New Results

White matter hyperintensities may be an early marker for age-related cognitive decline in the ADNI cohort

Cassandra Morrison, Mahsa Dadar, Sylvia Villeneuve, D. Louis Collins, for Alzheimer’s Disease Neuroimaging Initiative
doi: https://doi.org/10.1101/2021.09.23.461560

Abstract

Background Previous research suggests that white matter hyperintensities, amyloid, and tau contribute to age-related cognitive decline. It remains unknown as to how these factors relate to one another and how they jointly contribute to cognitive decline in normal aging. This project examines the association between these pathologies and their relationship to cognitive decline.

Methods Cognitively normal older adult data from the Alzheimer’s Disease Neuroimaging Initiative were examined. Participants were included if they had no subjective cognitive decline, had baseline white matter hyperintensity, CSF Aß42/40, CSF pTau181, and cognitive scores. Of the 102 participants included, only 79 had follow-up cognitive scores. Linear regressions examined the influence of white matter hyperintensities, amyloid, and tau on baseline and follow-up cognitive scores. Linear regressions also examined the association of amyloid and tau on white matter hyperintensities and between tau and amyloid.

Results Increased white matter hyperintensity load was associated with lower baseline memory (ß= -0.20, p =.046), follow-up executive functioning (ß= -0.32, p<.001), and follow-up ADAS-13 (ß=2.69, p<.001) scores. White matter hyperintensities were not related to pTau or Aß42/40. Lower Aß42/40 was associated with increased pTau (p=.025). pTau was not associated with decline in any cognitive score. Lower Aß42/40 was associated with lower baseline (p=.015) but not follow-up executive function.

Discussion White matter hyperintensities may be one of the earliest pathologies observed in healthy older adults that contribute to cognitive decline. The inclusion of white matter hyperintensities as an additional marker for early cognitive decline may improve our current understanding of age-related changes.

Introduction

Alzheimer’s disease (AD) is characterized by several neuropathological brain changes such as senile plaques (ß-amyloid, Aß) and neurofibrillary tangles (Tau) deposition1,2. These accumulations have been associated with the hallmarked progressive cognitive decline observed in people with AD. Both CSF Aß and tau pathologies3–5 and position emission tomography (PET) Aß and tau6–8 have been associated with cognitive decline in healthy older adults. Tau pathology markers have been observed to be better predictors of cognitive decline than amyloid6,9.

Another pathological change, cerebral small vessel disease, results in structural damage to the white matter of the brain that is often observed as white matter hyperintensities (WMH) in T2w or FLAIR MRI in both non-clinical healthy aging populations and individuals with cognitive decline10. An association between high WMH load and decreased cognitive functioning in healthy older adults is often reported. A meta-analysis observed that WMH impacts all cognitive domains in healthy older adults, with the strongest association seen between WMH and attention and executive functioning11. WMH load increases a healthy older adults’ risk for future development of mild cognitive impairment (MCI)12 and dementia13. WMH also continues to contribute to cognitive decline observed in AD14 because many cases of AD have a mixed etiology with cerebral small vessel disease15. WMH has been observed to precede neurodegeneration and cognitive decline in MCI, AD, and Parkinson’s disease16,17, suggesting that WMH may be involved with the etiology of cognitive decline in normal aging and cognitive impairment due to MCI and Alzheimer’s disease (AD).

While many studies have examined the effects of CSF tau and amyloid on cognitive functioning3–5 and the relationship between WMH and amyloid16,18,19 there is limited research examining the interaction between WMH, amyloid, and tau. One study found that increases in WMH were associated with increases in amyloid-PET in cognitively healthy older adults, but no relationship between tau-PET and WMH20. However, this study did examine the relationship between these pathologies and cognitive decline. In contrast, another study observed no association between WMH burden and amyloid-PET21. This study also observed that increased WMH was associated with lower executive functioning whereas amyloid measured with PET had no effect on cognitive function21.

One difficulty when studying these AD-related pathologies is that they occur years prior to the onset of clinical symptoms1,22, emphasizing the need to evaluate these changes in the healthy aging population. The goal of this study was thus to examine the relationship between WMH, amyloid, and tau and their association with cognitive functioning in healthy older adults. The first aim was to explore how these pathologies are related to one another and whether they impact each other’s progression. Furthermore, we sought to examine how these AD-related pathologies are associated with current cognitive function and cognitive decline in healthy older adults.

Methods

Alzheimer’s Disease Neuroimaging Initiative

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. Participants were between 55 and 90 years old at the time of recruitment. The study received ethical approval from the review boards of all participating institutions. Written informed consent was obtained from participants or their study partner.

Participants

The purpose of this paper was to examine healthy cognitive aging. Including people with subjective cognitive decline (SCD) may bias the associations between the pathologies being observed. Therefore people with SCD were excluded to avoid possible confounds of normal controls that may already be on the AD trajectory23.

ADNI-1 and ADNI-GO did not measure subjective cognitive decline; therefore, we applied the following study inclusion and exclusion criteria to the 406 cognitively normal controls with no SCD from the ADNI-2 and ADNI-3 cohorts with MRI scans. Full participant inclusion/exclusion criteria can be downloaded from www.adni-info.org. Briefly, Participants were between 55 and 90 years old at the time of recruitment and had no evidence of cognitive decline on either the Mini Mental Status Examination or Clinical Dementia Rating. Cognitively normal older adults were excluded if they had scored >16 on the Cognitive Change Index, signifying SCD. Inclusion criteria included having a PD+T2w or FLAIR MRI scan from which WMH load could be estimated and having both tau and amyloid measures. Inclusion criteria also included baseline executive functioning, memory, and ADAS-13 scores. These criteria resulted in 102 participants for our study at baseline. At follow-up, 79 of these participants had cognitive scores. Figure 1 summarizes the methodology used to select participants. This project is a cohort study design that followed healthy older adults over a period of time.

Figure 1:
Figure 1:

Flowchart summarizing the participant inclusion and exclusion criteria based on WMH, amyloid, tau, and cognitive score measurements.

We used executive functioning (ADNI-EF24) and memory (ADNI-MEM25) composite scores that have been previously developed and validated in ADNI. Alzheimer’s Disease Assessment Scale-Cog13 (ADAS-1326) scores were also included for all participants. The ADAS-13 measures severity of cognitive decline, for a total of 85 points, with higher scores indicating greater severity. These scores were downloaded from the ADNI public website.

To obtain cerebrospinal fluid (CSF) samples, lumbar punctions were performed as described in the ADNI procedures manual. CSF Aß42/40 ratio and phosphorylated p-Tau181 (pTau) were measured using the multiplex xMAP Luminex platform (Luminex Corp, Austin, TX, USA) with the INNO-BIA AlzBio3 kit (Innogenetics)27,28.

Structural MRI acquisition and processing

All participants were imaged using a 3T scanner with T1-weighted imaging parameters (see http://adni.loni.usc.edu/methods/mri-tool/mri-analysis/ for the detailed MRI acquisition protocol). Baseline scans were downloaded from ADNI.

T1w scans for each participant were pre-processed through our standard pipeline including noise reduction29, intensity inhomogeneity correction30, and intensity normalization into range [0-100]. The pre-processed images were then both linearly (9 parameters: 3 translation, 3 rotation, and 3 scaling)31 and nonlinearly (1 mm3 grid)32 registered to the MNI-ICBM152-2009c average template33. The quality of the linear and nonlinear registrations was visually verified by an experienced rater.

WMH measurements

WMH measurements were obtained through a previously validated WMH segmentation technique and a library of manual segmentations based on 50 ADNI participants (independent of the 102 studied here). WMHs were automatically segmented at baseline using the T1w contrasts, along with a set of location and intensity features obtained from a library of manually segmented scans in combination with a random forest classifier to detect the WMHs in new images34,35.

WMH load was used as a proxy for cerebrovascular pathology and was defined as the volume of all voxels identified as WMH in the standard space (in mm3) and are thus normalized for head size. WMH volumes were log-transformed to achieve normal distribution.

Data availability statement

The data used for this analysis are available on request from the ADNI database (ida.loni.usc.edu).

Statistical Analysis

Analyses were performed using MATLAB R2019b. Participant demographic information is presented in Table 1. Linear regression models were conducted to examine whether WMH, amyloid, and tau would influence cognitive scores. CognitiveScore_bl represents executive function (or memory composite) score at baseline. For baseline measurements, 102 participants were included for both the executive functioning and memory composite models. Both CSF Aß42/40 and pTau levels were downloaded from ADNI.

View this table:
Table 1:

Descriptive statistics for the participants included in this study. Data are number (N) or mean ± standard deviation.

Embedded Image For follow-up measurements, 79 participants had follow-up scores over periods 0-1 year (n=23), 1-2 years (n=44), and 2-3 years (n=12) that were included for both the executive functioning and memory composite models. Baseline cognitive scores were also included in the follow-up model to ensure that the follow-up results account for more variance than what can be explained by the baseline scores. Embedded Image A linear regression was completed to examine whether amyloid and tau influence white matter hyperintensities. All 102 participants were included in this analysis: Embedded Image A linear regression was also completed to examine whether tau and amyloid are associated. All 102 participants were included in this analysis: Embedded Image A descriptive analysis was completed to examine the number of participants who had elevated pTau and Aß42/40 levels. A threshold of Aß42/40 of <0.075 was employed based on previously research as the optimal cut-off for amyloid positivity36. The pTau threshold was based on previous research that determined the optimal threshold, in the ADNI dataset, for distinguishing between Aß negative and Aß positive cognitively normal older adults is 26.64 pg/ml37.

Results

Cognitive Score Analysis

Figures 2 and 3 display executive functioning composite, memory composite, and ADAS-13 scores with Aß42/40, pTau, and WMH at baseline and follow-up. Table 2 summarizes the regression model results. At baseline, increased scores in executive functioning were associated with higher CSF Aß42/40 (t= 2.48, p=.015) and higher education (t= 2.26, p=.026), and lower scores on executive functioning were associated with increased age (t= -2.42, p=.017). At follow-up, increased executive functioning scores were associated with increased education (t= 3.62, p<.001) and increased baseline executive functioning (t= 7.61, p<.001). At follow-up, lower executive functioning scores were associated with increased WMHs (t= -2.90, p=.004).

View this table:
Table 2:

Regression model outputs.

Figure 2:
Figure 2:

Baseline cognitive scores and their relationship with amyloid, pTau and WMH load.

Figures show executive functioning (first row), memory (second row) and ADAS-13 (third row) scores and their association with Abeta Ratio (first column), P Tau (Second column), and WMH Load (third column). Abbreviations: EF, executive functioning; MEM, memory; ADAS, Alzheimer’s Disease Assessment Scale, WMH, white matter hyperintensity.

Figure 3:
Figure 3:

Follow-up cognitive scores and their relationship with amyloid, pTau and WMH load.

Figures show executive functioning (first row), memory (second row) and ADAS-13 (third row) scores and their association with Abeta Ratio (first column), pTau (Second column), and WMH Load (third column). Abbreviations: EF, executive functioning; MEM, memory; ADAS, Alzheimer’s Disease Assessment Scale, WMH, white matter hyperintensity.

At baseline, increased memory was associated with higher education (t= 2.38, p=.019) and lower memory scores were associated with increased WMHs (t= -2.02, p=.046) and male sex (t= -3.49, p<.001). At follow-up, increased memory was only associated with higher education (t= 2.02, p=.047) and higher baseline memory (t= 11.13, p<.001).

At baseline, increases in ADAS-13 scores were only positively associated with male sex, (t= 2.25, p=.027). At follow-up, lower ADAS-13 scores were associated with age (t= -3.61, p<.001) whereas higher ADAS-13 scores were associated with increased WMHs (t= 4.10, p<.001) and higher baseline ADAS score (t= 9.12, p<.001).

WMH, Tau, and Amyloid Analysis

At baseline, WMH were not associated with either pTau (t=-1.19, p=0.24) or amyloid burden (t<1, p=0.48), however significant increased WMHs were associated with increased age (t= 4.62, p<.001). Decreases in amyloid was observed to be associated with increases in tau (t= -2.28, p=.025).

Tau and amyloid accumulation

At baseline, 48/102 participants had amyloid levels below the threshold of 0.075 and only 22/102 participants had elevated pTau levels above 26.64 pg/ml. Figure 5 presents individual values, means, and confidence intervals for the Aß42/40 and pTau levels.

Figure 4:
Figure 4:

Baseline measurements of white matter hyperintensity load, amyloid, and pTau.

From left to right, WMH association with Abeta Ratio, WMH association with pTau, and pTau association with Abeta Ratio. Abbreviations: WMH, white matter hyperintensity.

Figure 5:
Figure 5:

Pirateplots of participant Abeta ratio and pTau values.

Figures present the mean values (thick, solid horizontal line), 95% confidence intervals (light horizontal box), smooth frequency distribution (shaded area), and individual data points (jittered).

Discussion

The current study investigated the effects of WMH, amyloid, and tau on current and future cognitive decline in cognitively normal older adults. We observed that CSF pTau was not associated with current or future decline for either memory or executive function in this cohort. CSF Aß burden measured with the 42/40 ratio was associated with only decreased baseline executive functioning. Increased WMH loads were associated with reduced performance on follow-up executive function and baseline memory. Increased WMH loads were also associated with reduced ADAS-13 follow-up scores. WMH loads were not related to either tau or amyloid, whereas decreases in Aß42/40 was associated with increases in pTau. These results suggest WMH may contribute to the initial stages of cognitive decline in healthy older adults.

In this sample of cognitively unimpaired participants with no subjective cognitive decline, WMH loads appeared to have the largest association with future cognitive decline in cognitively normal older adults. Vascular deterioration, as measured by WMHs, may be one of the first pathologies observed in the healthy older adult population that affects cognitive functioning. Pathologies that occur later in the aging process (i.e., tau and amyloid accumulation) may require less deposition to result in deterioration in cognitive functioning in those with high WMH loads. Reducing WMH may thus reduce the risk of future cognitive decline and the impact of other pathologies. The observation that future cognitive decline is impacted in healthy aging by WMH supports previous findings suggesting that WMHs increase the risk of MCI and dementia15 by lowering the threshold for cognitive decline due to AD. Risk factors that contribute to WMHs (e.g., hypertension and cardiovascular disease) are also associated with cognitive decline in healthy older adults21,38 and a higher conversion rate from MCI to AD39. Taken together, these findings provide support for the need for aggressive treatments of vascular factors to help reduce future cognitive decline. For example, previous research has observed that people who undergo antihypertensive treatment exhibit less cognitive decline40 and a reduced risk of dementia41 than those with untreated high blood pressure.

In MCI and AD, both PET and CSF tau and amyloid deposition is reported to be a strong predictor of cognitive decline42–44. It is well-known that this accumulation of pathological amyloid and tau begins years before deficits in cognitive functioning can be detected45, and thus may occur during healthy aging. The relationship between tau and amyloid in healthy older adults is, however, less prominent. Associations between CSF tau and amyloid3–5 with reduced cognitive performance in healthy older adults have been observed. In our study, we observed that a decrease in CSF Aß42/40 was associated with increases in pTau. Lower Aß42/40 is indicative of more brain amyloid, thus lower CSF Aß42/40 is associated with increased burden measured by pTau. Neither CSF pTau nor amyloid was associated with cognitive decline in healthy older adults with no subjective cognitive decline. The sample of participants in this study comprises healthy older adults who exhibit low overall pTau burdens46 making it difficult to detect associations between tau and cognition at this stage. Amyloid deposition may occur on average 13.3 years earlier than tau accumulation47. Thus, it is not surprising that more participants had elevated amyloid burden in the absence of elevated tau. Both elevated amyloid and tau deposition may be necessary for measurable memory decline in the preclinical phases of AD7. Therefore, despite the “somewhat” elevated amyloid burden, the lack of elevated tau may have inhibited our ability to observe measurable associations with tau and amyloid in our healthy older adult sample.

The low amyloid and tau values also make it difficult to determine whether these individuals will remain healthy older adults or will progress to MCI/AD or another dementia. In the current study, we only have 2-3yrs of follow-up data so we cannot examine differences in people who develop more advanced accumulation. Future research should examine longer follow-ups to determine whether cognitively normal healthy older adults with WMHs have a faster rate of decline once elevated amyloid and tau contribute to cognitive decline. In this study, we did not observe a relationship between WMH load and either tau or amyloid. Graff-Radford et al. reported no associations between tau-PET and WMH burden, however, they reported that amyloid-PET load correlated with WMH (2019). One explanation for the current study not observing the correlation between WMH and amyloid is related to the low levels of amyloid burden in our healthy controls. The lack of association between WMH and amyloid is consistent with other research21. Future research should further explore these inconsistent results in the healthy aging population.

The limitations of the current study include the inability to examine the influence of APOE status, limited follow-up, limited sample size, and high participant education. Only 28 participants had APOE ε4 positivity (24 with one ε4 allele and 4 with two ε4 alleles). Since APOE ε4 positivity increases the risk of future AD23, future research would benefit from replicating this study in a sample comparing more participants with APOE ε4 positivity. Our follow-up times were limited to 3 years because we included participants from ADNI-3. Longer follow-ups may demonstrate stronger associations between tau and amyloid pathologies and cognitive decline. The relatively limited sample in this study did not allow for the testing of the interaction between amyloid, tau, and WMH on cognition with enough statistical power. The high education of this sample may limit the generalizability of these results to other populations.

The image processing methods employed in this study have been developed and extensively validated for use in multi-center and multi-scanner studies. These processing methods have previously observed WMH effects in PD16,48, healthy aging, and AD16. Therefore, the lack of association between WMH with amyloid, tau, and some cognitive scores is not the result of poor sensitivity of the image processing methods employed.

Conclusion

The current study sought to examine the relationship between WMHs, amyloid, and tau measured with CSF and their effects on cognitive functioning in healthy older adults. Our findings suggest that in healthy older adults, WMHs may be one of the first pathologies to contribute to age-related cognitive decline. It is possible that WMH burden may preceded tau and amyloid deposition, however, without being able to determine who converts to AD, this hypothesis needs to be further examined. Despite not being able to determine which participants will eventually develop dementia in this study, our findings that WMHs contribute to cognitive decline aligns with previous work suggesting that vascular biomarkers may improve our current understanding of preclinical AD if incorporated into the AD research framework49.

Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. 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 Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

MD is supported by a scholarship from the Canadian Consortium on Neurodegeneration in Aging as well as an Alzheimer Society Research Program (ASRP) postdoctoral award. The Consortium is supported by a grant from the Canadian 24 Institutes of Health Research with funding from several partners including the Alzheimer Society of Canada, Sanofi, and Women’s Brain Health Initiative.

Footnotes

  • ↵‡ Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.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. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

  • Funding information Alzheimer’s Disease Neuroimaging Initiative; This research was supported by a grant from the Canadian Institutes of Health Research and The Louise & André Charron Family.

  • Financial Disclosures Dr. Morrison is supported by a Canadian Institute of Health Research Postdoctoral Fellowship Funding Reference Number: MFE-176608.

    Dr. Dadar is supported by a scholarship from the Canadian Consortium on Neurodegeneration in Aging as well as an Alzheimer Society Research Program (ASRP) postdoctoral award. The Consortium is supported by a grant from the Canadian 24 Institutes of Health Research with funding from several partners including the Alzheimer Society of Canada, Sanofi, and Women’s Brain Health Initiative.

    Dr. Villeneuve reports work supported by a Canada Research Chair, a Canadian Institutes of Health Research Foundation Grant, a Canada Fund for Innovation Grant, an Alzheimer’s Association Grant, and an Alzheimer’s society of Canada and Fonds de recherche Sante Quebec fellowship.

    Dr. Collins reports receiving research funding from Canadian Institutes of Health research, the Canadian National Science and Engineering Research Council, Brain Canada, the Weston Foundation, and the Famille Louise & André Charron.

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Posted September 24, 2021.
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White matter hyperintensities may be an early marker for age-related cognitive decline in the ADNI cohort
Cassandra Morrison, Mahsa Dadar, Sylvia Villeneuve, D. Louis Collins, for Alzheimer’s Disease Neuroimaging Initiative
bioRxiv 2021.09.23.461560; doi: https://doi.org/10.1101/2021.09.23.461560
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