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Fleisher AS, Chen K, Quiroz YT, Jakimovich LJ, Gutierrez Gomez M, Langois CM, Langbaum JB, Roontiva A, Thiyyagura P, Lee W, Ayutyanont N, Lopez L, Moreno S, Muñoz C, Tirado V, Acosta-Baena N, Fagan AM, Giraldo M, Garcia G, Huentelman MJ, Tariot PN, Lopera F, Reiman EM. Associations between biomarkers and age in the presenilin 1 E280A autosomal dominant Alzheimer disease kindred: a cross-sectional study. JAMA Neurol. 2015 Mar;72(3):316-24. PubMed.
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Cyceron
This is paper represents a huge effort that provides a valuable and strong contribution to the field. The most impressive characteristic of this study is that the cohort of 32 mutation carriers (including 20 asymptomatic and 12 cognitively impaired individuals) has a single genetic variant and the same ethnicity, race, and general geographic location with similar cultural influences. This probably reduces the variability considerably compared, for instance, with the Dominantly Inherited Alzheimer's Network (DIAN) study, where the 88 mutation carriers had a combined total of 51 different mutation pedigrees. It would be interesting to get direct comparisons between both cohorts (in terms of variability but also as to the results per se), as it is difficult to evaluate these differences just by comparing this paper with those from DIAN.
In any case, there is no doubt as to the relevance of this study. And yet I do not completely share the authors' interpretation. Indeed, instead of the similarities with the DIAN study (see Bateman et al., 2012), I am rather surprised by the contrasts. In the Alzheimer's Prevention Initiative study, Fleisher et al. found that hippocampal atrophy differs between carriers and non-carriers six years before the kindred’s median age at MCI onset, whereas DIAN found it occurred approximately 15 years before expected symptom onset. Also, the plasma Aβ1-42 levels were consistently elevated from 15 years prior to symptom onset in mutation carriers in DIAN, whereas in the API study there was no significant separation between carrier and non-carrier levels, and in fact the values tend to overlap more as people age (see Fig. 1 in Fleisher et al.).
This divergence might partly be due to methodological differences, notably in the way the biomarkers are measured, since this is known to have a considerable impact on the findings (see e.g. Frisoni et al., 2013). Also, as highlighted by the authors, the sensitivity in the measures might differ among biomarkers, which would influence the sequence in which they test positive. The optimization of the methods might differ across biomarkers as well, depending on the quality of the acquired images (especially for MRI).
Of course, biomarker differences might also reflect heterogeneity of the genetic variants. This raises the broader issue of the generalization of the findings, and notably relates to the question that always gets raised about autosomal dominant AD studies: How relevant are results from mutation carriers to those expected in sporadic AD? This question is especially relevant when attempting to characterize and compare the age at initial change across biomarkers, because mutations alter the age at onset compared with the sporadic form, and they modulate, specifically, Aβ-related changes.
Apart from the comparison to DIAN, I also have a different take on the sequence of preclinical AD pathologies: In the discussion, the authors claim that the results are consistent with the biomarker timeline proposed by Jack, namely “CSF and PET measures of Aβ pathology are followed by CSF measures of tau pathology and regional CMRgl decline, followed by hippocampal atrophy and clinical progression.” However, it seems to me that, maybe surprisingly, the two PET markers (FDG and Florbetapir), as well as CSF tau, emerge at the same time, if one looks at the mean age the markers test positive (figure below). There is even no difference from CSF Aβ42 when looking at the 95 percent confidence interval. One would definitively need to use statistical comparisons (in the age of onset across biomarkers) to further understand how age at onset of these biomarkers differs significantly from each other.
Illustration of the age at initial change (vertical bar) with the 95 percent confidence interval (data taken from Table 2 in Fleisher et al.).
References:
Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, Marcus DS, Cairns NJ, Xie X, Blazey TM, Holtzman DM, Santacruz A, Buckles V, Oliver A, Moulder K, Aisen PS, Ghetti B, Klunk WE, McDade E, Martins RN, Masters CL, Mayeux R, Ringman JM, Rossor MN, Schofield PR, Sperling RA, Salloway S, Morris JC. Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N Engl J Med. 2012 Aug 30;367(9):795-804. PubMed.
Frisoni GB, Bocchetta M, Chételat G, Rabinovici GD, de Leon MJ, Kaye J, Reiman EM, Scheltens P, Barkhof F, Black SE, Brooks DJ, Carrillo MC, Fox NC, Herholz K, Nordberg A, Jack CR, Jagust WJ, Johnson KA, Rowe CC, Sperling RA, Thies W, Wahlund LO, Weiner MW, Pasqualetti P, Decarli C, . Imaging markers for Alzheimer disease: Which vs how. Neurology. 2013 Jul 30;81(5):487-500. PubMed.
Eli Lilly
Banner Alzheimer's Institute
Arizona Alzheimer's Consortium
We thank Dr. Chetelat for her kind remarks and thoughtful input. While there are some differences between the DIAN and API data, we continue to be impressed by the consistency of our findings, especially given the relatively small sample sizes, large confidence intervals, and differences in mutations, image analysis techniques employed, and the ways in which biomarker trajectories and ages at clinical onset were characterized.
Regarding ages at clinical onset, DIAN estimated each mutation carrier’s age at clinical onset based on the estimated age of symptom onset in his or her affected parent, whereas API estimated the PSEN1 E280A mutation carriers’ age at clinical onset based on the median age at which carriers in the kindred met clinical criteria for MCI. There are also different ways to measure the onset of progressive biomarker changes, and they impact estimated ages of progressive biomarker onset. Indeed, our article discusses three complementary methods for calculating the onset of progressive biomarker changes.
For estimated ages at the onset of hippocampal shrinkage, initial slope declines began to deviate approximately 15 years prior to clinical onset in DIAN and six years prior to clinical onset in API. On the other hand, hippocampal volumes began to significantly differ between mutation carriers and non-carriers roughly three years prior to clinical onset in DIAN and two years prior to clinical onset in API. Given the small sample sizes, large confidence intervals, and similarities in the age-related hippocampal changes in DIAN and API, we would be hesitant to suggest that the cohorts differ significantly in their hippocampal volume trajectories.
We previously reported that 18- to 26-year-old PSEN1 E280A mutation carriers had elevated (not reduced) CSF Aβ42 levels more than two decades before the kindred’s estimated median age at clinical onset, consistent with Aβ42 overproduction in this form of ADAD (Reiman et al., 2012). While we did not have a sufficient number of very young adult subjects in the present study to confirm that finding, we continue to believe that CSF Aβ42 levels are initially elevated in autosomal dominant AD prior to beginning the sequestration of Aβ42 in amyloid plaques.
Regarding plasma Aβ42 levels, DIAN and API findings each suggest that these levels are elevated throughout the preclinical and clinical stages of autosomal dominant AD, and neither study demonstrated significant associations with age. While it is conceivable that significant direct or inverse associations with age might be detected in larger samples, we currently postulate that the levels are unchanged throughout the natural history of autosomal dominant AD.
As we discussed in our article and as Dr. Chetelat astutely observes, we could not statistically distinguish ages of onset between the different biomarkers (with the exception of CSF Aβ42 and hippocampal volumes), due at least in part to our relatively small sample sizes and large confidence intervals. Nonetheless, we believe that our age associations are consistent with the hypothetical curves by Cliff Jack (Jack et al., 2013).
Future studies promise to refine the onset and trajectories of different biomarker, cognitive, and clinical changes in autosomal dominant AD, which typically overproduce Aβ, and determine the extent to which those findings are generalizability to late-onset AD, a disease with reduced Aβ clearance.
References:
Reiman EM, Quiroz YT, Fleisher AS, Chen K, Velez-Pardo C, Jimenez-Del-Rio M, Fagan AM, Shah AR, Alvarez S, Arbelaez A, Giraldo M, Acosta-Baena N, Sperling RA, Dickerson B, Stern CE, Tirado V, Munoz C, Reiman RA, Huentelman MJ, Alexander GE, Langbaum JB, Kosik KS, Tariot PN, Lopera F. Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer's disease in the presenilin 1 E280A kindred: a case-control study. Lancet Neurol. 2012 Dec;11(12):1048-56. PubMed.
Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, Shaw LM, Vemuri P, Wiste HJ, Weigand SD, Lesnick TG, Pankratz VS, Donohue MC, Trojanowski JQ. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013 Feb;12(2):207-16. PubMed.
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