How does tau PET fit in with more-established biomarkers of Alzheimer’s disease, such as the concentration of tau and phosopho-tau in the cerebrospinal fluid? For amyloid PET, the correlation with CSF Aβ was made early on (Fagan et al., 2006). It was subsequently confirmed to the point where amyloid PET and CSF Aβ are now seen as broadly equivalent for certain uses, for example, to screen prospective participants in a trial of an anti-amyloid drug. For tau PET, the first such data was presented at the 9th Human Amyloid Imaging conference, held January 14 to 16 in Miami Beach, Florida.
It came from the work of Jasmeer Chhatwal and colleagues at Harvard Medical School. Working with Keith Johnson and others, Chhatwal compared T807/AV1451 with CSF data in 31 older cognitively normal participants in the Harvard Aging Brain Study who had had a lumbar puncture and a tau PET scan within two years of each other. Comparing the CSF tau concentration to the T807/AV1451 signal in a host of different brain regions, the scientists spotted a statistically significant correlation in six regions. Intriguingly, these regions fall along the Braak staging route by which tau pathology is broadly suspected of spreading from its early site in the medial temporal lobe (MTL). They are the entorhinal and parahippocampal regions, and the inferior temporal, middle temporal, and superior temporal cortices.
Chhatwal and colleagues also related the tau PET signal to the participant’s CSF Aβ42 level. To do this with some spatial resolution, they drew up a matrix correlating the T807/AV1451 signal in each of eight regions along Braak’s proposed path of tangle spread to CSF tau, CSF phospho-tau, and CSF Aβ42. In this table a signal became apparent whereby the relationship between T807/AV1451 PET and CSF tau got weaker as it moved outward from the MTL and toward more distant regions of neocortex. In contrast, tau’s relationship to CSF Aβ42 persisted. This may be because the cognitively normal volunteers in this first sample had little tau pathology in those distant regions, but they did have amyloid there as part of the more widespread cortical deposition that is typical for amyloid pathology, Johnson said.
“If you believe the central tenet of the Braak staging system, that tau pathology spreads from the MTL to the neocortex, then you can interpret this data to mean that people with neocortical tau spread include individuals with high levels of brain Aβ deposition,” said Johnson, who presented the data on Chhatwal’s behalf. “As you move out along this path, you see a growing relationship with CSF Aβ and diminishing relationship with tau. “
This talk generated intense discussion. Clifford Jack of the Mayo clinic in Rochester, Minnesota, called it a “beautiful story.” Bill Jagust of the University of California, Berkeley, expressed hope that if these relationships show up even in cognitively normal people who have a relatively low tau signal, then scientists can hope to make robust and meaningful measurements of this sort in clinically affected people whose tau PET signal tends to be much higher.
In a separate talk, Hitoshi Shimada of the National Institute of Radiological Sciences in Chiba, Japan, presented binding data on his group’s tracer, PBB3. In this study, the PET signal did not correlate with CSF tau. This discrepancy could be due to differences in sample size, measurement techniques, or tracer characteristics, researchers said.
Does Tau PET Change Over Time?
As was the case with amyloid imaging, longitudinal observation will yield more powerful answers. With tau PET, repeat scanning is only just beginning, and at HAI, two groups presented their first case results. Mark Mintun of Avid Radiopharmaceuticals in Philadelphia is collaborating with Johnson and others to attempt to measure how the tau T807/AV1451 signal might evolve. Does it change slowly, as amyloid appears to do, making natural history and drug response tricky to quantify? Or does it grow fast, offering a wider range for researchers developing therapies to work with? To answer this question, the scientists first tried to get a sense of how reproducible T807/AV1451 measurements themselves are. In an initial sample of 21 older control, MCI, or AD cases, the difference between two scans taken within a month’s time was around 4 to 5 percent, Mintun told the audience. The intraclass correlation coefficient (ICC), used to express test-retest consistency, was above 0.93, Mintun said.
Mintun then showed longitudinal data from seven volunteers with MCI and one with AD. They had had two T807/AV1451 scans an average 14 months apart, along with two MMSE tests and a florbetapir amyloid scan. This is an exploratory study, Mintun noted. Two people had negative T807/AV1451 scans both times. One aced the MMSE both times, one did not; however, both were amyloid-negative. The remaining six people had a high amyloid burden. Their T807/AV1451 uptake grew between the first and second scans. By and large, their tau signal intensified and the distribution pattern expanded slightly. Five of the six performed lower on the MMSE the second time.
Mintun estimated a whopping 10 percent increase in uptake per year, though he emphasized that this preliminary measure will change as more data come in. “On the whole, it lines up. The tau SUVR went up as the MMSE went down in those folks who were amyloid-positive,” he told the audience.
Presenting the first data of his longitudinal T807/AV1451 work in non-AD dementias, Brad Dickerson of Massachusetts General Hospital showed two scans taken one year apart in a patient with a disease called nonfluent variant primary progressive aphasia (PPA). Dickerson not only saw the tau signal increasing but, tantalizingly, it looked as if tau pathology might be spreading through the language networks which functional connectivity MRI has implicated in this disease. For more news on tau PET in Alzheimer’s variants and frontotemporal dementia, see Part 4 of this series.—Gabrielle Strobel
- Fagan AM, Mintun MA, Mach RH, Lee SY, Dence CS, Shah AR, Larossa GN, Spinner ML, Klunk WE, Mathis CA, Dekosky ST, Morris JC, Holtzman DM. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol. 2006 Mar;59(3):512-9. PubMed.
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