Share

September 26, 2017; 89 (13) ArticleOpen Access

1H-MRS metabolites and rate of β-amyloid accumulation on serial PET in clinically normal adults

Zuzana Nedelska, Scott A. Przybelski, Timothy G. Lesnick, Christopher G. Schwarz, Val J. Lowe, Mary M. Machulda, Walter K. Kremers, Michelle M. Mielke, Rosebud O. Roberts, Bradley F. Boeve, David S. Knopman, Ronald C. Petersen, Clifford R. Jack, Kejal Kantarci
First published August 25, 2017, DOI: https://doi.org/10.1212/WNL.0000000000004421
Full PDF
Citation
Permissions

Make Comment

See Comments

Downloads
870

Share

Abstract

Objective: To assess whether noninvasive proton magnetic resonance spectroscopy (1H-MRS) tissue metabolite measurements at baseline can predict an increase in the rate of β-amyloid (Aβ) accumulation on serial PET in clinically normal (CN) older adults.

Methods: Consecutive participants aged 60 years and older (n = 594) from the Mayo Clinic Study of Aging who were CN at baseline and who underwent 1H-MRS from the posterior cingulate voxel and longitudinal 11C-Pittsburgh compound B (PiB)–PET were included. The rate of Aβ accumulation by serial cortical PiB standardized uptake value ratios was estimated as a function of baseline 1H-MRS metabolite ratios and time using mixed-effect models adjusted for age, sex, and APOE ε4. Effect of APOE ε4 on the relationship between baseline MRS and an increased rate of Aβ accumulation was also assessed.

Results: Among all participants, a higher myo-inositol (mI)/creatine (p = 0.011) and a lower N-acetylaspartate/mI (p = 0.006) at baseline were associated with an increased Aβ accumulation over time after adjusting for age, sex, and APOE ε4. APOE ε4 did not modify the association of baseline 1H-MRS metabolite ratios and rate of Aβ accumulation. However, APOE ε4 carriers accumulated Aβ faster than noncarriers regardless of the baseline Aβ load (p = 0.001).

Conclusion: Among CN older adults, early metabolic alterations on 1H-MRS and APOE ε4 status are independently associated with an increased rate of Aβ accumulation. Our findings could have important implications for early diagnosis and identification of individuals for secondary prevention trials, because an increased rate of Aβ accumulation in CN older adults may confer a higher risk for cognitive decline and mild cognitive impairment.

GLOSSARY

Aβ=
β-amyloid;
AD=
Alzheimer disease;
Cho=
choline;
CN=
clinically normal;
Cr=
creatine;
MCI=
mild cognitive impairment;
mI=
myo-inositol;
MPRAGE=
magnetization-prepared rapid acquisition gradient echo;
MRS=
magnetic resonance spectroscopy;
NAA=
N-acetylaspartate;
PC=
posterior cingulate;
PiB=
Pittsburgh compound B;
SUVR=
standardized uptake value ratio;
WM=
white matter

Twenty to forty percent of clinically normal (CN) older adults have significant β-amyloid (Aβ) load on cross-sectional PET.1,–,3 An increased rate of Aβ accumulation may put them at a higher risk for cognitive decline4 and mild cognitive impairment (MCI).5 Cost-effective and noninvasive biomarkers that can predict a further increase in Aβ accumulation over time are necessary for better identification of at-risk individuals who may benefit from preventive and disease-modifying strategies.

In CN older adults, elevated myo-inositol (mI), a marker of glial activation, has been associated with a higher Aβ load on PET,6,7 lower CSF Aβ1-42,7 and a higher Aβ density in an autopsy-confirmed cohort.8 Moreover, an elevated mI has been found in APOE ε4 carriers with no evidence of Aβ deposition on PET.7 Although the mechanistic relationship between APOE ε4 and elevated mI is unclear, it has been suggested that APOE ε4 enhances glial activation and modulates the relationship between Aβ and glial activation.9,–,11 However, the APOE ε4 effect on magnetic resonance spectroscopy (MRS) metabolite levels among CN elderly has been equivocal.7,9,12 Further, all of the studies examining MRS, Aβ-related pathology, and APOE ε4 were cross-sectional.

Our objective was to investigate the association of MRS metabolite ratios from a posterior cingulate (PC) gyrus voxel at baseline with the change in Aβ accumulation over time on serial amyloid PET in CN older adults drawn from a population-based sample. A secondary objective was to assess whether APOE ε4 modifies the relationship between MRS metabolites and the rate of Aβ accumulation.

METHODS

Participants.

Consecutive participants aged ≥60 years were drawn from the ongoing population-based, longitudinal Mayo Clinic Study of Aging,13,14 between January 2006 and May 2016. To be included in this imaging study, participants were required to be CN at baseline when MRS and amyloid PET were performed and have at least one follow-up amyloid PET. The diagnostic process and criteria for being clinically normal are described in appendix e-1 at Neurology.org. A total of 594 participants meeting inclusion criteria and passing image quality control were included in the analyses. The final cohort flowchart is provided as figure e-1.

Blood was collected to determine the APOE ε4 noncarrier or carrier status.

Change in Aβ accumulation over time was assessed using all available follow-up amyloid PET examinations on an individual. Consecutive participants had 1 (n = 416; 70%), 2 (n = 144; 24.6%), 3 (n = 29; 4.8%), or 4 (n = 5; 0.6%) follow-up PET scans performed approximately every 15 months.

Standard protocol approvals, registrations, and participant consents.

The Mayo Clinic and Olmsted Medical Center institutional review boards approved the study. Every participant provided written informed consent.

1H-MRS and MRI studies.

Baseline MRS and MRI were performed at 3T using an 8-channel phase array coil (GE Healthcare, Waukesha, WI). A 3D high-resolution T1-weighted magnetization-prepared rapid acquisition gradient echo (MPRAGE) scan was acquired for anatomic segmentation and region labeling of PET and localization of 1H-MRS voxel.

A point-resolved spectroscopic sequence was acquired with repetition time/echo time 2,000/30 ms with a single voxel of 2 × 2 × 2 cm3 placed in the midsagittal MPRAGE image including right and left PC gyri and inferior precunei. Although the transverse relaxation time of N-acetylaspartate (NAA) and choline (Cho) is longer than mI, a single short echo time of 30 ms allowed measurements of all 3 metabolites in participants within the Alzheimer disease (AD) continuum.15 Metabolites were quantified using the automated proton brain examination/single-voxel package, and their intensities were scaled by creatine (Cr), a standard reference. Individual voxel placement and magnetic resonance spectra were visually evaluated by a trained image analyst for quality control. A trained image analyst reviewed the location of the MRS voxel and evaluated water suppression, baseline distortions, or lipid contamination. Voxels that did not include the PC location according to the predetermined anatomic landmarks and spectra with poor water suppression, lipid contamination, or baseline distortions failed quality control and were excluded. Although the spectral fit was not measured quantitatively, the quantification of metabolite ratios failed if the spectral fit was poor.

PET studies.

11C-Pittsburgh compound B (PiB)–PET/CT images were acquired using a GE scanner (GE Healthcare). Participants were injected with the PiB tracer (average activity 625 MBq; range 385–723) and a low-dose CT scan was acquired for attenuation correction. Forty to sixty minutes postinjection, a 20-minute dynamic PET scan consisting of four 5-minute frames was acquired. These 4 frames were averaged to create a single statistical image.

Cortical Aβ retention for each PiB image was calculated as a global cortical standardized uptake value ratio (SUVR).16 For measuring change over time in PiB uptake, we used a previously published SUVR measurement technique, which was demonstrated to improve reliability and plausibility for serial measurements compared to traditional cross-sectional approaches.17 This technique uses a reference region of eroded supratentorial white matter (WM) segmented using MRI, combined with the whole cerebellum and pons. In brief, each PiB scan is rigidly registered to its corresponding T1-weighted MRI. Each MRI is segmented using SPM1218 with an in-house population-specific template and several population-specific measure alterations previously described.19 These segmentations are used for locating supratentorial WM for the reference region.

The target region and the cerebellar/pons regions included as part of the reference region were each localized using a corresponding in-house atlas19 that was nonlinearly registered to each corresponding MRI using the advanced normalization tools symmetric normalization algorithm,20 resampled using nearest-neighbor interpolation, and refined using the tissue segmentations described above. Automated registration and segmentation steps were each visually confirmed for acceptable quality. SUVRs were then calculated from PiB scans as the mean of all voxels in the target region normalized by the mean of all voxels in the reference region.

Statistical analysis.

We used mixed-effects models to model repeated PiB SUVR values as a function of baseline MRS metabolite ratios for all participants. We incorporated random slopes and intercepts for each participant (estimating the correlation between the slopes and intercepts). The primary predictor of interest involved an interaction of baseline MRS ratio with time, because we were interested in longitudinal change in Aβ accumulation. Models included the nested time and MRS ratio at baseline as is proper for an evaluation of interaction with time. A significant interaction of MRS ratio at baseline with time would indicate that the increase in the rate of Aβ accumulation on serial PET depends upon the value of the MRS ratio at baseline. A significant association between baseline MRS ratio and Aβ accumulation in the model would indicate that the MRS ratio was associated with consistently higher Aβ accumulation across all serial PET measurements in a given participant but not with an increased rate of accumulation. The models also accounted for effects of baseline age, sex, and APOE ε4 status. We computed the 3-way interactions of baseline MRS ratios and APOE ε4 status with time to assess whether APOE ε4 modifies the relationship between baseline metabolite MRS ratios and change over time in Aβ accumulation on serial PET.

Analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R statistical software version 3.1.1 (R-project.org) with 2-sided significance set at α 0.05 type I error rate. Because we were interested in the association of individual metabolite ratios with serial PiB measurements, and at this stage did not want to inflate type II error by declaring true associations null, we did not adjust for multiple comparisons.21,22

RESULTS

Participants' characteristics at baseline are listed in table 1. Although a higher mI/Cr at baseline was only borderline associated with a higher Aβ load across all serial PET of a given participant (p = 0.06), it is more important that it was significantly associated with a greater increase in rate of Aβ accumulation (p = 0.011), adjusting for baseline age, sex, APOE ε4, and the interaction between APOE ε4 and age (table 2). Similarly, a lower baseline NAA/mI was associated with consistently higher Aβ load across all serial PETs of a given participant (p = 0.007), but moreover, it was associated with an increased rate of Aβ accumulation (p = 0.006), adjusting for the same covariates (table 2). Figure 1 illustrates the estimated increase in rate of Aβ accumulation by baseline MRS ratios and by APOE ε4 with specific estimated values shift according to age, sex, and APOE ε4. Figure 2 shows the 3 individuals from the current cohort, their magnetic resonance spectra at baseline, and associated Aβ load at baseline and at follow-up.

View this table:
Table 1

Participants' (n = 594) characteristics at baseline

View this table:
Table 2

Estimated rate of β-amyloid (Aβ) accumulation by baseline myo-inositol (mI)/creatine (Cr) and N-acetylaspartate (NAA)/mI ratios

Figure 1
Figure 1 Estimated rate of β-amyloid (Aβ) accumulation on serial PET by baseline magnetic resonance spectroscopy (MRS) metabolite ratios and APOE ε4 status

The estimates for the rate of Aβ accumulation for a 75-year-old clinically normal man from the Mayo Clinic Study of Aging are shown. His rate of Aβ accumulation is estimated using an interaction between his baseline MRS metabolite ratios and time as primary predictor. The 1st and 3rd quartiles of MRS ratios are based on the models in table 2. (A) With a higher baseline myo-inositol (mI)/creatine (Cr) ratio, rate of Aβ accumulation increases more than it does with lower baseline mI/Cr ratio. Interaction of baseline MRS metabolites with time is visualized by gradually diverging slopes between quartiles. (B) A lower baseline N-acetylaspartate (NAA)/mI ratio is associated with an increase in rate of Aβ accumulation, a negative association represented by inverse order of quartiles. (C) The rate of Aβ accumulation is compared between a 75-year-old male APOE ε4 noncarrier and an APOE ε4 carrier of the same age and sex (table 3). The carrier slope is increasing and diverging from the noncarrier slope with time. However, note an obvious difference in baseline Aβ load between the APOE ε4 carrier and noncarrier, and because of this Aβ accumulation might be faster in the APOE ε4 carrier. (D) The difference in rate of Aβ accumulation is shown between the APOE ε4 carrier matched on age, sex, and baseline Aβ load to the noncarrier. The APOE ε4 carrier accumulates Aβ faster, regardless of baseline Aβ load. Note that the baseline Pittsburgh compound B (PiB) standardized uptake value ratio (SUVR) is derived using the serial PiB SUVR measurement approach.

Figure 2
Figure 2 Magnetic resonance spectra and amyloid PET at baseline and follow-up

These are 3 individuals from the Mayo Clinic Study of Aging. (A) A 76-year-old woman with high N-acetylaspartate (NAA)/myo-inositol (mI) ratio 3.25 and low mI/creatine (Cr) ratio 0.35 and minimal Aβ load (Pittsburgh compound B [PiB] standardized uptake value ratio [SUVR] 1.29) at baseline and minimal progression on follow-up 32 months later (PiB SUVR 1.30). (B) A 78-year-old man with a high mI/Cr ratio 0.63 and higher amyloid load at baseline (PiB SUVR 1.40) further shows a considerable progression on follow-up PET (PiB SUVR 1.82) 45 months later. (C) A 76-year-old woman with low NAA/mI ratio 2.74 with baseline PiB SUVR 1.35 shows progression on PET (PiB SUVR 1.58) 30 months later.

Mixed-effect models for the remaining metabolite ratios showed that a lower baseline NAA/Cr was associated with consistently higher Aβ load across all serial PET examinations in a given participant (p = 0.02; table e-1). However, NAA/Cr was not associated with an increased rate of Aβ accumulation (p = 0.18; figure e-2). Cho/Cr was associated neither with Aβ load across serial PET (p = 0.28) nor with rate of Aβ accumulation (p = 0.47; table e-1).

APOE ε4 was associated with consistently higher Aβ load across all serial PiB SUVR measurements (p < 0.001). Furthermore, the interaction of APOE ε4 with time (p < 0.001) was associated with an increased rate of Aβ accumulation, taking into account baseline age, sex, and time with baseline age interaction (table 3, model 1; figure 1). However, an accelerated rate of Aβ accumulation in APOE ε4 carriers compared to noncarriers may be because APOE ε4 carriers have higher baseline Aβ load.16 Nevertheless, when we compared rates of Aβ accumulation between a subset of our APOE ε4 carriers (n = 149) and noncarriers matched on age, sex, and baseline Aβ load to a subset of our noncarriers (n = 149), the interaction between APOE ε4 and time remained significant (p = 0.001; table 3, model 2) using mixed-effect model with a random block design to account for matching. Therefore, APOE ε4 carriers accumulated Aβ at an accelerated rate compared to APOE ε4 noncarriers even when they had a similar baseline Aβ load (figure 1).

View this table:
Table 3

Estimated rate of β-amyloid (Aβ) accumulation by APOE ε4 status

Finally, we assessed whether APOE ε4 status modified the relationship between the baseline metabolites and increased rate of Aβ accumulation using a 3-way interaction of baseline metabolites, APOE ε4, and time among all. None of these interactions was significant, including for mI/Cr × APOE ε4 × time (p = 0.35) and for NAA/mI × APOE ε4 × time (p = 0.90). Therefore, longitudinally, APOE ε4 status did not modify the relationship between MRS metabolites and rate of Aβ accumulation on serial PET.

DISCUSSION

In this large cohort of CN older adults, mean age of 74, drawn from a population-based sample, we demonstrated that noninvasive and inexpensive baseline MRS metabolite levels are associated with an increased rate of Aβ accumulation on serial PET. Lower NAA/mI and higher mI/Cr at baseline were associated with an increased rate of Aβ accumulation taking into account age at baseline, sex, and APOE ε4 status. APOE ε4 carriers accumulated Aβ faster than noncarriers. APOE ε4 status did not alter the relationship between baseline metabolite levels and rate of Aβ accumulation and both MRS metabolites and APOE ε4 likely are independently associated with an increased Aβ accumulation over time.

An elevated mI/Cr has been consistently associated with biomarkers of elevated Aβ in CN adults cross-sectionally.7,8,23,6 Moreover, in the transgenic murine AD model, a passive immunization with anti-Aβ antibodies lessened the mI/Cr in the treatment arm compared to placebo.24 In the current longitudinal cohort, we demonstrated findings that suggest an elevated mI/Cr as a predictor of an accelerated Aβ accumulation over time on serial PET.

NAA/Cr ratio, a marker of neuronal viability and synaptic integrity, is reduced in participants with MCI25 and AD,15,26,27 but not in CN older adults, suggesting that a decline in NAA/Cr is preceded by an increased mI/Cr during the course of AD. In line with this, an autopsy-confirmed study8 demonstrated an association between a lower NAA/Cr and a higher burden of tau-related pathology and loss of synaptic integrity that is believed to follow the changes in Aβ biomarkers during the course of AD.28 Accordingly, we observed that a lower NAA/Cr was associated with consistently higher Aβ load across all serial PET examinations of a given participant but not with an increased rate of Aβ accumulation. In addition to mI/Cr, significant association was observed between a lower baseline NAA/mI and an increased rate of Aβ accumulation. Lower NAA/mI has predicted progression from CN to MCI in a population-based cohort29 and only a lower NAA/mI among routinely examined MRS ratios correlated with both greater tau and Aβ burden at autopsy.8 The current study supports the composite NAA/mI ratio as a marker of increased longitudinal Aβ accumulation in CN older adults.

Although Cho/Cr has been associated with a higher Aβ load on PET7,29 and a worse cognitive performance in CN older adults,6,9 we did not observe an association of Cho/Cr and rate of Aβ accumulation. No association was found between Cho/Cr and AD-related pathology in an autopsy-confirmed cohort,8 and the significance of Cho/Cr during the progression of AD remains unclear.

APOE ε4 status did not modify the relationship between baseline MRS metabolites and rate of Aβ accumulation on serial PET. Instead, APOE ε4 was independently associated with an accelerated Aβ accumulation. Whereas the relationship between a higher Aβ load in APOE ε4 carriers has been well-established cross-sectionally,3,30,31 the effect of APOE ε4 status on longitudinal Aβ accumulation in CN older adults has not been clarified, likely due to small sample sizes, various PiB uptake measurement approaches, and different interpretation of contributing effects of baseline Aβ load, age, sex, and number of available follow-up PET scans.5,32,–,34 Moreover, APOE ε4 effect on longitudinal Aβ accumulation may be mediated by baseline Aβ load,16 which is higher in APOE ε4 carriers than noncarriers. Higher baseline Aβ load is a risk factor for increased Aβ accumulation over time.16 However, our finding of longitudinally accelerated Aβ accumulation in APOE ε4 carriers with similar baseline Aβ load to APOE ε4 noncarriers indicates that APOE ε4 carriers accumulate Aβ faster than noncarriers, regardless of baseline Aβ levels.

A cross-sectional study by Voevodskaya et al.7 demonstrated that already cognitively normal APOE ε4 carriers with still normal Aβ biomarker levels had elevated mI/Cr. It was suggested that mI/Cr may be an early biomarker of Aβ accumulation.35 Our findings support this hypothesis by showing that an elevated mI/Cr ratio in older adults is associated with an increased rate of Aβ accumulation. In addition, we showed that the relationship between MRS metabolite alterations and rates of Aβ accumulation is independent of APOE ε4 status. Taken together, these findings suggest that cross-sectional MRS metabolite alterations may occur in APOE ε4 carriers because of their risk of increased Aβ accumulation over time.

Higher baseline Aβ load increases the risk of cognitive decline over time in CN older adults.34,36 However, a recent cut point for Aβ positivity on PET in CN older adults37 was based on repeated measurements of Aβ accumulation. Using repeated measurements, the reliable worsening in Aβ accumulation was identified and served as cut point. Moreover, a few studies demonstrated that an increased rate of Aβ accumulation on PET is associated with cognitive decline over time in CN4 and progression to MCI.5 Therefore identifying those who accumulate Aβ faster over time provides additional and valuable information on at-risk individuals beyond cross-sectional measurements, which do not provide any information on disease progression. However, so far, the biomarkers that would identify such individuals have been scarce. Identifying those who accumulate Aβ faster can have important implications for early diagnosis and selecting at-risk individuals for secondary preventive interventions targeted to reduce Aβ accumulation rate. It is possible that interventions might be more effective in those who are still clinically normal but on the way to higher rates of Aβ accumulation. Our current findings suggest that both MRS metabolite alterations and APOE ε4 status independently are associated with accelerated rates of Aβ accumulation.

We did not dichotomize the participants as amyloid-positive or -negative by a cut point, although this popular approach may have practical advantages. Instead, we treated serial PiB SUVR as continuous measures that allowed us to include the CN participants with the full range of PiB SUVR values. Cut point may create a gray zone where subthreshold but important relationships might be obscured. For example, the biological difference between those participants who are close to the cut point, but arbitrarily fall into opposite groups, or a minor longitudinal change that moves a participant from one group to another by a given cut point might be negligible. On the contrary, a large difference in PiB SUVR among 2 participants who are in the same PiB group and a large change in PiB SUVR in a participant over time without a change in PiB group designation by cut point may be very meaningful. Finally, the cut point for amyloid positivity for longitudinal amyloid measurements remains to be established.

Strengths of this study are the large sample of individuals drawn from a single population with serial amyloid PET including a large subset of APOE ε4 carriers matched to noncarriers on demographics and baseline Aβ load. In this cohort, Aβ accumulation over time was measured using a modified reference region that has demonstrated an improved reliability and plausibility for serial measurements compared to traditional cross-sectional approaches.17

The limitations of our study are similar to those of other studies using participants drawn from a population-based sample, such as the presence of various subthreshold pathologies in CN older adults, which may increase the variability of MRS metabolite measurements and weaken some of the studied relationships. However, the levels of MRS ratios in the current cohort of CN older adults were consistent with previous studies by others and by our group,9,29,38 including an autopsy-confirmed study on MRS correlates of Aβ accumulation.8 Our proportion of CN APOE ε4 carriers (29%) was similar to previous reports.5,32,34 However, we cannot exclude the potential for participation or survival bias because more educated and generally healthier participants may be more willing to participate longitudinally in imaging studies,29 and our findings may not be entirely generalizable to other populations of CN adults. Finally, an inclusion of those ≥60 years old does not allow studying the relationship between rate of Aβ accumulation and MRS metabolites at an even earlier stage of AD pathophysiology. However, younger adults do not show sufficient increase in rate of Aβ accumulation to model longitudinal process. Increase in the rate of Aβ accumulation on serial PET in those younger than 60 is minimal33,39 and limited in capturing potential associations with baseline MRS metabolites.

MRS is a noninvasive and inexpensive technique that can be part of a standard clinical magnetic resonance examination, including in clinical trials. However, for these purposes, the standardization and optimization of multicenter MRS studies are necessary. Moreover, a longitudinal investigation of serial MRS metabolites to estimate progression of Aβ in participants within the AD continuum would provide additional information on the temporal ordering between the alterations in MRS metabolites and Aβ pathophysiology.

AUTHOR CONTRIBUTIONS

Dr. Nedelska: study concept and design, analysis and interpretation of the data, drafting the manuscript. S.A. Przybelski: design and analysis or interpretation of the data, critical revision of the manuscript for important intellectual content. T. Lesnick: design and analysis or interpretation of the data, drafting manuscript, critical revision of the manuscript for important intellectual content. Dr. Lowe: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Schwarz: data analysis, critical revision of the manuscript for important intellectual content. Dr. Machulda: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Mielke: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Roberts: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Kremers: analysis or interpretation of the data, critical revision of the manuscript for important intellectual content. Dr. Boeve: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Knopman: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Petersen: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Jack: acquisition of data, critical revision of the manuscript for important intellectual content. Dr. Kantarci: study concept and design, acquisition of data, interpretation of the data, drafting the manuscript, critical revision of the manuscript for important intellectual content.

STUDY FUNDING

NIH (R01-AG040042, R01-AG11378, P50-AG16574, U01-AG006786, AG034676, C06-RR018898), Gerald A. and Henrietta Rauenhorst Foundation, Mangurian Foundation, and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer's Disease Research Program.

DISCLOSURE

Z. Nedelska was supported by a joint Research Fellowship 2016 provided by the International Brain Research Organization and the International Society for Neurochemistry. S. Przybelski, T. Lesnick, and C. Schwarz report no disclosures relevant to the manuscript. V. Lowe is a consultant for Bayer Schering Pharma and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals, the NIH (National Institute on Aging [NIA], National Cancer Institute), the Elsie and Marvin Dekelboum Family Foundation, the Minnesota Partnership for Biotechnology and Medical Genomics, and the Leukemia & Lymphoma Society. M. Machulda is funded by the NIH. M. Mielke receives research support from the NIH/NIA and unrestricted research grants from Biogen. R. Roberts receives research support from the NIH/NIA and from Abbott Research Laboratories. W. Kremers is funded by the NIH and has received research funding from AstraZeneca and Biogen. B. Boeve has served as an investigator for clinical trials sponsored by GE Healthcare, FORUM Pharmaceuticals, C2N Diagnostics, and Axovant; receives royalties from the publication of Behavioral Neurology of Dementia (Cambridge Medicine, 2009); serves on the Scientific Advisory Board of the Tau Consortium; and receives research support from the NIH (U01-AG045390, U54-NS092089, P50-AG016574, U01-AG006786, R01-AG041797), the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program, and the Little Family Foundation. D. Knopman served as Deputy Editor for Neurology®, served on a Data Safety Monitoring Board for Lilly Pharmaceuticals, serves on a Data Safety Monitoring Board for Lundbeck Pharmaceuticals and for the DIAN study, served as a consultant to TauRx Pharmaceuticals ending in November 2012, was an investigator in clinical trials sponsored by Baxter and Elan Pharmaceuticals in the last 2 years, is currently an investigator in a clinical trial sponsored by TauRx, and receives research support from the NIH. R. Petersen serves on scientific advisory boards for Pfizer, Elan Pharmaceuticals, Wyeth Pharmaceuticals, and GE Healthcare; receives royalties from the publication of Mild Cognitive Impairment (Oxford University Press, 2003); and receives research support from the NIH/NIA (P50-AG16574 [PI] and U01-AG06786 [PI], R01-AG11378 [Co-I], and U01–24904 [Co-I]). C. Jack serves as a consultant for Janssen, Bristol-Meyer-Squibb, General Electric, and Johnson & Johnson; is involved in clinical trials sponsored by Allon and Baxter, Inc.; and receives research support from Pfizer, Inc., the NIA (AG11378 [PI], P50-AG16574 [Co-I], and U01-AG024904–01 [Co-I]), and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Foundation. K. Kantarci serves on the Data Safety Monitoring Board for Takeda Global Research & Development Center, Inc., and data monitoring boards of Pfizer and Janssen Alzheimer Immunotherapy, and is funded by the NIH (R01-AG040042 [PI], R21-NS066147 [PI], P50-AG44170/Project 2 [PI], P50-AG16574/Project 1 [PI], and R01-AG11378 [Co-I]). Go to Neurology.org for full disclosures.

Footnotes

  • Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article. The Article Processing Charge was funded by the Department of Radiology, Mayo Clinic Rochester, MN.

  • Supplemental data at Neurology.org

  • Received March 21, 2017.
  • Accepted in final form July 5, 2017.

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

REFERENCES

  1. 1.↵
    1. Jansen WJ,
    2. Ossenkoppele R,
    3. Knol DL, et al
    . Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 2015;313:1924–1938.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Jack CR Jr.,
    2. Wiste HJ,
    3. Weigand SD, et al
    . Age-specific population frequencies of cerebral beta-amyloidosis and neurodegeneration among people with normal cognitive function aged 50-89 years: a cross-sectional study. Lancet Neurol 2014;13:997–1005.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Rowe CC,
    2. Ellis KA,
    3. Rimajova M, et al
    . Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging 2010;31:1275–1283.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Leal SL,
    2. Landau SM,
    3. Bell RK,
    4. Jagust WJ
    . Hippocampal activation is associated with longitudinal amyloid accumulation and cognitive decline. Elife 2017;6:e22978.
    OpenUrl
  5. 5.↵
    1. Villemagne VL,
    2. Pike KE,
    3. Chetelat G, et al
    . Longitudinal assessment of abeta and cognition in aging and Alzheimer disease. Ann Neurol 2011;69:181–192.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Kantarci K,
    2. Lowe V,
    3. Przybelski SA, et al
    . Magnetic resonance spectroscopy, beta-amyloid load, and cognition in a population-based sample of cognitively normal older adults. Neurology 2011;77:951–958.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Voevodskaya O,
    2. Sundgren PC,
    3. Strandberg O, et al
    . Myo-inositol changes precede amyloid pathology and relate to APOE genotype in Alzheimer disease. Neurology 2016;86:1754–1761.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Murray ME,
    2. Przybelski SA,
    3. Lesnick TG, et al
    . Early Alzheimer's disease neuropathology detected by proton MR spectroscopy. J Neurosci 2014;34:16247–16255.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Gomar JJ,
    2. Gordon ML,
    3. Dickinson D, et al
    . APOE genotype modulates proton magnetic resonance spectroscopy metabolites in the aging brain. Biol Psychiatry 2014;75:686–692.
    OpenUrlPubMed
  10. 10.↵
    1. Ringman JM,
    2. Elashoff D,
    3. Geschwind DH, et al
    . Plasma signaling proteins in persons at genetic risk for Alzheimer disease: influence of APOE genotype. Arch Neurol 2012;69:757–764.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Gispert JD,
    2. Monte GC,
    3. Suarez-Calvet M, et al
    . The APOE epsilon4 genotype modulates CSF YKL-40 levels and their structural brain correlates in the continuum of Alzheimer's disease but not those of sTREM2. Alzheimers Dement 2017;6:50–59.
    OpenUrl
  12. 12.↵
    1. Kantarci K,
    2. Smith GE,
    3. Ivnik RJ, et al
    . 1H magnetic resonance spectroscopy, cognitive function, and apolipoprotein E genotype in normal aging, mild cognitive impairment and Alzheimer's disease. J Int Neuropsychol Soc 2002;8:934–942.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Roberts RO,
    2. Geda YE,
    3. Knopman DS, et al
    . The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology 2008;30:58–69.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Petersen RC,
    2. Roberts RO,
    3. Knopman DS, et al
    . Prevalence of mild cognitive impairment is higher in men: The Mayo Clinic Study of Aging. Neurology 2010;75:889–897.
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    1. Kantarci K,
    2. Jack CR Jr.,
    3. Xu YC, et al
    . Regional metabolic patterns in mild cognitive impairment and Alzheimer's disease: a 1H MRS study. Neurology 2000;55:210–217.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Jack CR Jr.,
    2. Wiste HJ,
    3. Lesnick TG, et al
    . Brain beta-amyloid load approaches a plateau. Neurology 2013;80:890–896.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. Schwarz CG,
    2. Senjem ML,
    3. Gunter JL, et al
    . Optimizing PiB-PET SUVR change-over-time measurement by a large-scale analysis of longitudinal reliability, plausibility, separability, and correlation with MMSE. Neuroimage 2016;144:113–127.
    OpenUrl
  18. 18.↵
    1. Ashburner J,
    2. Friston KJ
    . Unified segmentation. Neuroimage 2005;26:839–851.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Schwarz CG,
    2. Gunter JL,
    3. Wiste HJ, et al
    . A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. Neuroimage Clin 2016;11:802–812.
    OpenUrl
  20. 20.↵
    1. Avants BB,
    2. Epstein CL,
    3. Grossman M,
    4. Gee JC
    . Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008;12:26–41.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Rothman KJ
    . No adjustments are needed for multiple comparisons. Epidemiology 1990;1:43–46.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Perneger TV
    . What's wrong with Bonferroni adjustments? BMJ 1998;316:1236–1238.
    OpenUrlFREE Full Text
  23. 23.↵
    1. Kantarci K,
    2. Knopman DS,
    3. Dickson DW, et al
    . Alzheimer disease: postmortem neuropathologic correlates of antemortem 1H MR spectroscopy metabolite measurements. Radiology 2008;248:210–220.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Marjanska M,
    2. Weigand SD,
    3. Preboske G, et al
    . Treatment effects in a transgenic mouse model of Alzheimer's disease: a magnetic resonance spectroscopy study after passive immunization. Neuroscience 2014;259:94–100.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Kantarci K
    . Proton MRS in mild cognitive impairment. J Magn Reson Imaging 2013;37:770–777.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Catani M,
    2. Cherubini A,
    3. Howard R, et al
    . (1)H-MR spectroscopy differentiates mild cognitive impairment from normal brain aging. Neuroreport 2001;12:2315–2317.
    OpenUrlCrossRefPubMed
  27. 27.↵
    1. Huang W,
    2. Alexander GE,
    3. Chang L, et al
    . Brain metabolite concentration and dementia severity in Alzheimer's disease: a (1)H MRS study. Neurology 2001;57:626–632.
    OpenUrlAbstract/FREE Full Text
  28. 28.↵
    1. Jack CR Jr.,
    2. Knopman DS,
    3. Jagust WJ, et al
    . Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013;12:207–216.
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. Kantarci K,
    2. Weigand SD,
    3. Przybelski SA, et al
    . MRI and MRS predictors of mild cognitive impairment in a population-based sample. Neurology 2013;81:126–133.
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    1. Morris JC,
    2. Roe CM,
    3. Xiong C, et al
    . APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Ann Neurol 2010;67:122–131.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Mielke MM,
    2. Wiste HJ,
    3. Weigand SD, et al
    . Indicators of amyloid burden in a population-based study of cognitively normal elderly. Neurology 2012;79:1570–1577.
    OpenUrlAbstract/FREE Full Text
  32. 32.↵
    1. Resnick SM,
    2. Bilgel M,
    3. Moghekar A, et al
    . Changes in Abeta biomarkers and associations with APOE genotype in 2 longitudinal cohorts. Neurobiol Aging 2015;36:2333–2339.
    OpenUrl
  33. 33.↵
    1. Vlassenko AG,
    2. Mintun MA,
    3. Xiong C, et al
    . Amyloid-beta plaque growth in cognitively normal adults: longitudinal [11C]Pittsburgh compound B data. Ann Neurol 2011;70:857–861.
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. Petersen RC,
    2. Wiste HJ,
    3. Weigand SD, et al
    . Association of elevated amyloid levels with cognition and biomarkers in cognitively normal people from the community. JAMA Neurol 2016;73:85–92.
    OpenUrl
  35. 35.↵
    1. Kantarci K,
    2. Goldberg TE
    . MR spectroscopy, APOE genotype, and evolving beta-amyloid pathology: what is being detected and when. Neurology 2016;86:1750–1751.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Vemuri P,
    2. Lesnick TG,
    3. Przybelski SA, et al
    . Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain 2015;138:761–771.
    OpenUrlCrossRefPubMed
  37. 37.↵
    1. Jack CR Jr.,
    2. Wiste HJ,
    3. Weigand SD, et al
    . Defining imaging biomarker cut points for brain aging and Alzheimer's disease. Alzheimers Dement 2016;13:205–216.
    OpenUrl
  38. 38.↵
    1. Kantarci K,
    2. Petersen RC,
    3. Boeve BF, et al
    . 1H MR spectroscopy in common dementias. Neurology 2004;63:1393–1398.
    OpenUrlAbstract/FREE Full Text
  39. 39.↵
    1. Rodrigue KM,
    2. Kennedy KM,
    3. Devous MD Sr., et al
    . Beta-amyloid burden in healthy aging: regional distribution and cognitive consequences. Neurology 2012;78:387–395.
    OpenUrlAbstract/FREE Full Text
  40. 40.
    1. Kokmen E,
    2. Smith GE,
    3. Petersen RC,
    4. Tangalos E,
    5. Ivnik RC
    . The short test of mental status: correlations with standardized psychometric testing. Arch Neurol 1991;48:725–728.
    OpenUrlCrossRefPubMed

Letters: Rapid online correspondence

No comments have been published for this article.
Comment

REQUIREMENTS

You must ensure that your Disclosures have been updated within the previous six months. Please go to our Submission Site to add or update your Disclosure information.

Your co-authors must send a completed Publishing Agreement Form to Neurology Staff (not necessary for the lead/corresponding author as the form below will suffice) before you upload your comment.

If you are responding to a comment that was written about an article you originally authored:
You (and co-authors) do not need to fill out forms or check disclosures as author forms are still valid
and apply to letter.

Submission specifications:

  • Submissions must be < 200 words with < 5 references. Reference 1 must be the article on which you are commenting.
  • Submissions should not have more than 5 authors. (Exception: original author replies can include all original authors of the article)
  • Submit only on articles published within 6 months of issue date.
  • Do not be redundant. Read any comments already posted on the article prior to submission.
  • Submitted comments are subject to editing and editor review prior to posting.

More guidelines and information on Disputes & Debates

Compose Comment

More information about text formats

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Author Information
NOTE: The first author must also be the corresponding author of the comment.
First or given name, e.g. 'Peter'.
Your last, or family, name, e.g. 'MacMoody'.
Your email address, e.g. [email protected]
Your role and/or occupation, e.g. 'Orthopedic Surgeon'.
Your organization or institution (if applicable), e.g. 'Royal Free Hospital'.
Publishing Agreement
NOTE: All authors, besides the first/corresponding author, must complete a separate Publishing Agreement Form and provide via email to the editorial office before comments can be posted.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Vertical Tabs

You May Also be Interested in

Back to top
Advertisement

Topics Discussed

Alert Me

Recommended articles