Figure 1. Prototypical linear mixed-effects models based on different baseline shapes and longitudinal change. The dashed line in each panel characterizes the mean value of the biomarker at baseline as a function of disease severity while the solid lines characterize the mean within-subject rate of change. The dashed lines show either no baseline effect (flat line) (A, B, and C), a linear baseline effect (D, E, and F), or a nonlinear baseline effect, which in this case is reaching an asymptote or saturation point (G, H, and I). The solid lines show either no within-subject changes with increasing disease severity (flat lines) (A, D, and G), constant within-subject changes over time (parallel increasing lines) (B, E, and H), or nonconstant within-subject changes, which in this case are greater early in the disease and less when the disease becomes more severe (C, F, and I).
Figure 2. Cerebrospinal fluid (CSF)/magnetic resonance imaging (MRI) cohort. Individual trajectory plots by Mini-Mental State Examination (MMSE) score for hippocampal volume, CSF Aβ42 level, and CSF total tau (t-tau) level are plotted in the left column. Because of the large number of subjects, the left column illustrates a random subset of the CSF/MRI cohort. Plots in the middle and right columns are based on modeling the entire cohort. Cognitively normal (CN) participants are represented with red squares; participants with mild cognitive impairment (MCI), with blue circles; and participants with Alzheimer disease (AD), with green triangles. Arrows indicate trajectories that extend beyond the plotting region. The center and right columns are model summary plots in the CSF/MRI cohort for APOE ϵ4 noncarriers (center) and APOE ϵ4 carriers (right). The dashed lines represent the baseline relationship with biomarker and MMSE score estimated from the linear mixed-effects models. The solid lines represent the within-subject rate of change in biomarker with a decrease in MMSE score of 3 points. Light orange (APOE ϵ4 noncarriers) and light blue (APOE ϵ4 carriers) lines represent the effects for a subject with a baseline age of 75 years and dark orange (APOE ϵ4 noncarriers) and dark blue (APOE ϵ4 carriers) lines represent the effects for a subject with a baseline age of 85 years. P values are shown for all effects in the model, with a P value <.10 being significant. BL indicates baseline biomarker and MMSE score effects. These may be nonzero, nonlinear, or interact with baseline age. WSR indicates within-subject rates of change in biomarker with worsening MMSE score. These may be nonzero, interact with baseline age, or be nonconstant such that the rate of change differs by baseline MMSE score. * P values reported when the change in age and the change in MMSE score are zero.
Figure 3. Positron emission tomography/magnetic resonance imaging cohort. Individual trajectory plots by Mini-Mental State Examination (MMSE) score for hippocampal volume, Pittsburgh Compound B (PiB) ratio, and fluorodeoxyglucose (FDG) ratio in a random subset of the positron emission tomography/magnetic resonance imaging cohort. Model summary plots by MMSE score for hippocampal volume, PiB ratio, and FDG ratio by APOE ϵ4 genotype. The organization of Figure 3 is analogous to that of Figure 2. AD indicates Alzheimer disease; BL, baseline biomarker and MMSE score effects; CN, cognitively normal; MCI, mild cognitive impairment; and WSR, within-subject rates of change in biomarker with worsening MMSE score.
Figure 4. Amyloid-positive cohorts. Individual trajectory plots by Mini-Mental State Examination (MMSE) score of hippocampal volume and cerebrospinal fluid (CSF) tau level for the amyloid-positive CSF/magnetic resonance imaging (MRI) cohort and of hippocampal volume and fluorodeoxyglucose (FDG) ratio for the amyloid-positive positron emission tomography (PET)/MRI cohort. The organization of Figure 4 is analogous to that of Figure 2 and Figure 3. AD indicates Alzheimer disease; BL, baseline biomarker and MMSE score effects; CN, cognitively normal; MCI, mild cognitive impairment; t-tau, total tau; and WSR, within-subject rates of change in biomarker with worsening MMSE score. * P values reported when the change in age and the change in MMSE score are zero.
Customize your JAMA Network experience by selecting one or more topics from the list below.
Jack CR, Vemuri P, Wiste HJ, et al. Shapes of the Trajectories of 5 Major Biomarkers of Alzheimer Disease. Arch Neurol. 2012;69(7):856–867. doi:https://doi.org/10.1001/archneurol.2011.3405
Author Affiliations: Departments of Radiology (Drs Jack, Vemuri, Lowe, Kantarci, Bernstein, and Gunter and Mr Senjem) and Neurology (Drs Boeve, Petersen, and Knopman) and Division of Biomedical Statistics and Informatics (Ms Wiste and Messrs Weigand and Lesnick), Mayo Clinic and Foundation, Rochester, Minnesota; Department of Pathology and Laboratory Medicine and Institute on Aging, University of Pennsylvania School of Medicine, Philadelphia (Drs Trojanowski and Shaw); and Department of Neurosciences, University of California, San Diego, La Jolla (Dr Aisen), and Veterans Affairs and University of California, San Francisco (Dr Weiner).
Group Information: A complete listing of Alzheimer's Disease Neuroimaging Initiative investigators can be found at http://adni.loni.ucla.edu /wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf.
Objective To characterize the shape of the trajectories of Alzheimer disease biomarkers as a function of Mini-Mental State Examination (MMSE) score.
Design and Setting Longitudinal registries from the Mayo Clinic and the Alzheimer's Disease Neuroimaging Initiative.
Patients Two different samples (n = 343 and n = 598) were created that spanned the cognitive spectrum from normal to Alzheimer disease dementia. Subgroup analyses were performed in members of both cohorts (n = 243 and n = 328) who were amyloid positive at baseline.
Main Outcome Measures The shape of biomarker trajectories as a function of MMSE score, adjusted for age, was modeled and described as baseline (cross-sectional) and within-subject longitudinal effects. Biomarkers evaluated were cerebrospinal fluid (CSF) Aβ42 and tau levels, amyloid and fluorodeoxyglucose positron emission tomography imaging, and structural magnetic resonance imaging.
Results Baseline biomarker values generally worsened (ie, nonzero slope) with lower baseline MMSE score. Baseline hippocampal volume, amyloid positron emission tomography, and fluorodeoxyglucose positron emission tomography values plateaued (ie, nonlinear slope) with lower MMSE score in 1 or more analyses. Longitudinally, within-subject rates of biomarker change were associated with worsening MMSE score. Nonconstant within-subject rates (deceleration) of biomarker change were found in only 1 model.
Conclusions Biomarker trajectory shapes by MMSE score were complex and were affected by interactions with age and APOE status. Nonlinearity was found in several baseline effects models. Nonconstant within-subject rates of biomarker change were found in only 1 model, likely owing to limited within-subject longitudinal follow-up. Creating reliable models that describe the full trajectories of Alzheimer disease biomarkers will require significant additional longitudinal data in individual participants.
The 5 most well-established biomarkers of Alzheimer disease (AD) at this time can be divided into 2 major categories: (1) measures of brain Aβ deposition; these are cerebrospinal fluid (CSF) Aβ421-8 and positron emission tomography (PET) amyloid imaging9-15 and (2) measures of neuronal injury and degeneration; these are CSF tau (total and phosphorylated tau),1,2,4,5,16-18 fluorodeoxyglucose (FDG) PET,19,20 and structural magnetic resonance imaging (MRI).21-26 Some of us recently proposed a hypothetical model describing the temporal evolution of these 5 biomarkers over the entire adult lifespan of an individual who develops AD dementia.27 This model is based on the assumption that different AD biomarkers do not change in an identical fashion over time but rather in an ordered and sequential manner and likewise approach a pathological level in an ordered manner.27-32 This was proposed as a hypothetical model with validation awaiting additional data.
This hypothetical model can be divided into 2 conceptual components. The first is the order in which each biomarker significantly departs from normal (which was addressed in an earlier article33). The second conceptual component, which is the subject of this article, is the shape of the trajectory of each biomarker curve as the disease progresses. The trajectory shape can be envisioned from a plot of each biomarker where the horizontal axis represents clinical disease severity and the vertical axis represents the degree of abnormality of each biomarker, from most normal to most abnormal. In our model, we hypothesized that biomarker trajectories have a sigmoid shape.27 For reasons described later, we did not directly test for sigmoid-shaped trajectories in this article. Rather, we evaluated the shape of biomarker curves by modeling the baseline and longitudinal within-subject rate of change in 5 AD biomarkers as a function of Mini-Mental State Examination (MMSE) score, adjusted for age, in 2 large cohorts separately for APOE ϵ4 noncarriers and carriers. Then, as illustrated in Figure 1, we tested for evidence of nonzero, nonlinear, nonconstant, and interaction terms in baseline values and within-subject rates of biomarker change based on longitudinal values.
Two separate cohorts were created by pooling data from 2 Mayo Clinic studies and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants at Mayo were recruited from the Mayo Clinic Study of Aging, an epidemiologic cohort study of normal aging and mild cognitive impairment (MCI) in individuals aged 70 to 90 years in Rochester, Olmsted County, Minnesota,34 and the Mayo Alzheimer's Disease Research Center. For all participants, written informed consent was obtained for participation as approved by the local institutional review boards.
At baseline, all participants met diagnostic criteria for being cognitively normal or having MCI or AD dementia.35 Clinical disease severity was scored with the MMSE.36 For Mayo Clinic participants, a 38-point test, the Short Test of Mental Status,37 was converted to MMSE scores using an algorithm developed at our center.38 Short Test of Mental Status values transformed to MMSE scores are reported simply as MMSE scores throughout the article.
While we wished to maximize sample size, we also recognized that all participants within a cohort must have every biomarker test result to perform valid within-cohort comparisons. None of the Mayo Clinic participants had CSF samples taken, while many more Mayo than ADNI participants had amyloid PET studies available. We therefore created 2 cohorts, whom we refer to as the CSF/MRI cohort and the PET/MRI cohort. The CSF/MRI cohort included only ADNI participants and was used to evaluate trajectories of hippocampal volume, CSF Aβ42 level, and CSF total tau level. The PET/MRI cohort included both ADNI and Mayo participants and was used to evaluate trajectories of hippocampal volume, amyloid PET with Pittsburgh Compound B (PiB), and FDG-PET. Only visits with all biomarkers available were used in analysis.
The CSF was analyzed using a multiplex xMAP Luminex platform (Luminex Corp) with immunoassay kit–based reagents (INNO-BIA AlzBio3; Innogenetics)5,39 (http://www.adni-info.org/).
The ADNI participants received 1.5-T MRI scans and Mayo participants were scanned at either 1.5 T or 3 T. We used a standard 3-dimensional magnetization-prepared rapid-acquisition gradient-echo imaging sequence and standardized data postprocessing as described in Jack et al.40 Hippocampal and total intracranial volumes were measured at Mayo Clinic; the hippocampus, using FreeSurfer software (version 4.5.0)41; and total intracranial volumes, using an in-house algorithm.42 We evaluated the statistical agreement between FreeSurfer hippocampal volumes obtained at 1.5 T vs 3 T among 91 ADNI participants (32 cognitively normal, 39 with MCI, and 20 with AD) who underwent MRI examinations at both field strengths at the same visit. The Lin concordance correlation coefficient, which measures agreement about the identity line,43 was excellent (0.98; P < .001).
The PET amyloid and FDG imaging methods were similar for Mayo and ADNI participants. Amyloid imaging was performed with carbon 11–labeled PiB.44 Quantitative image analysis for both PiB and FDG was done using our in-house fully automated image processing pipeline described in Jack et al.45 The PiB-PET regions of interest were based on anatomically defined regions while FDG-PET regions of interest were not. Positron emission tomography quantification was reported in SUVR units. Cortical target regions were defined by combining the prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/precuneus regions of interest. The target regions were normalized to the cerebellum.46,47 The FDG-PET scans were analyzed in a similar manner using medial parietal, angular gyrus, and inferior temporal cortical regions of interest as described in Landau et al,48 normalized to pons uptake.
We used linear mixed-effects models49 to investigate the shape of the biomarker trajectories. For each biomarker, the model always included terms for baseline age, baseline MMSE score, change in age from baseline, and change in MMSE score from baseline. Using this model parameterization, the baseline MMSE score term allowed us to assess the baseline, or cohort-level, relationship between biomarker and MMSE score while the change in MMSE score term allowed us to assess the within-subject rates of biomarker change with worsening MMSE score. The age terms were included as a necessary adjustment to impose the correct ordering of a subject's visits over time.
As a first step in model fitting, baseline age and baseline MMSE score were fit as a restricted cubic spline with knots at the 10th, 50th, and 90th percentiles. If the P value from a likelihood ratio test comparing the spline fit with the linear fit was less than P = .10, the nonlinear effects were retained. Otherwise, they were kept as linear terms. We next fit a full model for each biomarker with all 2-way interactions between baseline MMSE score, baseline age, change in MMSE score, and change in age to test for significant interactions but only retained interactions with P < .10 in the final models. Interactions with baseline age and baseline MMSE score or change in MMSE score allowed us to assess if the biomarker–MMSE score relationship depends on age. An interaction term with baseline MMSE score and change in MMSE score allowed us to assess if the within-subject rates of biomarker change depend on the level of disease severity (ie, are nonconstant over disease severity). Nine possible prototype models based on different baseline shapes and longitudinal change are shown in Figure 1.
Random intercepts and slopes for change in age and change in MMSE score were included when possible. All models were adjusted for sex. Hippocampal volume models were additionally adjusted for total intracranial volume. Because of the known effect of APOE on rates of cognitive change,50 separate models were fit within APOE ϵ4 noncarriers and carriers for each biomarker.
For each participant, all available times where all biomarker tests were performed were used in the models. Because of differences in when each biomarker was collected, this reduced the amount of follow-up used for some biomarkers (MRI and FDG), but it was necessary to use only those visits where all biomarkers were available within a cohort so that all biomarkers would be evaluated on equal footing.
The CSF/MRI cohort (111 cognitively normal, 154 with MCI, and 78 with AD) was composed entirely of ADNI participants; all 343 had baseline data and 262 had longitudinal data (Table 1). The PET/MRI cohort (429 cognitively normal, 129 with MCI, and 40 with AD) was composed of Mayo and ADNI participants; all 598 had baseline data and 182 had longitudinal data (Table 1).
The data are summarized in Figure 2 and Figure 3. Each biomarker value is plotted in its native units, with the vertical axis oriented so that increasing values correspond to worsening. Similarly, the x-axis is oriented so that left to right movement corresponds to worsening cognition. Each figure contains 3 columns with the left column showing the biomarker values for individual participants in a random subset of the same 100 participants; a subset was used to reduce overlapping values and allow individual trajectories to be discerned. The middle and right columns illustrate the shape of the curve based on the baseline effects (dotted) and the within-subject rates of biomarker change based on longitudinal data (solid) vs MMSE score for APOE ϵ4 noncarriers (middle column) and carriers (right column) from the model estimates. Within-subject rates of change are shown as an average change in biomarker for a 3-point MMSE score worsening. Nonzero terms indicate biomarker values that change with worsening MMSE score. Nonlinear terms indicate baseline effects that are nonlinear (most often saturating or plateauing) with advancing MMSE score. Interaction terms indicate that the relationship between biomarker and MMSE score depends on baseline age. Nonconstant terms indicate that the within-subject rate of change depends on baseline MMSE score or disease severity. Significant nonlinear, interaction, and nonconstant terms imply a nonzero relationship with biomarker and MMSE score. Significant terms from the models summarized in Figure 2 and Figure 3 are displayed in Table 2.
Figure 2 illustrates biomarkers vs MMSE score in the CSF/MRI cohort. The baseline effects were that all biomarkers worsened (ie, displayed nonzero slope) with lower MMSE scores at baseline (P ≤ .005) except for total tau level in APOE ϵ4– positive participants (Table 2). A cross-sectional age effect in the CSF Aβ model was unexpected in that CSF Aβ values decreased/worsened as ages increased from 70 to 78 years and then increased for older ages. The longitudinal effects were that within-subject rates in hippocampal volume worsened (ie, nonzero slope) as MMSE score worsened in both APOE ϵ4– negative and –positive participants (P = .004 and P < .001) (Table 2). Within-subject rates of Aβ42 worsened (ie, nonzero slope) as MMSE score worsened in APOE ϵ4– positive participants only (P = .04). The within-subject total tau rate changed as MMSE score worsened (ie, nonzero slope) for APOE ϵ4– positive participants only (P = .007) but the rate of change depended on baseline age.
Figure 3 shows biomarkers vs MMSE score in the PET/MRI cohort. The baseline effects were that hippocampal volumes in APOE ϵ4– negative participants and FDG in APOE ϵ4– positive participants worsened (ie, nonzero slope) with lower MMSE scores (P < .001) (Table 2). The relationship with biomarker and baseline MMSE score for both PiB- and FDG-PET in APOE ϵ4– negative participants depended on baseline age (P = .04 and P = .08). Baseline hippocampal volume and amyloid PET values worsened in APOE ϵ4– positive participants with lower MMSE scores but the effect plateaued at lower MMSE values (ie, nonlinear slope) (P = .005 and P = .009). The longitudinal effects were that within-subject rates of change in hippocampal volume worsened (ie, nonzero slope) as MMSE score worsened (P = .03) in APOE ϵ4– negative participants (Table 2). In APOE ϵ4– positive participants, within-subject rates of change in hippocampal volume also worsened as MMSE score worsened, but this effect depended on baseline age (P = .001). Within-subject rates of change in PiB-PET decreased (ie, nonzero slope) as MMSE score worsened in APOE ϵ4– positive participants (P = .03) but not in APOE ϵ4– negative participants. Within-subject rates of FDG worsened as MMSE score worsened (ie, nonzero slope) in APOE ϵ4– positive participants (P < .001) but not in APOE ϵ4– negative participants.
We performed a subgroup analysis restricted to participants who had evidence of amyloid deposition either by amyloid PET or CSF Aβ42 level and thus are likely on the AD pathophysiological pathway. We selected PiB more than 1.4 SUVR as the cut point. This reflects a lenient cut point but one that still likely eliminated individuals clearly not on the AD pathophysiological pathway. Using data from an analysis of 41 subjects who had PiB and CSF measured on the same visit,51 we used linear regression to identify the CSF Aβ42 cut point that corresponded to PiB more than 1.4 SUVR as being 209 pg/mL. This resulted in an “amyloid-positive” CSF cohort of 243 participants and a PET cohort of 328 participants (Table 3). Figure 4 illustrates biomarkers vs MMSE score in the amyloid-positive participants. Because the range of amyloid values was truncated by design in this subgroup analysis, we did not create trajectory plots for PiB-PET or CSF Aβ42 level and limited the plots to MRI, CSF tau level, and FDG.
Significant effects in the amyloid-positive cohorts are summarized in Table 4. While many findings were the same between the amyloid-positive cohorts and the entire sample, there were some differences. In the amyloid-positive CSF/MRI cohort, baseline hippocampal volume increased nonlinearly, ie, plateaued at lower MMSE values in APOE ϵ4 carriers. Within-subject hippocampal volume was nonconstant (rates of atrophy decelerated at higher MMSE scores) in APOE ϵ4 noncarriers. In the amyloid-positive PET/MRI cohort, baseline hippocampal volume and FDG-PET increased nonlinearly, ie, plateaued at lower MMSE values in APOE ϵ4 noncarriers. Within-subject change was nonzero for FDG-PET in APOE ϵ4 noncarriers. There was also an interaction between baseline age and baseline MMSE score in the hippocampal volume model for the APOE ϵ4 noncarriers.
Our major findings were the following: (1) Overall, biomarker trajectory shapes were complex and affected by interactions with age and APOE status. (2) Baseline biomarker values generally worsened (ie, nonzero slope) with lower baseline MMSE scores. (3) Baseline hippocampal volume, amyloid PET, and FDG-PET values plateaued (ie, nonlinear slope) with lower MMSE scores in 1 or more analyses. (4) Longitudinally, within-subject rates of biomarker change were associated with worsening MMSE scores. (5) Nonconstant within-subject rates of biomarker change were found in only 1 model; the rate of hippocampal volume change decelerated with worsening MMSE scores in amyloid-positive APOEϵ4– negative participants. (6) Trajectories for a given biomarker were often different in APOEϵ4 carriers vs noncarriers in the overall samples. This was less often so in the amyloid-positive subsamples. (7) While most findings were the same between the amyloid-positive cohorts and the entire sample, there was a slightly greater tendency toward nonlinear baseline effects in amyloid-positive participants.
Our hypothetical biomarker model27 predicts that each biomarker follows a sigmoid-shaped trajectory. The rationale for this prediction starts with the assumption that the rate of change of a biomarker denoting accumulating AD pathophysiology in the brain should be zero from birth through at least early adulthood. At some point, eg, age 50 years to 70 years, AD biomarkers deflect from the normal baseline and begin to become abnormal, which by definition represents acceleration in rate. Based on prior evidence that some biomarker rates of change (ie, amyloid PET, CSF Aβ42 level, and total tau level) do not accelerate in the dementia phase of the disease,31,52 we presumed that biomarker rates do not continue to accelerate indefinitely but instead begin to saturate or plateau at some point, which represents deceleration. An initial period of acceleration followed later by deceleration defines a trajectory that is approximately sigmoidal, with the midpoint of the curve defined as the initiation of deceleration. A second reason to suspect that biomarkers should follow a sigmoid-shaped trajectory relates to sensitivity limits of any measurement technique at extremes. Floor and ceiling measurement sensitivity effects impart a sigmoid shape to a data distribution.
Sources outside the field of human biomarker studies suggest that amyloid and neurodegenerative biomarkers might follow a sigmoid-shaped function. Ingelsson et al28 found in human autopsy studies that amyloid accumulation plateaus with increasing disease duration. Tau fibrillization follows a sigmoid-shaped function with time in vitro.53,54 A cumulative damage model of neurodegenerative disease where the risk of cell death in the vulnerable population of cells changes over time predicts a sigmoid-shaped trajectory of neurodegenerative brain atrophy.55,56
Reports from analyses of ADNI data draw somewhat inconsistent conclusions about the shapes of biomarker trajectories. Caroli and Frisoni57 analyzed cross-sectional ADNI data and found that mean baseline hippocampal volume, CSF Aβ42, and CSF tau data could be better modeled as a function of worsening cognition with sigmoid-shaped curves compared with linear fits. Lo et al58 examined rates of change of biomarkers in ADNI participants and illustrated deceleration in CSF Aβ42 levels but acceleration in hippocampal atrophy rates with advancing disease. Schuff et al59 found acceleration in atrophy rates in ADNI subjects with MCI and AD. Sabuncu et al60 examined brain atrophy rates in ADNI participants who had an AD-like CSF profile. They found that atrophy rates in a set of AD signature regions of interest exhibit early acceleration followed by deceleration, which was consistent with a sigmoid-shaped curve. Conversely, they found rates of hippocampal volume loss exhibited positive acceleration.
In the present study, we fit the models in such a way that would allow us to assess the shape of the biomarker trajectories without imposing a particular structure (ie, a sigmoid shape) on the data. Flexible restricted cubic splines allowed for nonlinearity if there was evidence for it. Interaction terms allowed for biomarker–MMSE score relationships to depend on covariates. This way of modeling let the data “speak for themselves” and was preferred in this study because of several important limitations in the nature of the data. (1) The right- and left-hand portions of a sigmoid curve are where the maximum inflection occurs and thus the portions of the function where data are most needed to detect acceleration and deceleration. Unfortunately, our data are sparse in these regions. In participants with abnormal biomarkers at baseline, we have no data that would allow us to characterize the initial deviation of biomarkers from their normal baseline. The right-hand tail is equally problematic in that many patients survive a decade or more after the clinical diagnosis of AD dementia is made, but most stop participating in clinical research studies once they develop moderate dementia. Indeed, the subjects with AD in our samples had only mild dementia (median MMSE score of 24 for the CSF/MRI cohort and 23 for the PET/MRI cohort). (2) The median follow-up time in our data was only about 1 year with a maximum of only 4 years. This is a small fraction of the total duration of the disease, which may span 30 years or more. Examining such a small window of time in each subject makes it difficult to detect acceleration or deceleration in within-subject rates. (3) We lacked a linear clinical measure of disease progression. Every cognitive testing instrument has a nonlinear response function with both floor and ceiling effects.50,61 Because subjects spanning the cognitive continuum were combined to estimate biomarker trajectories, a single universal cognitive test was needed to index all subjects on a common axis. The MMSE score was the best option that was available in all ADNI and Mayo subjects. However, the limited range of the MMSE score in cognitively normal participants (roughly 30-27) in particular made estimation of trajectory shape early in the disease particularly problematic. In many of our cognitively normal participants, MMSE scores did not change or fluctuated randomly from 1 point to the next.
Our results do not disagree with sigmoid-shaped biomarker trajectories in that most biomarkers worsened as MMSE score worsened in both baseline and longitudinal analyses, which is consistent with the middle, roughly linear, portion of a sigmoid curve. While cross-sectional data may be influenced by cohort effects, we did see some baseline effects that were consistent with a sigmoid-shaped trajectory (ie, baseline effects that plateaued with worsening MMSE scores). However, we found nonconstant within-subject rates in only 1 analysis. Several prior studies (including one of our own) have shown that rates of brain atrophy accelerate prior to incident dementia.62-65 However, these earlier MRI studies had considerably more within-subject longitudinal data than we had in the present study. Our failure to detect acceleration or deceleration in within-subject MRI rates may well be due to limited longitudinal data because we only used those times in individual participants where all biomarkers were available.
Alzheimer disease biomarkers are poised to become an essential component of a comprehensive assessment of the disease. In particular, AD biomarkers constitute a major (some would say only) window into the disease in its long preclinical phase. Designing clinical trials in early symptomatic and preclinical disease will depend on acquiring a thorough understanding of the longitudinal trajectory of AD biomarkers. In addition, the notion of biomarker trajectories is central to the staging proposed in the recent preclinical AD research criteria.66 However, creating reliable models that accurately describe the full trajectory shapes of AD biomarkers will require significant additional longitudinal data in individual participants beginning prior to deviation of biomarkers from normality through the end stage of the disease and ultimately to autopsy. Ideally, these data would be acquired in well-defined epidemiological cohorts.
Correspondence: Clifford R. Jack Jr, MD, Department of Radiology, Mayo Clinic and Foundation, 200 First St SW, Rochester, MN 55905 (firstname.lastname@example.org).
Accepted for Publication: September 30, 2011.
Published Online: March 12, 2012. doi:10.1001/archneurol.2011.3405
Author Contributions: All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Jack, Vemuri, Weigand, and Trojanowski. Acquisition of data: Jack, Lowe, Kantarci, Bernstein, Trojanowski, Shaw, Weiner, and Knopman. Analysis and interpretation of data: Jack, Vemuri, Wiste, Weigand, Lesnick, Lowe, Kantarci, Senjem, Gunter, Boeve, Trojanowski, Aisen, Weiner, Petersen, and Knopman. Drafting of the manuscript: Jack and Trojanowski. Critical revision of the manuscript for important intellectual content: Jack, Vemuri, Wiste, Weigand, Lesnick, Lowe, Kantarci, Bernstein, Gunter, Boeve, Trojanowski, Shaw, Aisen, Weiner, Petersen, and Knopman. Statistical analysis: J ack, Vemuri, Wiste, Weigand, Lesnick, Senjem, and Trojanowski. Obtained funding: Jack, Lowe, and Weiner. Administrative, technical, and material support: Jack, Kantarci, Bernstein, Senjem, Gunter, Boeve, and Weiner. Study supervision: Jack, Lowe, and Weiner.
Financial Disclosure: Dr Lowe receives grant support from GE Healthcare, Siemens Molecular Imaging, and Avid Radiopharmaceuticals and is a consultant for Bayer Pharmaceuticals. Dr Boeve's institution has received grants from the National Institutes of Health, Cephalon Inc, Allon Pharmaceuticals, the Mangurian Foundation, the Alzheimer's Association, and GE Healthcare and Dr Boeve has received royalties as the coeditor of the text Behavioral Neurology of Dementia and payment for development of educational presentations from the American Academy of Neurology. Dr Weiner has served on the following scientific advisory boards: 2009: Elan/Wyeth Alzheimer's Immunotherapy Program North American Advisory Board, Novartis Misfolded Protein Scientific Advisory Board Meeting, Banner Alzheimer's Institute Alzheimer's Prevention Initiative Advisory Board Meeting, and Research Advisory Committee on Gulf War Veterans' Illnesses; 2010: Lilly, Araclon and Institut Catala de Neurociencies Aplicades, Gulf War Veterans Illnesses Advisory Committee (VACO), and Biogen Idec; 2011: Pfizer. Dr Weiner has been a consultant for the following: 2009: Elan/Wyeth, Novartis, Forest, Ipsen, and Daiichi Sankyo, Inc; 2010: AstraZeneca, Araclon, Medivation/Pfizer, Ipsen, TauRx Therapeutics Ltd, Bayer Healthcare, Biogen Idec, Exonhit Therapeutics, SA, Servier, and Synarc; 2011: Pfizer. Dr Weiner has received funding for travel from the following: 2009: Elan/Wyeth Alzheimer's Immunotherapy Program North American Advisory Board, Alzheimer's Association, Forest, University of California, Davis, Tel-Aviv University Medical School, Colloquium Paris, Ipsen, Wenner-Gren Foundations, Social Security Administration, Korean Neurological Association, National Institutes of Health, Washington University at St Louis, Banner Alzheimer's Institute, Clinical Trials on Alzheimer's Disease, Veterans Affairs Central Office, Beijing Institute of Geriatrics, Innogenetics, and New York University; 2010: NeuroVigil, Inc, CHRU–Hopital Roger Salengro, Siemens, AstraZeneca, Geneva University Hospitals, Lilly, University of California, San Diego, ADNI, Paris University, Institut Catala de Neurociencies Aplicades, University of New Mexico School of Medicine, Ipsen, and CTAD (Clinical Trials on Alzheimer's Disease); 2011: Pfizer, AD Parkinson disease meeting, Paul Sabatier University, and Novartis. Dr Weiner has been on editorial advisory boards for AD and dementia and magnetic resonance imaging. Dr Weiner has received honoraria from the following: 2009: American Academy of Neurology and Ipsen; 2010: NeuroVigil, Inc, and Insitut Catala de Neurociencies Aplicades. Dr Weiner has received commercial entities research support from Merck and Avid and government entities research support from the Department of Defense and Veterans Affairs. Dr Weiner has stock options with Synarc and Elan. Dr Petersen is the chair of the Data Monitoring Committee for Pfizer, Inc, and Janssen Alzheimer Immunotherapy; is a consultant for Elan Pharmaceuticals and GE Healthcare; and gave a Continuing Medical Education presentation for Novartis. Dr Knopman serves as deputy editor for Neurology ; serves on a Data Safety Monitoring Board for Lilly Pharmaceuticals; is an investigator in clinical trials sponsored by Baxter, Elan Pharmaceuticals, and Forest Pharmaceuticals; and receives research support from the National Institutes of Health. The following organizations contributed to the Foundation for the National Institutes of Health and thus to the National Institute on Aging–funded ADNI: Abbott, Alzheimer's Association, Alzheimer's Drug Discovery Foundation, Anonymous Foundation, AstraZeneca, Bayer Healthcare, BioClinica, Inc (ADNI 2), Bristol-Myers Squibb, Cure Alzheimer's Fund, Eisai, Elan, Gene Network Sciences, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly & Company, Medpace, Merck, Novartis, Pfizer Inc, Roche Schering-Plough, Synarc, and Wyeth.
Funding/Support: This work was supported by National Institute on Aging grants R01AG11378, P50 AG16574, and U01 AG06786 and the ADNI, the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Foundation, and the Robert H. and Clarice Smith Alzheimer's Disease Research Program of the Mayo Foundation.
Additional Contributions: Samantha Wille, BA, prepared the manuscript. Data used in the preparation of this article were obtained from the ADNI database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.
Create a personal account or sign in to: