Abstract
Objective
The newly proposed National Institute on Aging-Alzheimer’s Association (NIA-AA) criteria for mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) suggest a combination of clinical features and biomarker measures, but their performance in the community is not known.
Methods
The Mayo Clinic Study of Aging (MCSA) is a population-based longitudinal study of non-demented subjects in Olmsted County, Minnesota. A sample of 154 MCI subjects from the MCSA was compared to a sample of 58 amnestic MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI 1) to assess the applicability of the criteria in both settings and to assess their outcomes.
Results
In the MCSA, 14% and in ADNI 1 16% of subjects were biomarker negative. In addition, 14% of the MCSA and 12% of ADNI 1 subjects had evidence for amyloid deposition only, while 43% of MCSA and 55% of ADNI 1 subjects had evidence for amyloid deposition plus neurodegeneration (MRI atrophy, FDG PET hypometabolism or both). However, a considerable number of subjects had biomarkers inconsistent with the proposed AD model, e.g., 29% of MCSA subjects and 17% of the ADNI 1 subjects had evidence for neurodegeneration without amyloid deposition. These subjects may not be on an AD pathway. Neurodegeneration appears to be a key factor in predicting progression relative to amyloid deposition alone.
Interpretation
The NIA-AA criteria apply to most MCI subjects in both the community and clinical trials settings however, a sizeable proportion of subjects had conflicting biomarkers which may be very important and need to be explored.
MCI and Biomarkers
Mild cognitive impairment (MCI) represents a state between the cognitive changes of aging and early dementia1,2. Even though MCI as a general construct need not be progressive nor be the earliest stage of Alzheimer’s disease (AD), it has been most often studied in this context and is commonly referred to as the earliest clinical manifestation of AD pathophysiology 3.
The National Institute on Aging and the Alzheimer’s Association (NIA-AA) recently published research criteria for MCI due to AD that incorporated the use of biomarkers to assess the likelihood that the MCI syndrome is due to the underlying pathophysiology of AD 3. At present while only the clinical diagnosis of MCI has been recommended for use by practitioners, a growing body of evidence strongly suggests that the clinical diagnosis of MCI plus the use of imaging and fluid biomarkers will enhance the likelihood of predicting which subjects are likely to progress to AD dementia 4-11. The new MCI due to AD criteria are currently untested, and, in particular, their performance in the general community is unknown. The distribution of these biomarkers in a clinically diagnosed group of MCI subjects who have been derived from a random sample of non-demented subjects would be particularly informative with respect to the utility of the biomarkers in general clinical practice and potentially for FDA regulatory purposes.
The present study assesses the distribution of imaging biomarkers in an MCI cohort drawn from the Mayo Clinic Study of Aging (MCSA) which is a population-based sample of non-demented subjects in Olmsted County, MN 12. A comparison of biomarker distributions between the MCSA and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is also reported.
Methods
This biomarker study was part of the MCSA, a population-based study of residents in Olmsted County, Minnesota, ages 70-89 years at the time of enrollment. The overall study design has been published elsewhere12.
Briefly, all Olmsted County residents who were aged 70-89 on October 1, 2004, were identified using the Rochester Epidemiology Project medical records-linkage system 13-15. We randomly selected 5,233 of them for recruitment, and subjects with a pre-existing diagnosis of dementia were identified by screening the medical records in the system, and the clinical information was reviewed in detail by a neurologist (DSK). Subjects who had been diagnosed with dementia were not invited to participate in this study and, consequently, a total of 4,398 subjects were considered eligible for participation in the active evaluation.
Clinical Evaluations
Each participant received an evaluation by a study coordinator who collected information regarding medical history, family history, and medications. The study coordinator also interviewed a study partner about the individual and completed a modified Clinical Dementia Rating 16. The second part of the examination was conducted by a physician who performed a medical history review, mental status examination, and performed a neurological examination. The third component consisted of a neuropsychological evaluation in which nine tests were performed, comprising four cognitive domains. Three tests were used for memory and two for the other domains: Memory: Wechsler Memory Scale-Revised (WMS-R) Logical Memory II (delayed recall), WMS-R Visual Reproductions II (delayed recall), and the Auditory Verbal Learning Test (delayed recall) 17,18; Attention-Executive Function: the Trail Making Test Part B and Digit Symbol Substitution from the Wechsler Adult Intelligent Scale-Revised (WAIS-R) 19, 20; Language: the Boston Naming Test and category fluency scores 21; and Visuospatial Skills: Block Design and Picture Completion Tests from the WAIS-R 20. The raw scores from each test were transformed into age-adjusted scores using independent normative data from the Mayo’s Older American Normative Studies 22, 23.
Diagnostic Categories
For the purposes of this study, performance of an individual in a particular cognitive domain was measured by comparing the person’s domain score to the score in normal subjects from the normative work in the same but independent population 22. Subjects with scores of approximately 1.0 SD or greater below the age-specific mean in the general population were considered for possible cognitive impairment. However, it is important to note that no algorithm was used to derive the diagnosis of MCI; rather, a panel including the study coordinator, neuropsychologist, and physician who had examined the subject discussed each component of the examination and assigned a diagnosis of MCI according to published criteria 24. The criteria used for MCI included the following: 1) cognitive concern by the subject, informant, or clinician; 2) impairment in one or more of four cognitive domains from the neuropsychological test battery; 3) essentially normal functional activities as derived from the CDR and the Functional Activities Questionnaire (FAQ) and 4) absence of dementia (DSM-IV) 25. Subjects who were diagnosed with MCI were further classified as having amnestic MCI (aMCI) if the memory domain was impaired or non-amnestic MCI (naMCI) if there was no impairment in memory 24. In follow-up evaluations in the MCSA, approximately 15 months after the previous assessment, the investigators were blinded to the previous diagnostic classification of the subjects.
ADNI Comparison Group
Individuals from the Alzheimer’s Disease Neuroimaging Intiative 1 (ADNI 1) who had aMCI and 1.5T MRI, Fluorodeoxyglucose (18F-FDG) PET and 11C Pittsburgh Compound B (PiB)-PET scans at the time of the aMCI diagnosis were selected as a comparison sample to determine the correspondences between a population-based and clinical trials samples of subjects. The ADNI 1 subjects were all aMCI and had to have a memory impairment at approximately 1.5 SD below an education-adjusted norm for Logical Memory II and their CDR had to be 0.5 16, 17.
Imaging Methods
For both Mayo (3T) and ADNI (1.5T) subjects, MRI was performed with a 3D-MPRAGE sequence26. Images were corrected for distortion due to gradient non-linearity and for bias field 27. Our primary MRI measure was hippocampal volume measured with FreeSurfer software (version 4.5.0) 28. Each subject’s raw hippocampal volume was adjusted by his/her total intracranial volume 29, measured using an in-house algorithm, to form an adjusted hippocampal volume (HVa). We calculated HVa as the residual from a linear regression of hippocampal volume (y) versus total intracranial volume (x).
At Mayo, PET images were acquired using a GE Discovery RX PET/CT scanner. A CT image is obtained for attenuation correction. The 11C Pittsburgh Compound B (PiB)-PET scan consisting of four 5-minute dynamic frames was acquired 40–60 minutes after injection 30, 31. Fluorodeoxyglucose (18F-FDG) PET images were obtained 1 hour after the PiB scan. Subjects were injected with 18F-FDG and imaged after 30–38 minutes, for an 8-minute image acquisition consisting of four 2-minute dynamic frames. PET acquisition protocols for ADNI were similar to those at Mayo, but scanner models varied ADNI is a multi-site study.
Quantitative image analysis for both PiB and FDG was done using our in-house fully automated image processing pipeline 32. A global cortical PiB-PET retention ratio (SUVr) was obtained by calculating the median uptake over voxels in the prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/precuneus values for each subject and dividing this by the median uptake over voxels in the cerebellar gray matter regions of interest (ROI) of the atlas 32. FDG-PET scans were analyzed in a similar manner. We used angular gyrus, posterior cingulate, and inferior temporal cortical ROIs to denote an “AD-signature meta ROI”, as described in Landau et al 33, normalized to pons and vermis uptake. Imaging data for MCSA and ADNI subjects was analyzed at Mayo, thus analytic methods were identical for Mayo and ADNI subjects.
Statistical methods for developing imaging biomarker and cognitive testing cut-points
Even though all biomarkers and cognitive tests are continuous measures, the new criteria for MCI due to AD require the classification of every biomarker and cognitive test as either normal or abnormal3. Thus, cut points must be created in these continuous distributions. The ideal method for selecting biomarker cut-points would be to use autopsy diagnoses as the standard for comparison 34-37. Because we do not have autopsy cohorts with antemortem 3T MRI, PiB PET and FDG PET, we created cut-points such that a majority of clinically defined AD dementia patients would be deemed abnormal. Cut-points were based on estimated percentiles. For biomarkers where higher values are worse (PiB PET), the cut-point was the 10th percentile of AD distribution (corresponding to 90% sensitivity) 38. For biomarkers where lower values are worse (FDG PET, HVa), the cut-point was the 90th percentile of the AD distribution. In this way, approximately 90% of ADs were considered abnormal. While we did not have CSF available in our subjects, we had amyloid (PiB PET) and neurodegenerative (FDG PET and MRI) biomarkers in all subjects, and were therefore able to stage all subjects in accordance with the new MCI due to AD criteria 3. We had two measures in the neurodegenerative biomarker category (FDG PET and MRI) and we considered a subject positive for evidence of neurodegeneration if one or both measures fell below the cut-point.
Variables were described as median (interquartile range) or count (percent). Differences between the MCSA aMCI and ADNI1 subjects and between the MCSA aMCI and naMCI subjects were tested with Wilcoxon rank-sum tests for continuous variables and chi-square tests for categorical data. Differences across the four biomarker groups were tested with Kruskal-Wallis tests for continuous variables and chi-square tests for categorical data. We computed multinomial 95% confidence intervals for the percentages in each of the four biomarker groups within the ADNI 1 and aMCI MCSA subjects. The study was approved by the Mayo Clinic and Olmsted Medical Center Institutional Review Boards.
Results
For this study, 154 subjects met the clinical criteria for any type of MCI in the MCSA and had received an MRI, FDG PET and PiB PET scans at the time of the MCI diagnosis. Of these, 126 (82%) were aMCI subjects and 28 (18%) were naMCI. In the ADNI 1, 58 subjects met the clinical criteria for aMCI and received MRI, FDG PET, and PiB PET scans. The demographic, clinical, and imaging characteristics of the aMCI MCSA subjects and ADNI 1 subjects are shown in Table 1.
Table 1. Characteristics of all aMCI participants with MRI and PET from the MCSA and the ADNI1.
| Characteristic | MCSA (N = 126) |
ADNI 1 (N = 58) |
P |
|---|---|---|---|
| Age, years, median (IQR) | 82 (78, 86) | 75 (71, 81) | <0.001 |
| Male gender, no. (%) | 84 (67) | 37 (64) | 0.70 |
| Education, years, median (IQR) | 13 (12, 16) | 16 (14, 18) | <0.001 |
| APOE ε4 positive, no. (%) | 49 (40) | 32 (55) | 0.05 |
| MMSE, median (IQR) | 26 (24, 27) | 27 (26, 29) | <0.001 |
| CDR sum of boxes, median (IQR) | 1.0 (0.5, 1.5) | 1.5 (1.0, 2.4) | <0.001 |
| PIB Ratio, median (IQR) | 1.66 (1.36, 2.22) | 1.90 (1.39, 2.28) | 0.39 |
| PIB > 1.50, no. (%) | 72 (57) | 39 (67) | 0.19 |
| FDG Ratio, median (IQR) | 1.29 (1.18, 1.42) | 1.27 (1.17, 1.37) | 0.32 |
| FDG < 1.31, no. (%) | 68(54) | 34 (59) | 0.56 |
| Adjusted Hippocampal Volume, median (IQR) |
−0.71 (−1.29, −0.29) | −0.70 (−1.42, 0.03) | 0.34 |
| HVa < 0.70, no. (%) | 64 (51) | 29 (50) | 0.92 |
| Biomarker Group | 0.32 | ||
| All biomarkers negative | 18 (14) | 9 (16) | |
| Amyloid positive only | 18 (14) | 7 (12) | |
| Amyloid positive & neurodegeneration |
54 (43) | 32 (55) | |
| Neurodegeneration only | 36 (29) | 10 (17) | |
| Follow-up diagnosis*, no. (%) | 0.006 | ||
| CN | 25 (26) | 3 (6) | |
| MCI | 57 (59) | 32 (65) | |
| Dementia | 14(15) | 14 (29) | |
| Annual change in MMSE | |||
| N | 93 | 48 | |
| Median (IQR) | 0.00 (−1.58, 0.74) | −0.82 (−2.93, 0.97) | 0.38 |
| Annual change in CDR-SB | |||
| N | 96 | 48 | |
| Median (IQR) | 0.38 (0.00, 1.23) | 0.50 (0.00, 1.00) | 0.53 |
Follow-up data was obtained at the 15 month visit in the MCSA and the 12 month visit in the ADNI.
The ADNI subjects were younger and more highly educated than the MCSA aMCI subjects. The MCSA aMCI subjects on average were more mild in the state of their disease process with a median CDR sum of boxes (SB) or 1.0 (IQR 0.5-1.5) while the ADNI subjects were more impaired, by design, with a CDR SB of 1.5 (1.0-2.4).
The subjects were classified into one of four groups based on their amyloid status and the presence or absence of neurodegenerative features as measured by FDG PET or MRI hippocampal volume. Cut-points for normal and abnormal were used as described above 38. Table 1 and the Figureshow the similar distribution of subjects into the four biomarker groups for the MCSA aMCI and ADNI 1 subjects. In the MCSA, among those aMCI subjects with amyloid and neurodegeneration, 13 (24%) had abnormal HVa alone, 10 (19%) had abnormal FDG alone, and 31 (57%) had both abnormal HVa and FDG, and in the ADNI 1 subjects, 7 (22%) had abnormal HVa, 8 (25%) had abnormal FDG, and 17 (53%) had both. Among those with neurodegeneration but no evidence of amyloid deposition, 9 (25%) had abnormal HVa, 16 (44%) had abnormal FDG, and 11 (31%) had both abnormal HVa and FDG in the MCSA aMCI subjects and in the ADNI 1 subjects, 1 (10%) had abnormal HVa, 5 (50%) had abnormal FDG, and 4 (40%) had both.
Figure.
Frequency of positive biomarkers in the Mayo Clinic Study of Aging (MCSA) and Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Table 2 shows the demographics, clinical characteristics, and imaging features of the four biomarker classification groups in the subgroup of MCSA subjects with aMCI. The percentage of Apolipoprotein E4 carriers correlated with the presence of amyloid as expected (p<0.001).
Table 2. Characteristics of all MCSA aMCI subjects by biomarker group.
| Characteristic | Biomarker Negative (N = 18) |
Amyloid Only (N = 18) |
Amyloid + Neurodegeneration (N = 54) |
Neurodegeneration Only (N = 36) |
P |
|---|---|---|---|---|---|
| Age, years, median (IQR) | 80 (76, 84) | 79 (77, 83) | 84 (80, 87) | 82 (78, 85) | 0.15 |
| Male gender, no. (%) | 14 (78) | 10 (56) | 32 (59) | 28 (78) | 0.15 |
| Education, years, median (IQR) | 12 (12, 14) | 12 (12, 16) | 14 (12, 16) | 13 (12, 16) | 0.50 |
| APOE s4 positive, no. (%) | 2 (12) | 9 (50) | 34 (63) | 4 (11) | <0.001 |
| MMSE, median (IQR) | 27 (25, 27) | 24 (24, 27) | 26 (24, 27) | 25 (24, 27) | 0.22 |
| CDR sum of boxes, median (IQR) | 0.5 (0.0, 1.0) | 1.0 (0.5, 1.5) | 1.0 (0.5, 2.4) | 0.5 (0.0, 1.0) | 0.003 |
| PIB Ratio, median (IQR) | 1.36 (1.34, 1.39) | 1.97 (1.85, 2.13) | 2.23 (1.80, 2.50) | 1.35 (1.28, 1.40) | -- |
| FDG Ratio, median (IQR) | 1.46 (1.41, 1.53) | 1.45 (1.39, 1.54) | 1.22 (1.14, 1.30) | 1.26 (1.18, 1.31) | -- |
| Adjusted Hippocampal Volume, median (IQR) |
−0.21 (−0.55, 0.34) | −0.18 (−0.48, 0.01) | −1.04 (−1.70, −0.81) | −0.87 (−1.18, −0.52) | -- |
| Diagnosis at follow-up, no. (%) | 0.005 | ||||
| CN | 6 (50) | 5 (36) | 2(5) | 12 (36) | |
| MCI | 5(42) | 9 (64) | 29 (78) | 14 (42) | |
| Dementia | 1 (8) | 0 (0) | 6(16) | 7(21) | |
| Annual change in MMSE | |||||
| N | 12 | 14 | 35 | 32 | |
| Median (IQR) | 0.00 (−0.82, 0.84) | 0.00 (−0.70, 0.83) | −0.80 (−2.34, 0.00) | 0.00 (−1.58, 0.77) | 0.042 |
| Annual change in CDR-SB | |||||
| N | 11 | 13 | 39 | 33 | |
| Median (IQR) | 0.00 (−0.36, 0.00) | 0.35 (−0.38, 0.42) | 0.39 (0.00, 1.50) | 0.40 (0.00, 1.55) | 0.15 |
Of the 126 aMCI subjects in the MCSA, 96 had a follow-up at 15 months and 49 of the 58 ADNI 1 subjects had a follow-up at approximately 12 months (Table 3). For the MCSA subjects during the 15 month period, 16 (17%) progressed to dementia (12 of 14 aMCI subjects and 1 of 2 naMCI subjects progressed clinically to dementia due to AD), 57 (59%) remained MCI, and 25 (26%) were designated as cognitively normal. For the ADNI 1 subjects, 14 (29%) had progressed to dementia (all 14 to clinical dementia due to AD), 32 (65%) remained MCI, and 3 (6%) were designated as cognitively normal. In both MCSA and ADNI 1 aMCI groups, the highest proportion of subjects who progressed to dementia was found in the amyloid plus neurodegeneration group and the neurodegeneration only group. In neither MCSA nor ADNI 1 did progression to dementia occur in subjects who were in the amyloid only biomarker group.
Table 3. Characteristics of all ADNI aMCI subjects by biomarker group.
| Characteristic | Biomarker Negative (N = 9) |
Amyloid Only (N = 7) |
Amyloid + Neurodegeneration (N = 32) |
Neurodegeneration Only (N = 10) |
P |
|---|---|---|---|---|---|
| Age, years, median (IQR) | 72 (64, 77) | 75 (74, 80) | 75 (71, 81) | 77 (73, 83) | 0.45 |
| Male gender, no. (%) | 6 (67) | 5 (71) | 19 (59) | 7 (70) | 0.89 |
| Education, years, median (IQR) | 18 (16, 18) | 16 (14, 16) | 16 (14, 18) | 16 (16, 18) | 0.61 |
| APOE ε4 positive, no. (%) | 1 (11) | 6 (86) | 21 (66) | 4 (40) | 0.007 |
| MMSE, median (IQR) | 28 (27, 29) | 28 (28, 30) | 27 (26, 28) | 28 (26, 29) | 0.07 |
| CDR sum of boxes, median (IQR) | 1.5 (1.0, 2.0) | 1.0 (1.0, 1.5) | 2.0 (1.4, 2.6) | 1.0 (0.6, 1.9) | 0.23 |
| PIB Ratio, median (IQR) | 1.32 (1.24, 1.39) | 1.78 (1.67, 2.29) | 2.24 (2.09, 2.34) | 1.30 (1.28, 1.36) | -- |
| FDG Ratio, median (IQR) | 1.43 (1.36, 1.60) | 1.41 (1.39, 1.45) | 1.23 (1.16, 1.29) | 1.16 (1.05, 1.26) | -- |
| Adjusted Hippocampal Volume, median (IQR) |
0.56 (0.13, 1.29) | 0.03 (−0.29, 0.70) | −0.96 (−1.59, −0.71) | −0.85 (−1.63, 0.02) | -- |
| Diagnosis at follow-up, no. (%) | 0.19 | ||||
| CN | 0 (0) | 1 (17) | 2 (8) | 0 (0) | |
| MCI | 8 (89) | 5 (83) | 13 (50) | 6 (75) | |
| Dementia | 1 (11) | 0 (0) | 11 (42) | 2 (25) | |
| Annual change in MMSE | |||||
| N | 9 | 6 | 25 | 8 | |
| Median (IQR) | 0.00 (0.00, 1.05) | 0.43 (−1.44, 0.96) | −1.00 (−3.00, 0.00) | −2.41 (−3.19, 0.24) | 0.44 |
| Annual change in CDR-SB | |||||
| N | 9 | 6 | 25 | 8 | |
| Median (IQR) | 0.50 (0.00, 0.52) | −0.49 (−0.77, 0.25) | 0.50 (0.40, 1.45) | 0.49 (−0.12, 0.68) | 0.17 |
Table 4 shows the comparisons of the aMCI and naMCI subjects in the MCSA. The PiB ratios were higher (p=0.048) and HVa values were smaller (p<0.001) in the aMCI subjects compared to the naMCI subjects with a greater proportion of the aMCI subjects having abnormal HVa values (p=0.013).
Table 4. Characteristics of all MCSA MCI subjects by amnestic and non-amnestic MCI.
| Characteristic | aMCI (N = 126) |
naMCI (N = 28) |
P |
|---|---|---|---|
| Age, years, median (IQR) | 82 (78, 86) | 84 (78, 87) | 0.66 |
| Male gender, no. (%) | 84 (67) | 19 (68) | 0.90 |
| Education, years, median (IQR) | 13 (12, 16) | 12 (12, 14) | 0.13 |
| APOE ε4 positive, no. (%) | 49 (40) | 6 (22) | 0.09 |
| MMSE, median (IQR) | 26 (24, 27) | 26 (24, 27) | 0.30 |
| CDR sum of boxes, median (IQR) | 1.0 (0.5, 1.5) | 0.8 (0.0, 1.5) | 0.61 |
| PIB Ratio, median (IQR) | 1.66 (1.36, 2.22) | 1.36 (1.32, 1.82) | 0.048 |
| PIB > 1.50, no. (%) | 72 (57) | 11 (39) | 0.09 |
| FDG Ratio, median (IQR) | 1.29 (1.18, 1.42) | 1.29 (1.19, 1.36) | 0.72 |
| FDG < 1.31, no. (%) | 68 (54) | 15 (54) | 0.97 |
| Adjusted Hippocampal Volume, median (IQR) |
−0.71 (−1.29, −0.29) | −0.22 (−0.60, 0.18) | <0.001 |
| HVa < 0.70, no. (%) | 64 (51) | 7 (25) | 0.013 |
| Biomarker Group | 0.28 | ||
| All biomarkers negative | 18 (14) | 7 (25) | |
| Amyloid positive only | 18 (14) | 4 (14) | |
| Amyloid positive & neurodegeneration |
54 (43) | 7 (25) | |
| Neurodegeneration only | 36 (29) | 10 (36) | |
| Diagnosis at follow-up, no. (%) | 0.79 | ||
| CN | 25 (26) | 6 (27) | |
| MCI | 57 (59) | 14 (64) | |
| Dementia | 14(15) | 2(9) | |
| Annual change in MMSE | |||
| N | 93 | 22 | |
| Median (IQR) | 0.00 (−1.58, 0.74) | −0.38 (−1.94, 0.00) | 0.46 |
| Annual change in CDR-SB | |||
| N | 96 | 22 | |
| Median (IQR) | 0.38 (0.00, 1.23) | 0.00 (−1.01, 0.40) | 0.016 |
Discussion
Our investigation of biomarkers in the MCSA MCI group is the first population-based study to assess the recently published MCI criteria with respect to the distribution of imaging biomarkers in MCI. The distribution of biomarker abnormalities was similar between the MCSA aMCI subjects and ADNI 1, even though the ADNI 1 subjects were selected to be more impaired at baseline as evidenced by the CDR scores. Although the number of subjects who progressed in both cohorts was small, the trends were very similar.
However, the neurodegeneration positive but amyloid negative group provides conflicting information for the model of the temporal progression of biomarkers in AD proposed by Jack et al. but may be very important. The model suggests that by the time of symtomatic impairment with MCI, both amyloid and neurodegeneration should be present. (39-41). While not statistically significant, this group had the highest rate of progression to dementia in the MCSA and was second highest in the ADNI cohort raising questions regarding the salience of amyloid. Neurodegeneration may be more important at predicting progression than amyloid, and other work by Landau et al. and Hiester et al. has suggested that neurodegenerative features such as hypometabolism on FDG PET and hippocampal atrophy are key in predicting progression5, 39. This group of subjects with MCI is similar to the “suspected non-AD pathway” (sNAP) subjects who were cognitively normal in the MCSA and could be designated as MCI-sNAP 38.
Subjects with an aMCI subtype may have AD biomarkers present more frequently than subjects with a naMCI subtype as suggested by their greater amyloid burden and more hippocampal atrophy. Although the most common clinical phenotype for AD pathophysiology is an amnestic presentation, certainly non-amnestic clinical profiles can occur, and this study highlights the expected heterogeneity of the MCI construct in the community It is also possible that naMCI subjects may represent prodromal stages of non-AD dementias 40-43. The ADNI subjects are uniquely selected and may not represent community MCI subjects (younger, more ApoE4 carriers, higher MMSE and CDR sum of boxes) but do simulate clinical trials populations. Both groups provide complementary data on the criteria.
It has been suggested that the amyloid levels increase to a maximum level and then plateau as one progresses along the putative continuum for AD pathophysiology proposed by Jack and colleagues 44. Our data partially support this model but also recognize some inconsistencies concerning the model since the neurodegenerative only group was prevalent and tended to progress to dementia. These findings are more consistent with the revised model proposed recently by Jack et al. suggesting that there may be other pathways for progression45. The amyloid positive only group in the MCSA aMCI had a median SUVR of 1.97 and the amyloid positive plus neurodegenerative biomarker group had an SUVR of 2.23 (p=0.06), and a similar trend was observed in the ADNI 1 subjects (1.78 vs. 2.24, p=0.30) supporting the concept of a progression from amyloid positivity to amyloid plus neurodegeneration. However, as discussed above this may not be the only path to progression.
Forty-three percent of the MCSA aMCI subjects had evidence for the presence of amyloid and neurodegeneration, while another 43% had no evidence of amyloid at the time of aMCI. Only 33% of ADNI 1 subjects were amyloid negative46. The high percent of MCI subjects who are amyloid positive implies that aMCI typically leads to dementia due to AD. However, the fact that not all aMCI are amyloid positive indicates that this is not always the case and argues for the use of biomarkers to stratify subjects at the MCI stage of the cognitive disorders spectrum especially for clinical trials. While the distributions were similar, more of the ADNI subjects had imaging evidence for the AD signature (amyloid plus neurodegeneration) than MCSA subjects, but the neurodegeneration alone was more prevalent in the MCSA subjects. This was probably a result of the requirement in ADNI 1 that MCI subjects have impaired memory and were more advanced; whereas in contrast in the MCSA, all MCI subjects were enrolled, again underscoring the importance of studying these biomarkers in the community.
In summary, this study suggests that the proposed addition of biomarkers to the clinical diagnosis of MCI is largely valid. The frequency of conflicting biomarkers, however, suggests the necessity of following these subjects. The final validation of the use of biomarkers will come from longitudinal studies, but the initial categorization of subjects with clinical MCI and a variety of biomarkers appears to be appropriate. When evaluating cognitively normal individuals, there are also many subjects who appear to be outside of the AD pathophysiological pathway (when defined to require biomarker evidence of amyloid deposition) as has been demonstrated by us previously, and now a corresponding group of subjects with MCI are here designated as MCI-sNAP is also recognized 38, 47. Subjects evaluated in a population-based study such as the MCSA are, by definition, more heterogeneous than those seen in AD or dementia clinics and, consequently, this factor needs to be considered when planning for clinical trials. However, given that these compounds will be used by typical community patients, these data are important.
Acknowledgments
This work was supported by grants from the National Institute on Aging: U01 AG006786, P50 AG016574, R01 AG034676, R01 AG011378, RO1 AG041851, and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer’s Disease Research Program. Data collection and sharing for this project was funded in part by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514.
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