Richard E, Schmand BA, Eikelenboom P, Van Gool WA, .
MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study.
BMJ Open. 2013;3(6)
PubMed.
This study by Richard et al. provides important findings on the diagnostic benefit of combining neuropsychological memory assessment with biomarkers for predicting AD dementia in patients with amnestic MCI.
The authors conclude that biomarkers are not useful to improve predictive accuracy once memory test performance has been accounted for. Since memory tests are less expensive than biomarker assessment, neuropsychological tests might be preferred in clinical practice.
The study has several strengths and supports the notion that the costs of biomarkers need to be weighed against their real benefit. It should be noted, however, that the current paper focused on a few measures, and none of the prediction models (biomarkers or memory tests) reached clinically relevant levels of accuracy (remaining below 80 percent). Thus, more sophisticated prediction models need to be developed, and biomarkers may still contribute to any improvement in the models.
Other points to consider are the predictive value with respect to differentiating AD from other types of dementia, which was not assessed in the current study. Furthermore, it remains to be tested whether biomarkers are beneficial at the very early stage of AD, when no or only very slight cognitive impairment is present.
The findings of this study are not that surprising—baseline cognition is the biggest predictor of cognitive progression in MCI. The RAVLT is the most sensitive test at the MCI stage simply because verbal recall is the key criterion used to define MCI patients for entry.
The Achilles' heel of most AD biomarkers is that their additive predictive value above and beyond a memory test is still not optimal. This is where we need to improve our tests. That said, pathology biomarkers have a high negative predictive value. MCI can be due to 20 different types of brain pathology, all yielding memory test scores in the same range; a negative brain scan can help clinicians rule out the likelihood of AD. In addition, memory tests are less useful at predicting future disease in people who have perfectly normal memory, whereas biomarkers can identify silent pathology. For example, in a recent multicenter study, amyloid PET significantly predicted an 18-month cognitive decline in normal subjects, above and beyond their baseline memory test score (see Doraiswamy et al., 2012).
The overall message of the paper is a good one—that we need to develop biomarkers with significant additive value above and beyond simple memory tests.
References:
Doraiswamy PM, Sperling RA, Coleman RE, Johnson KA, Reiman EM, Davis MD, Grundman M, Sabbagh MN, Sadowsky CH, Fleisher AS, Carpenter A, Clark CM, Joshi AD, Mintun MA, Skovronsky DM, Pontecorvo MJ, .
Amyloid-β assessed by florbetapir F 18 PET and 18-month cognitive decline: a multicenter study.
Neurology. 2012 Oct 16;79(16):1636-44.
PubMed.
Comments
Ludwig Maximilian University
This study by Richard et al. provides important findings on the diagnostic benefit of combining neuropsychological memory assessment with biomarkers for predicting AD dementia in patients with amnestic MCI.
The authors conclude that biomarkers are not useful to improve predictive accuracy once memory test performance has been accounted for. Since memory tests are less expensive than biomarker assessment, neuropsychological tests might be preferred in clinical practice.
The study has several strengths and supports the notion that the costs of biomarkers need to be weighed against their real benefit. It should be noted, however, that the current paper focused on a few measures, and none of the prediction models (biomarkers or memory tests) reached clinically relevant levels of accuracy (remaining below 80 percent). Thus, more sophisticated prediction models need to be developed, and biomarkers may still contribute to any improvement in the models.
Other points to consider are the predictive value with respect to differentiating AD from other types of dementia, which was not assessed in the current study. Furthermore, it remains to be tested whether biomarkers are beneficial at the very early stage of AD, when no or only very slight cognitive impairment is present.
View all comments by Michael EwersThe findings of this study are not that surprising—baseline cognition is the biggest predictor of cognitive progression in MCI. The RAVLT is the most sensitive test at the MCI stage simply because verbal recall is the key criterion used to define MCI patients for entry.
The Achilles' heel of most AD biomarkers is that their additive predictive value above and beyond a memory test is still not optimal. This is where we need to improve our tests. That said, pathology biomarkers have a high negative predictive value. MCI can be due to 20 different types of brain pathology, all yielding memory test scores in the same range; a negative brain scan can help clinicians rule out the likelihood of AD. In addition, memory tests are less useful at predicting future disease in people who have perfectly normal memory, whereas biomarkers can identify silent pathology. For example, in a recent multicenter study, amyloid PET significantly predicted an 18-month cognitive decline in normal subjects, above and beyond their baseline memory test score (see Doraiswamy et al., 2012).
The overall message of the paper is a good one—that we need to develop biomarkers with significant additive value above and beyond simple memory tests.
References:
Doraiswamy PM, Sperling RA, Coleman RE, Johnson KA, Reiman EM, Davis MD, Grundman M, Sabbagh MN, Sadowsky CH, Fleisher AS, Carpenter A, Clark CM, Joshi AD, Mintun MA, Skovronsky DM, Pontecorvo MJ, . Amyloid-β assessed by florbetapir F 18 PET and 18-month cognitive decline: a multicenter study. Neurology. 2012 Oct 16;79(16):1636-44. PubMed.
View all comments by P. Murali DoraiswamyMake a Comment
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