Abstract
Introduction
Motoric cognitive risk (MCR) and amnestic mild cognitive impairment (aMCI) syndromes are each reliable predictors of incident Alzheimer’s disease (AD), but MCR may be a stronger predictor of vascular dementia than AD. This study contrasted cortical and hippocampal atrophy patterns in MCR and aMCI.
Methods
Cross-sectional data from 733 older adults without dementia or disability (M age = 73.6; 45% women) in the multicountry MCR consortium were examined. MCR was defined as presence of slow gait and cognitive concerns. Amnestic MCI was defined as poor episodic memory performance and cognitive concerns. Cortical thickness and hippocampal volumes were quantified from structural MRIs. Multivariate and univariate general linear models were used to examine associations between cortical thickness and hippocampal volume in MCR and aMCI, adjusting for age, sex, education, total intracranial volume, white matter lesions, and study site.
Results
The prevalence of MCR and aMCI was 7.64% and 12.96%, respectively. MCR was associated with widespread cortical atrophy, including prefrontal, insular, cingulate, motor, parietal, and temporal atrophy. aMCI was associated with hippocampal atrophy.
Conclusion
Distinct patterns of atrophy were associated with MCR and aMCI. A distributed pattern of cortical atrophy – that is more consistent with VaD or mixed dementia– was observed in MCR. A more restricted pattern of atrophy – that is more consistent with AD – was observed in aMCI. The biological underpinnings of MCR and aMCI likely differ and may require tailored interventions.
Keywords: Motoric cognitive risk, Amnestic mild cognitive impairment, Cortical thickness, Hippocampal volume
Introduction
Alzheimer’s disease (AD) and related dementia (e.g., vascular dementia [VaD]) are neurodegenerative diseases – without a known cure – characterized by cognitive and functional impairment and disability. More than 6 million older adults in the USA currently have AD or related dementia, and by 2050 that number is expected to be 14 million [1]. Early identification of those at an increased risk for dementia creates opportunities for early and more effective interventions designed to improve or maintain cognitive functions and to alter patterns of brain activation and atrophy [2–4].
Mild cognitive impairment (MCI) is a well-established preclinical dementia syndrome characterized by the presence of cognitive impairment and complaint in the absence of functional impairment. MCI can be further subcategorized into amnestic MCI (aMCI; memory impaired) and non-amnestic MCI (naMCI; non-memory impaired) [5–8]. aMCI is a stronger predictor of AD than naMCI, and naMCI is a stronger predictor of non-AD dementia than aMCI [7–10]. Brain atrophy – loss of brain volume, neurons, and neural connections – in aMCI is particularly pronounced in hippocampal, amygdala, and neighboring temporal regions, while brain atrophy in naMCI is more widely distributed to prefrontal, temporal, insular, as well as cingulate regions [11, 12].
The motoric cognitive risk (MCR) syndrome is a preclinical dementia syndrome characterized by the presence of slow gait speed and subjective cognitive concerns [13–18]. A key strength of MCR is that it can be quickly identified in most clinical settings, including resource-poor settings. MCR is a reliable predictor of AD and VaD, even after adjusting for MCI. Yet, there is some evidence that MCR is a stronger predictor of VaD than AD [16]. We have shown that MCR is associated with a distributed pattern of cortical atrophy – including motor, supplementary motor, insular, prefrontal, temporal, and parietal regions – in the multicountry MCR consortium using harmonized (or centralized) image processing and analysis [19, 20].
In the current study of 733 older adults in the MCR consortium, we contrasted cortical and hippocampal atrophy patterns in MCR and aMCI. Contrasting atrophy patterns in MCR and aMCI is intriguing due to their overlapping roles in predicting AD. Contrasting atrophy patterns in MCR and aMCI could also provide insights into the neural underpinnings of the stronger association observed between MCR and VaD than between MCR and AD. We hypothesized that MCR would be associated with a distributed pattern of cortical atrophy consistent with vascular or mixed dementia, while aMCI would be associated with atrophy in hippocampal and neighboring temporal regions consistent with AD. If the atrophy pattern observed in MCR is more consistent with vascular or mixed dementia than AD, intervening with vascular risk factors for dementia (e.g., hypertension, physical inactivity) may prove efficacious in MCR.
Methods
Participants
Cross-sectional data from 733 older adults (M age = 73.61 years) from 4 cohorts in the MCR consortium was examined: (1) the LonGenity (N = 111) study in the USA [21, 22], (2) the Gait and Alzheimer’s Interactions Tracking (GAIT; N = 171) study in France [23, 24], (3) the Central Control of Mobility in Aging (CCMA; N = 139) study in the USA [25, 26], and (4) the Tasmanian Study of Cognition and Gait (TASCOG; N = 312) in Australia [27, 28]. The demographic characteristics of each cohort are presented in Table 1. The LonGenity, CCMA, and TASCOG cohorts were recruited from the community. The GAIT cohort was recruited from a memory clinic.
Table 1.
Demographic characteristics of the whole sample and separately for each cohort/study site
| All cohorts | LonGenity (USA) | GAIT (France) | CCMA (USA) | TASCOG (Australia) | |
|---|---|---|---|---|---|
| N | 733 | 111 | 171 | 139 | 312 |
| Age, years; M | 73.61 | 78.77 | 70.61 | 75.38 | 72.67 |
| Sex; % female (N) | 45.43 (333) | 55.86 (62) | 38.01 (65) | 50.36 (70) | 43.59 (136) |
| Education | |||||
| ≤4 years; % (N) | 1.50 (11) | 0 (0) | 1.75 (3) | 0 (0) | 2.56 (8) |
| 5–8 years; % (N) | 16.64 (122) | 0 (0) | 39.77 (68) | 2.16 (3) | 16.35 (51) |
| 9–12 years; % (N) | 35.88 (263) | 4.50 (5) | 35.09 (60) | 18.71 (26) | 55.13 (172) |
| ≥13 years; % (N) | 45.98 (337) | 95.50 (106) | 23.39 (40) | 79.14 (110) | 25.96 (81) |
| MCR; % (N) | 7.64 (56) | 11.71 (13) | 19.88 (34) | 2.16 (3) | 1.92 (6) |
| aMCI; % (N) | 12.96 (95) | 16.22 (18) | 29.82 (51) | 10.79 (15) | 3.53 (11) |
| Intracranial volume, mm3; M | 1,520,493 | 1,406,715 | 1,535,088 | 1,393,355 | 1,609,615 |
| White matter lesion, mm3; Mdn | 4,201 | 3,915 | 2,897 | 4,756 | 4,769 |
GAIT, Gait and Alzheimer’s Interactions Tracking study; CCMA, Central Control of Mobility in Aging study; TASCOG, Tasmanian Study of Cognition and Gait; MCR, motoric cognitive risk syndrome; aMCI, amnestic mild cognitive impairment.
Outcomes
MCR was operationalized as slow gait and subjective cognitive concerns in the absence of dementia and functional impairment – using previously established criteria [13, 14]. Gait velocity (cm/s) was quantified over 20 feet (609.6 cm) with GAITRite instrumented walkways (GAITRite System® Clifton, NJ) in all cohorts. Slow gait was defined as gait velocity one standard deviation or more below age, sex, and cohort-specific means. Subjective cognitive concerns were obtained from the memory item on the Geriatric Depression Scale (GDS [29]), the Health Self-Assessment Form [30], and/or the Consortium to establish a registry for AD subjective cognitive impairment self-administered questionnaire [31] in LonGenity. The GDS and/or the AD8 dementia screening interview [32] were used in the CCMA, the GDS and/or Instrumental Activities of Daily Living (I-ADL) in TASCOG, and self-report in GAIT. Participants with dementia were excluded using the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV [33]) and a consensus procedure in all cohorts except the LonGenity cohort who excluded older adults with dementia based on a telephone memory impairment screen <5 [34], AD8 score >1, or dementia diagnosed by clinician.
aMCI was operationalized as subjective cognitive concerns (defined above) and 1.5 standard deviation or more below z-standardized performance on any normally distributed episodic memory measure within each cohort [10]. Each normally distributed memory measure was standardized and examined individually to determine aMCI status. The memory measures examined to determine aMCI in the LonGenity study were figure recall from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS [35]), free recall from the Free and Cued Selective Reminding Test (FCSRT [36, 37]) and the logical memory total from the Wechsler Memory Scale-Revised [38]. The memory measures examined to determine aMCI in the CCMA study were list recall, story recall, figure recall, total free recall, total cued recall, total recall, immediate memory, and delayed memory from the RBANS. The memory measures examined to determine aMCI in the TASCOG study were immediate and delayed figure recall from the Rey-Osterrieth Complex Figure Test [39, 40] and immediate recall, delayed recall, and recognition from the Hopkins Verbal Learning Test-Revised [41–43]. The memory measures examined to determine aMCI in the GAIT study were free recall 1, free recall 2, free recall 3, delayed free recall, cued recall 1, cued recall 2, cued recall 3, and delayed cued recall from the FCSRT.
MRI Acquisition and Processing
Images acquired at each study site were transferred to Albert Einstein College of Medicine for harmonized processing and analysis procedures established previously [20]. Images were acquired with a Philips 3T Elition scanner in LonGenity, a Philips 3T Achieva scanner in CCMA, a General Electric 1.5T MRI scanner in TASCOG, and a Siemens 1.5T MAGNETOM Avanto scanner in GAIT. Three-dimensional T1-weighted images were obtained from all cohorts: (1) LonGenity: TR/TE of 9.9/4.6 ms, 240 mm2 FOV, 1 mm voxel size, (2) CCMA: TR/TE of 9.9/4.6 ms, 240 mm2 FOV, 1 mm voxel size [26], (3) TASCOG: TR/TE of 37/7 ms, 240 mm2 FOV, 1 mm voxel size [27], and (4) GAIT: TR/TE 2,170/4.07 ms, 240 mm2 mm FOV, 1 mm voxel size [23]. Mean cortical thickness of 34 cortical regions [44] was quantified with FreeSurfer 6.0: http://surfer.nmr.mgh.harvard.edu/. FreeSurfer automatically reconstructs cortical regions based on the morphology of an individual’s gyri and sulci and is comparable to manual labeling [45]. Cortical reconstruction involves skull stripping, bias field correction, gray-white matter segmentation, reconstruction of cortical surface, and nonlinear registration. Overall hippocampal volume and the volume of 12 different hippocampal subfields was also computed with the hippocampal tool in FreeSurfer 6.0 [46] – which automatically segments and calculates the volume of 12 hippocampal subfields, using a probability atlas built with ultra-high-resolution ex vivo MRI data. FreeSurfer has been shown generate reliable estimates of cortical thickness and hippocampal volume across different scanning sessions and study sites [47–49]. Results were visually inspected with FreeView to ensure proper segmentation, reconstruction and registration. Minor deviations from the cortical surface, however, were not corrected because we have previously shown that this does not influence associations between cortical thickness and MCR in our consortium [20]. Note also that we examined cortical atrophy in terms of cortical thickness rather than volume because there is evidence for that cortical thickness is a more sensitive measure of age-related, AD-related, and MCR-related cortical changes [20, 50–54].
Statistical Approach
Differences in age, sex, education, white matter lesions, and study site as a function of MCR were examined with t tests or χ2 tests after inspection for potential violations to normality and other statistical assumptions. A multivariate general linear model (GLM) with cortical thickness in 34 different regions as outcomes was then performed and followed by 34 univariate GLMs for each cortical region. Univariate GLMs were only interpreted if the multivariate model showed a statistically significant association with our primary outcomes of interest (MCR, aMCI) at p < 0.05. To generate more precise confidence intervals for each univariate model, and to protect against potential violations of the normality assumption, the reliability of each model was further evaluated using 1,000 randomly generated bootstrapped samples (n-1, with resampling) [55]. An additional multivariate GLM with the volume of 12 different hippocampal subfields as outcomes was also performed and followed by 12 univariate GLMs for each subfield. Again, univariate GLMs were only interpreted if the overall multivariate model was significant at p < 0.05 and bootstrapping was used to generate more precise estimates of the confidence intervals. All models were completed with STATA version 17.0 (StataCorp LP, College Station, TX, USA) and adjusted for the following covariates: age (years), sex, education (quartiles), study site (or cohort), total intracranial volume, and overall white matter lesion burden (from FreeSurfer 6.0).
Results
Table 1 lists the demographic characteristics of all participants, and as a function of cohort. The mean age was 73.61 years, 45.43% were female, and 45.98% had more than 13 years of education. The overall prevalence of MCR and aMCI was 7.64% and 12.96%, respectively. 2.86% of the sample had both MCR and aMCI. The GAIT cohort had the highest prevalence of MCR (19.88%) and aMCI (29.82%). Individuals with and without MCR (Table 2) did not differ in terms of age (t [732] = x, p = 0.28), sex (χ2 [1] = 1.51, p = 0.22), total intracranial volume (t [732] = x, p = 0.15), or overall white matter lesion burden (square root transformation; t [732] = x, p = 0.16). MCR and non-MCR individuals did differ, however, as a function of education quartile (Pearson χ2 [3] = 20.74, p < 0.000) and cohort (Pearson χ2 [3] = 59.15, p < 0.000). Online supplementary Table 1 (see https://doi.org/10.1159/000540512) lists the demographic characteristics as a function of MCR and aMCI status.
Table 2.
Demographic characteristics and bivariate association between MCR status in the whole sample
| MCR (All cohorts) | non-MCR (All cohorts) | p value | |
|---|---|---|---|
| N | 56 | 676* | |
| Age, years; M (SD) | 74.56 (7.35) | 73.53 (6.75) | 0.280 |
| Sex; % female (N) | 37.50 (21) | 46.01 (311) | 0.219 |
| Education | |||
| ≤4 years; % (N) 0 | 1.63 (11) | 0.000 | |
| 5–8 years; % (N) | 37.50 (21) | 14.94 (101) | |
| 9–12 years; % (N) | 33.93 (19) | 36.09 (244) | |
| ≥13 years; % (N) | 28.57 (16) | 47.34 (320) | |
| aMCI; % (N) | 37.5 (21) | 20.56 (139) | 0.001 |
| Intracranial volume, mm3; M | 1,487,407 | 1,523,561 | 0.147 |
| White matter lesion, mm3; Mdn | 5,600.37 | 14,576 | 0.165 |
MCR, motoric cognitive risk syndrome; aMCI, amnestic mild cognitive impairment.
*One participant was missing an MCR diagnosis.
Cortical Thickness, MCR, and aMCI
Our multivariate model for cortical thickness was significant overall (F (408; 7376) = 9.79, p < 0.0001) – as were the multivariate effects of MCR (Wilks’ lambda [Λ] = 0.92, p = 0.021), age (Λ = 0.72, p < 0.0001), sex (Λ = 0.80, p < 0.0001), cohort (Λ = 0.06, p < 0.0001), total intracranial volume (Λ = 0.87, p < 0.0001), and white matter hyperintensities (Λ = 0.82, p < 0.0001). Table 3 lists the effects of MCR in our multivariate and univariate GLMs. The effect of aMCI in our multivariate model was not significant ([Λ] = 0.95, p = 0.514) and is therefore not interpreted further (but see Table 4). Our univariate models suggested that cortical thickness was significantly lower among older adults with MCR than those without MCR in a number of prefrontal, insular, cingulate, motor, parietal, and temporal regions – including caudal middle frontal (95% CI = −0.333; −0.062), pars opercularis (95% CI = −0.301; −0.0), pars triangularis (95% CI = −0.269; −0.027), insular (95% CI = −0.377; −0.060), rostral anterior cingulate (95% CI = −0.455; −0.118), posterior cingulate (95% CI = −0.272; −0.035), precentral (95% CI = −0.407; −0.079), paracentral (95% CI = −0.390; −0.089), precuneus (95% CI = −0.312; −0.062), inferior parietal (95% CI = −0.321; −0.063), inferior temporal (95% CI = −0.354; −0.054), and temporal pole (95% CI = −0.552; −0.047) regions.
Table 3.
Effect of MCR on cortical thickness in multivariate and follow-up univariate GLMs
| Original estimate (Wilk’s lambda) | p value | |||
|---|---|---|---|---|
| Cortical thickness: Multivariate GLM | 0.923 | 0.021 |
| Estimate (b) | 95% CI | 95% CI | p value | |
|---|---|---|---|---|
| (lower) | (upper) | |||
| Cortical thickness: Univariate GLMs | ||||
| Bank superior temporal sulcus | −0.188 | −0.336 | −0.040 | 0.013 |
| Caudal anterior cingulate | −0.184 | −0.388 | 0.019 | 0.076 |
| Caudal middle frontal | −0.197 | −0.333 | −0.062 | 0.004 |
| Cuneus | −0.148 | −0.260 | −0.036 | 0.009 |
| Entorhinal | −0.254 | −0.502 | 0.006 | 0.044 |
| Fusiform | −0.208 | −0.369 | −0.048 | 0.011 |
| Inferior parietal | −0.192 | −0.321 | −0.063 | 0.003 |
| Inferior temporal | −0.204 | −0.354 | −0.054 | 0.008 |
| Isthmus cingulate | −0.157 | −0.283 | −0.031 | 0.014 |
| Lateral occipital | −0.141 | −0.256 | −0.026 | 0.016 |
| Lateral orbitofrontal | −0.186 | −0.336 | −0.035 | 0.015 |
| Lingual | −0.106 | −0.211 | −0.001 | 0.047 |
| Medial orbitofrontal | −0.175 | −0.311 | −0.038 | 0.012 |
| Middle temporal | −0.116 | −0.261 | 0.028 | 0.115 |
| Parahippocampal | −0.084 | −0.294 | 0.124 | 0.427 |
| Paracentral | −0.239 | −0.390 | −0.089 | 0.002 |
| Pars opercularis | −0.156 | −0.301 | −0.012 | 0.034 |
| Pars orbitalis | −0.215 | −0.382 | −0.048 | 0.011 |
| Pars triangularis | −0.148 | −0.269 | −0.027 | 0.016 |
| Pericalcarine | −0.140 | −0.222 | −0.057 | 0.001 |
| Postcentral | −0.115 | −0.232 | −0.001 | 0.053 |
| Posterior cingulate | −0.153 | −0.272 | −0.035 | 0.011 |
| Precentral | −0.243 | −0.407 | −0.079 | 0.004 |
| Precuneus | −0.187 | −0.312 | −0.062 | 0.003 |
| Rostral anterior cingulate | −0.286 | −0.455 | −0.118 | 0.001 |
| Rostral middle frontal | −0.092 | −0.221 | −0.035 | 0.157 |
| Superior frontal | −0.161 | −0.307 | −0.015 | 0.030 |
| Superior parietal | −0.146 | −0.278 | −0.015 | 0.028 |
| Superior temporal | −0.136 | −0.281 | 0.009 | 0.067 |
| Supramarginal | −0.182 | −0.313 | −0.051 | 0.006 |
| Frontal pole | −0.155 | −0.363 | 0.052 | 0.142 |
| Temporal pole | −0.299 | −0.552 | −0.047 | 0.020 |
| Transverse temporal | −0.269 | −0.430 | −0.107 | 0.001 |
| Insula | −0.219 | −0.377 | −0.060 | 0.007 |
Models were adjusted for aMCI, age, sex, education, study site, total intracranial volume, and overall white matter lesion burden. Univariate effects should only be interpreted if multivariate effect was significant at p < 0.05.
Bold numbers are statistically significant at p < 0.05.
Table 4.
Effect of aMCI on cortical thickness in multivariate and follow-up univariate GLMs
| Original estimate (Wilk’s lambda) | p value | |||
|---|---|---|---|---|
| Cortical thickness: Multivariate GLM | 0.951 | 0.514 |
| Estimate (b) | 95% CI | 95% CI | p value | |
|---|---|---|---|---|
| (lower) | (upper) | |||
| Cortical thickness: Univariate GLMs | ||||
| Bank of superior temporal sulcus | 0.002 | −0.089 | 0.095 | 0.951 |
| Caudal anterior cingulate | 0.048 | −0.063 | 0.159 | 0.397 |
| Caudal middle frontal | 0.050 | −0.027 | 0.128 | 0.202 |
| Cuneus | 0.077 | −0.007 | −0.146 | 0.029 |
| Entorhinal | −0.052 | −0.223 | 0.119 | 0.550 |
| Fusiform | 0.025 | −0.061 | 0.112 | 0.569 |
| Inferior parietal | 0.041 | −0.034 | 0.118 | 0.284 |
| Inferior temporal | 0.073 | −0.002 | 0.150 | 0.059 |
| Isthmus cingulate | 0.021 | −0.062 | 0.105 | 0.610 |
| Lateral occipital | 0.051 | −0.009 | 0.112 | 0.100 |
| Lateral orbitofrontal | 0.067 | −0.017 | 0.152 | 0.118 |
| Lingual | 0.070 | 0.010 | 0.129 | 0.021 |
| Medial orbitofrontal | 0.081 | −0.003 | 0.166 | 0.060 |
| Middle temporal | 0.002 | −0.084 | 0.088 | 0.962 |
| Parahippocampal | −0.000 | −0.143 | 0.142 | 0.996 |
| Paracentral | 0.070 | −0.014 | 0.156 | 0.103 |
| Pars opercularis | 0.041 | −0.032 | 0.115 | 0.271 |
| Pars orbitalis | 0.115 | 0.034 | 0.196 | 0.005 |
| Pars triangularis | 0.034 | −0.032 | 0.101 | 0.316 |
| Pericalcarine | 0.023 | −0.042 | 0.089 | 0.484 |
| Postcentral | 0.050 | −0.014 | 0.115 | 0.131 |
| Posterior cingulate | 0.026 | −0.056 | 0.109 | 0.537 |
| Precentral | 0.090 | −0.002 | 0.183 | 0.056 |
| Precuneus | 0.046 | −0.025 | 0.118 | 0.206 |
| Rostral anterior cingulate | 0.109 | −0.008 | 0.228 | 0.070 |
| Rostral middle frontal | 0.057 | −0.012 | 0.127 | 0.105 |
| Superior frontal | 0.063 | −0.017 | 0.144 | 0.124 |
| Superior parietal | 0.047 | −0.028 | 0.123 | 0.219 |
| Superior temporal | −0.008 | −0.083 | 0.066 | 0.833 |
| Supramarginal | 0.056 | −0.018 | 0.130 | 0.138 |
| Frontal pole | 0.163 | 0.039 | 0.286 | 0.009 |
| Temporal pole | 0.084 | −0.069 | 0.238 | 0.282 |
| Transverse temporal | 0.067 | −0.042 | 0.177 | 0.229 |
| Insula | 0.052 | −0.030 | 0.135 | 0.214 |
Models were adjusted for MCR, age, sex, education, study site, total intracranial volume, and overall white matter lesion burden. Bold numbers are statistically significant at p < 0.05. Univariate effects should only be interpreted if multivariate effect was significant at p < 0.05.
Hippocampal Volume, MCR, and aMCI
Our multivariate volume model for hippocampal volume was significant overall (F (144; 5,736) = 10.93, p < 0.0001) – as were the multivariate effects of aMCI (Λ = 0.96, p < 0.001), age (Λ = 0.69, p < 0.0001), sex (Λ = 0.95, p < 0.001), education (Λ = 0.92, p = 0.009), cohort (Λ = 0.48, p < 0.0001), total intracranial volume (Λ = 0.81, p < 0.0001), and white matter hyperintensities (Λ = 0.94, p < 0.001). Hippocampal volume did not differ, however, between those with MCR and those without MCR (Λ = 0.98, p = 0.601) and is therefore not interpreted (but see Table 5). Table 6 lists the results of our multivariate and univariate GLMs for hippocampal volumes and aMCI. Our univariate models revealed that hippocampal volume was lower among individual with aMCI than those without aMCI in the subiculum (95% CI = −64.18; −17.71), hippocampal fissure (95% CI = −32.07; −5.22), presubiculum (95% CI = −46.91; −10.90), molecular layer (95% CI = −74.48; −12.95), granular cell layer of dentate gyrus (95% CI = −41.97; −8.55), CA3 (95% CI = −31.86; −2.85), CA4 (95% CI = −36.32; −7.40), fimbria (95% CI = −16.31; −2.04), and hippocampal-amygdala transition area (95% CI = −8.65; −0.29).
Table 5.
Effect of MCR on hippocampal volume in multivariate and follow-up univariate GLMs
| Original estimate (Wilk’s lambda) | p value | |||
|---|---|---|---|---|
| Hippocampal volume: Multivariate | 0.985 | 0.601 |
| Estimate (b) | 95% CI | 95% CI | p value | |
|---|---|---|---|---|
| (lower) | (upper) | |||
| Hippocampal subfield volume: Univariate | ||||
| Hippocampal tail | −5.054 | −28.326 | 38.435 | 0.767 |
| Subiculum | 3.467 | −25.659 | 32.594 | 0.816 |
| CA1 | 2.186 | −43.221 | 47.593 | 0.925 |
| Hippocampal fissure | 7.917 | −8.909 | 24.744 | 0.356 |
| Presubiculum | −9.803 | −30.156 | 10.550 | 0.345 |
| Parasubiculum | 2.547 | −3.393 | 8.488 | 0.401 |
| Molecular layer HP | −1.224 | −38.562 | 36.114 | 0.949 |
| GCM LDG | −0.632 | −22.181 | 20.917 | 0.954 |
| CA3 | −0.425 | −19.401 | 18.549 | 0.965 |
| CA4 | −0.180 | −18.745 | 18.384 | 0.985 |
| Fimbria | −2.542 | −11.949 | 6.864 | 0.596 |
| HATA | 0.397 | −4.148 | 4.942 | 0.864 |
Models were aMCI adjusted for age, sex, education, study site, total intracranial volume, and overall white matter lesion burden. Bold numbers are statistically significant at p < 0.05. Univariate effects should only be interpreted if multivariate effect was significant at p < 0.05.
HATA, hippocampal-amygdala transition area.
Table 6.
Effect of aMCI on hippocampal volume in multivariate and follow-up univariate GLMs
| Original Estimate (Wilk’s lambda) | p value | |||
|---|---|---|---|---|
| Hippocampal volume: Multivariate | 0.962 | 0.009 |
| Coefficient | 95% CI | 95% CI | p value | |
|---|---|---|---|---|
| (lower) | (upper) | |||
| Hippocampal subfield volume: Univariate | ||||
| Hippocampal tail | −30.819 | −64.183 | 2.543 | 0.070 |
| Subiculum | −41.587 | −65.468 | −17.706 | 0.001 |
| CA1 | −35.710 | −71.759 | 0.338 | 0.052 |
| Hippocampal fissure | −18.641 | −32.066 | −5.217 | 0.006 |
| Presubiculum | −28.902 | −46.908 | −10.895 | 0.002 |
| Parasubiculum | −4.513 | −9.751 | 0.725 | 0.091 |
| Molecular layer | −43.718 | −74.488 | −12.948 | 0.005 |
| Granular cell layer of dentate gyrus | −25.261 | −41.971 | −8.551 | 0.003 |
| CA3 | −17.352 | −31.859 | −2.846 | 0.019 |
| CA4 | −21.860 | −36.318 | −7.402 | 0.003 |
| Fimbria | −9.175 | −16.312 | −2.038 | 0.012 |
| HATA | −4.339 | −8.648 | −0.029 | 0.048 |
All models were adjusted for MCR age, sex, education, study site, total intracranial volume, and overall white matter lesion burden. Bold numbers are statistically significant at p < 0.05. Univariate effects should only be interpreted if multivariate effect was significant at p < 0.05.
HATA, hippocampal-amygdala transition area.
Discussion
The prevalence of MCR identified in this study (7.64%) is similar to the prevalence reported in other studies [13, 56, 57]. In our multicountry prevalence study, for example, the overall prevalence was 9.7% with a range between 5.3% and 15.5% in the individual cohorts. This direct comparison of brain atrophy in MCR and aMCI suggests that MCR is associated with distributed cortical atrophy, including prefrontal, parietal, temporal, insular, and cingulate regions, while aMCI is associated with more restricted hippocampal atrophy. The distributed pattern of atrophy observed in MCR is consistent with patterns of atrophy previously observed in VaD [58, 59] and as a result of vascular brain pathologies (e.g., white matter hyperintensities, lacunes, microbleeds) [60]. We have previously linked MCR to frontal lacunes [61]. These findings suggest that vascular risk factors for dementia such as hypertension, cholesterol (diet), and physical inactivity should be targeted in future interventions. Yet, previous studies also suggest that MCR is associated with AD [18] and AD pathologies (plasma-TAU) [62] and that a genetic risk factor of AD (APOE-4) predicts the conversion of MCR to dementia [63]. It is possible that (like MCI) there are different subtypes of MCR that are associated with different types of cognitive impairment and dementia and different patterns of brain atrophy and/or pathologies. One study suggests that while the conventional gait speed definition of MCR (used here) is associated with attention and language, a swing-time variability definition of MCR is associated with all cognitive domains, including memory [64]. Another study suggests that olfactory dysfunction is associated with incident MCR and that Tau tangle density and Lewy bodies are independently associated with olfaction in individuals with MCR [65]. Future research is needed to determine if different MCR subtype definitions based on different gait and olfactory parameters are associated with different patterns of brain atrophy and/or brain/dementia pathologies. Future studies are also needed to contrast brain atrophy and pathologies in MCR with other preclinical stages of dementia (e.g., naMCI), and those who meet criteria for more than one preclinical dementia syndrome (MCR, aMCI, and/or naMCI).
Regardless, these findings are consistent with previous findings indicating that social (e.g., social support), cognitive (e.g., severity of cognitive impairment), affective (e.g., depressive symptoms), and cardiovascular (e.g., cardiovascular disease) factors are associated with MCR and/or the conversion of MCR to dementia. Thus, interventions that are socially, cardiovascularly, and/or cognitively demanding may be efficacious in MCR. There is preliminary evidence for that acute aerobic exercise increases prefrontal cortex oxygenation in MCR [66], yet it is unclear if this increased activation results in improved cognitive function or disease progression. Future interventions are needed to examine these issues.
There are strengths and weaknesses of this study. First, this study directly contrasted cortical and hippocampal atrophy patterns in MCR and aMCI in a large sample of older adults without dementia – using harmonized definitions of MCR and aMCI. Second, unwanted variability was further reduced by using a centralized image processing pipeline (FreeSurfer 6.0) that has been shown to be highly reliable between sessions and study sites [47–49] and by using multivariate models adjusted for important covariates and confounders. Yet, using data from multiple cohorts introduces variability that is difficult to adjust for, including variability in methods for excluding individuals with dementia and determining the presence of aMCI (i.e., different memory tests). Additional neuroimaging measures (e.g., resting-state fMRI) and participant characteristics (e.g., functional status) of interest were also not available in all cohorts. Resting-state fMRI, for example, can provide insight into the function of atrophied regions and activation networks in individuals with MCR. Contrasting cortical and hippocampal atrophy at cross-section also does not address the rate of cortical and hippocampal atrophy in MCR and aMCI over time. Future studies are needed to determine the reliability of these cross-sectional findings and to examine changes in atrophy in MCR and aMCI over time.
Conclusion
Distinct patterns of cortical atrophy are associated with MCR and aMCI pre-dementia syndromes and may require different interventions. These findings indicate that MCR has a cortical atrophy pattern more consistent with vascular or mixed dementia than AD. Additional research on the brain changes and dementia pathologies associated with MCR, and different subtypes of MCR, is needed.
Statement of Ethics
Study protocols were approved by Ethics Committees at each of the participating sites: the Institutional Review Board (IRB) at Albert Einstein College of Medicine (Approval No.: #2018-9033, #2010-224), the University of Angers Ethical Review Committee (Clinical Trials Identifier: NCT01315717), and the Tasmanian Health and Medical Human Research Ethics Committee (Approval No.: H7947). The secondary consortia analyses presented in this paper were also approved by the IRB at Albert Einstein College of Medicine (Approval No.: 2017-8386). All participants provided written informed consent to participate in the study at their local institution.
Conflict of Interest Statement
Richard B. Lipton has received research support from the National Institutes of Health (NIH), the FDA, and the National Headache Foundation. He serves as a consultant, advisory board member, or has received honoraria or research support from AbbVie/Allergan, Amgen, Axon, Biohaven, Dr. Reddy’s Laboratories (Promius), electroCore, Eli Lilly and Company, GlaxoSmithKline, Lilly, Lundbeck, Merck, Novartis, Pfizer, Teva, Vector, and Vedanta Research. He receives royalties from Wolff’s Headache, 8th edition (Oxford University Press, 2009) and Informa. He holds stock/options in Axon, Biohaven, Cooltech, and Manistee. Helena M. Blumen serves as a consultant for Neural+.
Funding Sources
This study was supported by NIH: National Institute on Aging: R56AG057548 and R01AG057548 (PI: Joe Verghese) and R01AG062659 (PI: Helena Blumen).
Author Contribution
Joe Verghese and Helena Blumen were involved in the conceptualization and design of this study. Vineela Nagamalla and Helena Blumen curated/analyzed the data and wrote the first draft of the manuscript. All authors – Emmeline Ayers, Nir Barzilai, Olivier Beauchet, Helena Blumen, Richard Lipton, Vineela Nagamalla, Hiroyuki Shimada, Velandai Srikanth, and Joe Verghese – were involved in revising and editing the manuscript and approved the final version of the manuscript.
Funding Statement
This study was supported by NIH: National Institute on Aging: R56AG057548 and R01AG057548 (PI: Joe Verghese) and R01AG062659 (PI: Helena Blumen).
Data Availability Statement
The data that support the findings of this study are not publicly available because they contain information that could compromise the privacy of research participants but are available from the corresponding author (H.M.B.) or MCR Consortium Steering Committee (emmeline.ayers@einsteinmed.edu) upon reasonable request.
Supplementary Material.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are not publicly available because they contain information that could compromise the privacy of research participants but are available from the corresponding author (H.M.B.) or MCR Consortium Steering Committee (emmeline.ayers@einsteinmed.edu) upon reasonable request.