1 Boring Old Man

^{Everybody would like to find a brain scan or a blood or spinal fluid test to predict Alzheimer’s disease. Such objective tests seem inherently more reliable than the clinical interview. Recently we’ve seen various studies identifying biomarkers for predicting who will develop Alzheimer’s. The problem is that most of the studies are merely suggestive, though they are so complex that they create a veneer of definitiveness. The usual technique is a sort of statistical fishing expedition. First, you identify a group of people who are at risk of developing Alzheimer’s. Second, you collect boatloads of data, such as from brain scans, blood draws, or spinal fluid specimens. Third, wait a couple of years to see which patients develop Alzheimer’s. Finally, have a computer sort through dozens–sometimes hundreds–of possible biomarkers until you find one, or a combination  of several, that correlate with developing Alzheimer’s.}

^{There are many variations on this theme, of course, but this is basically how biomarker research is done. The problem is that the more biomarkers you test, the higher the risk that you’ll find a correlation that isn’t a valid biomarker — but is just a random finding that has little to do with Alzheimer’s.  In order to test the utility of biomarkers, you have to choose a few likely candidates, and see if they actually predict Alzheimer’s…and you have to compare the predictive power of the expensive biomarkers with the cheap old fashioned method — the clinical interview.  Amazingly, almost no such studies have been done–and not a single study has compared multiple types of biomarkers with the clinical interview. That is, until now.}

^{An article published in the latest issue of the Archives of General Psychiatry compared specific neuroimaging findings and spinal fluid markers with a clinical interview. Over a two period, the researchers found that only three variables were able to significantly predict which patients would develop Alzheimer’s: two clinical measures of memory, and one neuromaging finding — the thickness of a part of the temporal lobe. By far the most important predictor of cognitive decline was a combination of a brief survey of activities of daily living and 5 minute paper and pencil test. These two combined explained 50% of the variance in outcomes, which is a very large number in these kinds of studies.}

^{The bottom line is that if you want a test to predict whether a patient is going to progress from mild cognitive impairment to Alzheimer’s, stay away from the needle.  A careful clinical interview, repeated over time, is overwhelmingly more effective than these expensive tests.}

Utility of Combinations of Biomarkers, Cognitive Markers, and Risk Factors to Predict Conversion From Mild Cognitive Impairment to Alzheimer Disease in Patients in the Alzheimer’s Disease Neuroimaging Initiative
^{by Jesus J. Gomar, PhD; Maria T. Bobes-Bascaran, MA; Concepcion Conejero-Goldberg, MD, PhD; Peter Davies, PhD; Terry E. Goldberg, PhD; for the Alzheimer’s Disease Neuroimaging Initiative}
Archives of General Psychiatry. 2011 68(9):961-969^.

^{Context: Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors.
Objective: To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease.
Design: Longitudinal study.
Participants: We analyzed the Alzheimer’s Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses.
Setting: The Alzheimer’s Disease Neuroimaging Initiative public database.
Outcome Measures: Primary outcome measures were odds ratios, pseudo-R-squared, and effect sizes.
Results: In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease.
Conclusions: Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic trajectory of the disease than by a sharp decline in functional ability and, to a lesser extent, by declines in executive function.}