. Interpreting Biomarker Results in Individual Patients With Mild Cognitive Impairment in the Alzheimer's Biomarkers in Daily Practice (ABIDE) Project. JAMA Neurol. 2017 Dec 1;74(12):1481-1491. PubMed.


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  1. This is a well-designed study to evaluate the risk of AD dementia and dementia in general in MCI patients, using biomarkers. The cohort of MCI patients was recruited from memory clinics and followed up to three years. Time-dependent analyses reveal the multimodal biomarker model for the prognosis of MCI, which may be utilized in clinical settings.

    A strength of the study is the cross-validation procedure conducted in the same cohort and the external validation in the ADNI cohort. Even though there are differences between the two cohorts, with the current cohort being memory-clinic based, and ADNI designed as a clinical trial-like cohort, the models were still quite robust in the ADNI cohort. 

    During the mean follow-up period of 2.4 years, 51.8 percent of the MCI patients remained stable. Longer follow-up of these MCI patients in this stable cohort will likely yield more converters, which may influence the models. Also, it would be interesting to study the effects of APOE e4 status on the models. Although APOE genotyping is not routinely used in the clinic, it is a well-known risk factor for AD dementia and other dementias, and not hard to obtain. 

    View all comments by Kejal Kantarci
  2. For over two decades, biomarker technology has been used in Alzheimer’s research. The ABIDE study is the first major project to explore the adoption of biomarker technology in clinical care of patients with mild cognitive impairment, and is therefore a major step forward for the field. Although the methodology has been validated in a separate, well-characterized cohort of subjects, the major hurdles going forward will be first, determining whether the predictive model generalizes from larger care settings to small practices, where the patient populations are heterogeneous, and second, determining whether the assays can be sufficiently standardized to make the model useful in such settings.

    View all comments by Jeffrey Petrella
  3. In my clinical practice I always go over the MRI scan with my patients, showing them the atrophy in the hippocampus, parietal lobes, or other regions. “Does that mean I have Alzheimer’s disease?” they ask. I then go on to explain to them that if you take 100 people with Alzheimer’s and 100 people aging normally, there will be more atrophy in the group with Alzheimer’s than the group aging normally, but that you cannot predict anything for sure in an individual patient. Now, I’ll be able to give patients and families a different answer. Van Maurik and colleagues have done a study to try to use atrophy on MRI scans, and CSF Aβ and tau, to predict the likelihood that a given patient with mild cognitive impairment (MCI) will develop dementia due to Alzheimer’s or another disorder, one and three years in the future.

    On the one hand, the article is trying to be helpful for front-line clinicians. But on the other hand, the main paper describes brain volumes calculated with programs that are not standardly available. The supplemental tables do list the results calculated with visual estimates, which is what clinicians would use. Additionally, “validated semiquantitative visual rating scales” are referenced but otherwise not described, so anyone who wants to use these scales with tables or calculators would need to go and pull the papers themselves.

    One thing that seems quite odd, at least to this clinician, is the thought that someone would subject patients to a lumbar puncture for CSF and only do an MMSE as their neuropsychological testing. Yet that appears to be the only cognitive testing that the patients undergo (or is, at least, included in the model). One can hope that if patients underwent at least a modest amount of additional cognitive testing the prediction from the clinical data alone would be vastly improved. Given this fact, I do wonder how much the biomarkers would improve diagnosis if additional cognitive testing was put into the model.

    The authors created a calculator they say will be given freely upon request for academic use. I have received the calculator from the corresponding author but have not yet tried it, so I cannot comment on its usefulness or ease of use. Overall, the paper does represent a step forward in translating purely academic research on the relationship between atrophy, Aβ, tau, MCI, and the development of dementia into a clinical realm.

    View all comments by Andrew E. Budson
  4. This is an interesting study by van Maurik and colleagues that proposes an algorithm for calculating an individual's risk of progressing from MCI to an AD diagnosis in one or three years. The models highlighted a negative predictive value for MRI and CSF markers, reiterating their use to screen out individuals from clinical trials and intervention studies. Interestingly, gender appeared to have little impact on the risk of progression to AD. Whilst this represents an interesting method for ascertaining the risk of progression to AD, caution must be taken: The models are based on a relatively small number of individuals (575), with a relatively short follow-up (mean of 2.4 years).

    View all comments by Samantha Burnham

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  1. Can Biomarker Data Predict Individual Risk for Alzheimer’s?