated to determine optimal CDR-SB cut scores for distinguishing MCI from very early dementia. SETTING The 2008 NACC uniform data set. PARTICIPANTS There were 12 462 participants (5115 controls; 2551 patients with MCI; 4796 patients with dementia, all etiologies) in the NACC data set used for the current analysis. Main Outcome Measure Accurate prediction of diagnoses (MCI or dementia) using the CDR-SB score. RESULTS The previously proposed CDR-SB ranges successfully classified the vast majority of patients across all impairment ranges with a kappa of 0.91 and 94% overall correct classification rate. Additionally, the CDR-SB score discriminated between patients diagnosed with MCI and dementia when CDR-GS was restricted to 0.5 (overall area under the curve = 0.83). CONCLUSIONS These findings cross-validate the previously published CDR-SB interpretative guidelines for staging dementia severity and extend those findings to a large heterogeneous sample of patients with dementia. Additionally, the CDR-SB scores distinguished MCI from dementia in patients with reasonable accuracy when CDR-GS was restricted to 0.5.

Innovative diagnostic tools for early detection of Alzheimer's disease

Current state‐of‐the‐art diagnostic measures of Alzheimers disease (AD) are invasive (cerebrospinal fluid analysis), expensive (neuroimaging) and time‐consuming (neuropsychological assessment) and thus have limited accessibility as frontline screening and diagnostic tools for AD. Thus, there is an increasing need for additional noninvasive and/or cost‐effective tools, allowing identification of subjects in the preclinical or early clinical stages of AD who could be suitable for further cognitive evaluation and dementia diagnostics. Implementation of such tests may facilitate early and potentially more effective therapeutic and preventative strategies for AD. Before applying them in clinical practice, these tools should be examined in ongoing large clinical trials. This review will summarize and highlight the most promising screening tools including neuropsychometric, clinical, blood, and neurophysiological tests.

Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer's disease research

The lack of readily available biomarkers is a significant hindrance toward progressing to effective therapeutic and preventative strategies for Alzheimers disease (AD). Blood‐based biomarkers have potential to overcome access and cost barriers and greatly facilitate advanced neuroimaging and cerebrospinal fluid biomarker approaches. Despite the fact that preanalytical processing is the largest source of variability in laboratory testing, there are no currently available standardized preanalytical guidelines. The current international working group provides the initial starting point for such guidelines for standardized operating procedures (SOPs). It is anticipated that these guidelines will be updated as additional research findings become available. The statement provides (1) a synopsis of selected preanalytical methods utilized in many international AD cohort studies, (2) initial draft guidelines/SOPs for preanalytical methods, and (3) a list of required methodological information and protocols to be made available for publications in the field to foster cross‐validation across cohorts and laboratories.

Developing novel blood-based biomarkers for Alzheimer's disease

Alzheimers disease is the public health crisis of the 21st century. There is a clear need for a widely available, inexpensive and reliable method to diagnosis Alzheimers disease in the earliest stages, track disease progression, and accelerate clinical development of new therapeutics. One avenue of research being explored is blood based biomarkers. In April 2012, the Alzheimers Association and the Alzheimers Drug Discovery Foundation convened top scientists from around the world to discuss the state of blood based biomarker development. This manuscript summarizes the meeting and the resultant discussion, including potential next steps to move this area of research forward.

A blood-based algorithm for the detection of Alzheimer's disease.

Background: We previously created a serum-based algorithm that yielded excellent diagnostic accuracy in Alzheimer’s disease. The current project was designed to refine that algorithm by reducing the number of serum proteins and by including clinical labs. The link between the biomarker risk score and neuropsychological performance was also examined. Methods: Serum-protein multiplex biomarker data from 197 patients diagnosed with Alzheimer’s disease and 203 cognitively normal controls from the Texas Alzheimer’s Research Consortium were analyzed. The 30 markers identified as the most important from our initial analyses and clinical labs were utilized to create the algorithm. Results: The 30-protein risk score yielded a sensitivity, specificity, and AUC of 0.88, 0.82, and 0.91, respectively. When combined with demographic data and clinical labs, the algorithm yielded a sensitivity, specificity, and AUC of 0.89, 0.85, and 0.94, respectively. In linear regression models, the biomarker risk score was most strongly related to neuropsychological tests of language and memory. Conclusions: Our previously published diagnostic algorithm can be restricted to only 30 serum proteins and still retain excellent diagnostic accuracy. Additionally, the revised biomarker risk score is significantly related to neuropsychological test performance.

Estimating the Predictive Value of the Test of Memory Malingering: An Illustrative Example for Clinicians

Recent years have witnessed an explosion in the amount of research literature dedicated to the identification of symptom exaggeration and/or malingering in neuropsychological assessments. Additionally, there is now a growing literature devoted to estimating the base rates of symptom exaggeration/malingering in a range of populations and settings. However, very little literature has been devoted to estimating the positive predictive value (PPV) or negative predictive value (NPV) of these assessment devices and/or strategies. The current project was conducted to provide an illustrative example of how to use the research literature to calculate both PPV and NPV in everyday clinical practice. When the Word Memory Test (WMT) was used as the “gold standard” to which the Test of Memory Malingering (TOMM) was compared, the TOMM achieved very high PPV (.98) and acceptable NPV (.78). How to incorporate the strategy used into clinical practice is discussed.