Background
The two commonest contributors to late‐life cognitive impairment are Alzheimer’s (AD) and cerebrovascular disease; these two conditions almost invariably overlap. Understanding the ...determinants of the pathologies that underpin these conditions and how they interact to influence late‐life brain health is vital for rational risk prevention and for clinical trials.
Method
Data from the Insight 46 cohort will be presented, comprising individuals from the MRC 1946 British Birth Cohort all born in mainland Britain in one week in 1946. These individuals have been followed prospectively including serial measures of cognition from age 8, cardiovascular risk since the mid‐30s, and a range of cardiac and vascular outcomes in their 60s. At age 69‐71 they had detailed cognitive testing, 3T‐MRI including determination of white matter hyperintensity volume (WMHV) and 18F‐Florbetapir (β‐amyloid) PET. All individuals are being seen for a second visit, two‐years after the first.
Results
A total of 502 participants were assessed cross‐sectionally; data from up to 465 participants (51.0% male, mean age=70.7±0.7, 18.2% β‐amyloid positive) are available. Results of cross‐sectional analyses investigating the relationships between life course cardiovascular risk factors genetics, β‐amyloid and WMHV will be presented, alongside analyses of the associations of these pathologies with cross‐sectional cognitive function and MRI metrics. Investigations exploring mechanistic relationships e.g. between cardiac and vascular outcomes including pulse wave velocity and echocardiography, β‐amyloid and WMHV and cognition will be presented. Interim results of analyses exploring relationships between baseline β‐amyloid and WMHV and rates of cognitive decline and brain atrophy will be described in n=250 (47.6% female, age=72.5±0.33, 18.1% β‐amyloid positive) of the cohort. These results will be compared and contrasted with those from other cohort studies including the Atherosclerosis Risk in Communities (ARIC) study, and Mayo Clinic Study of Ageing.
Conclusion
Combining life course data, contemporaneous measurement of PET‐amyloid status, WMHV and cognition, vascular metrics and longitudinal measures of brain atrophy and cognitive change provides a powerful opportunity to explore how and when vascular and β‐amyloid pathology influence brain health in later‐life. Emerging evidence from several studies suggest that vascular risk influences the development of cognitive impairment and dementia principally via non‐amyloidogenic pathways.
Abstract
Background
Word‐finding difficulties are a common early feature of Alzheimer’s disease (AD) and may be detectable during the preclinical stage. However, the relationship between changes in ...naming ability and accumulation of β‐amyloid pathology is not fully understood, and questions remain about the role of factors such as sex and education.
Method
Participants in Insight 46, a sub‐study of the British 1946 birth cohort, completed baseline cognitive assessments and neuroimaging (combined MRI/
18
F‐Florbetapir‐PET) at age 69‐71. Follow‐up assessments are currently underway (mean interval 28.9 months, SD 2.1) and include an audio‐recorded version of the 30‐item Graded Naming Test (GNT), which was not administered at baseline. Preliminary interim analyses have been conducted based on 211 cognitively‐normal individuals with complete neuroimaging data (see Table 1 for characteristics). A multivariable regression model was used to investigate predictors of picture naming accuracy, where the outcome was GNT score (max. 30) and predictors were sex, age at follow‐up assessment, β‐amyloid status at baseline (positive / negative),
APOE
genotype (ε4 carrier/non‐carrier), and prospectively‐collected measures of childhood cognitive ability, education and socioeconomic position (based on occupation). Due to the negatively‐skewed distribution of GNT scores, bootstrapping was used to produce bias‐corrected and accelerated 95% confidence intervals from 2,000 replications.
Results will be updated to include the full sample before the conference. We also plan to include data on naming latency extracted from the audio‐recordings, which may be a more sensitive measure of early changes than naming accuracy.
Result
Higher childhood cognitive ability predicted higher GNT scores over 60 years later (Table 2
,
Figure 1). Men scored 0.9 points higher than women on average. Amyloid‐positive participants scored 1.2 points lower than amyloid‐negative participants on average. These effects were all significant at the 5% level and were mutually independent.
Conclusion
Subtle changes in naming accuracy associated with β‐amyloid pathology are detectable in cognitively‐normal individuals as early as age 72. Performance is additionally influenced by sex and general cognitive ability, so these factors should be accounted for where possible in future studies and clinical trials that seek to detect and track the emergence of naming deficits.
Background
Mid‐life hypertension is an established risk factor for late‐life cognitive impairment. Whilst previous studies demonstrate mid‐life hypertension is associated with larger white matter ...(WM) hyperintensity volumes, Differences in normal appearing white matter (NAWM) microstructure may provide an earlier indication of WM injury. In a population‐based life‐course study of cognitively healthy individuals, we explored the relationship between blood pressure (BP) over 30 years and NAWM microstructural metrics in later life.
Method
Participants from Insight 46, a sub‐study of the 1946 British birth cohort, underwent multi‐modal MR imaging including T1, T2, FLAIR and multi‐shell diffusion‐weighted sequences at age 69‐71. Diffusion‐weighted images were processed by automated pipelines, NAWM masks were derived by subtracting the BaMoS‐derived white matter hyperintensity mask from GIF pipeline generated WM mask (eroded by 1 voxel) using FSL. Mean values of microstructural integrity metrics (fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI), orientation dispersion index (ODI)) were extracted from T1‐registered diffusion maps using FSL and NODDI toolbox. Individuals with a major brain or neurodegenerative disorder such as dementia, neuroinflammatory condition or stroke were excluded. Linear regression analyses examined relationships between systolic blood pressure (SBP) and diastolic blood pressure (DBP) at ages 36, 43, 53, 60‐64 and 69 and microstructural metrics at age 69‐71 adjusting for sex, age, socioeconomic class, educational attainment, childhood cognition and antihypertensive medication.
Result
379 participants were included (mean age at imaging 70.7 years, 50% female). Higher SBP at ages 53 and 69 was associated with lower FA and NDI; and higher MD, and SBP at 69 was associated with higher ODI. Similarly, higher DBP at ages 53, 60‐64 and 69 were associated with lower FA and NDI; and higher MD. There was no evidence of associations between BP at age 36 or 43 and NAWM diffusion metrics.
Conclusion
Higher systolic and diastolic blood pressure from age 53 onwards are shown to be associated with differences in diffusion‐based measures of white matter microstructural integrity later in life, suggesting that systolic or diastolic hypertension in over 50’s may contribute to cognitive impairment risk via alterations in NAWM microstructure differences in later life.
Background
While APOE‐ε4 carriers are at higher risk of Alzheimer’s disease (AD), there is evidence that APOE‐ε4 may have some beneficial effects across the life‐span, including on cognition. It is ...unclear how such effects may relate to subtle memory decline during the preclinical phase of AD. Two previous studies reported that APOE‐ε4 carriers recalled object locations more accurately than non‐carriers on the “What was where?” visual short‐term memory binding test (10.1016/j.cortex.2016.12.016; 10.1016/j.neurobiolaging.2018.09.017), but these studies did not account for preclinical AD pathology.
Method
The “What was where?” task (Figure 1) was administered to participants in Insight 46 – a sub‐study of the British 1946 birth cohort – who were all born during the same week (aged 69‐71 at assessment (Table 1)). Outcomes included object identification and a sensitive analogue measure of localisation error (the distance between the location reported by the participant and the true location). Two‐dimensional mixture models (10.31234/osf.io/q57fm) were used to isolate three sources of localisation error: imprecision, guessing, and misbinding (swapping an object’s location with that of a different object). β‐Amyloid status (positive / negative) was determined from 18F‐Florbetapir‐PET. Multivariable regression models were used to investigate differential effects of APOE genotype (ε4‐carrier / non‐carrier) and β‐amyloid status on performance in 398 cognitively‐normal participants, adjusting for confounders including a prospectively‐collected measure of childhood cognitive ability.
Result
APOE‐ε4 and β‐amyloid had opposing effects on object identification, with APOE‐ε4 predicting better recall and β‐amyloid‐positivity predicting poorer recall. APOE‐ε4 carriers also recalled object locations more precisely, but a subtle detrimental effect of β‐amyloid on localisation was seen only among non‐carriers (Table 2, Figure 2). Childhood cognitive ability also predicted performance over 60 years later (Table 2).
Conclusion
In this large population‐based sample of cognitively‐normal ∼70‐year‐olds, a positive association between APOE‐ε4 and short‐term visual memory was observed. For the localization measure, this appeared to be protective against a subtle deficit associated with β‐amyloid pathology. This is consistent with the antagonistic pleiotropy hypothesis – whereby a gene controls both beneficial and detrimental traits – and provides novel evidence that these effects persist into older age, even among individuals who may be in the preclinical stages of AD.
Background
Age is the biggest risk factor for dementia, yet human brains do not age uniformly. The British 1946 birth cohort, the world’s longest continuously running birth cohort, provides a unique ...opportunity to assess these variations in biological ageing. So‐called ‘brain age’ is a biomarker of brain ageing, derived from machine‐learning analysis trained on a large sample of healthy brains (N=2001). Brain age has previously been related to cognitive ageing, physiological ageing and mortality risk (DOI: 10.1038/mp.2017.62), supporting the validity of this approach for assessing biological ageing.
Method
502 participants in the Insight 46 study, all born during one week in 1946, completed baseline cognitive and neuroimaging assessments at age 69‐71. 468 underwent combined 18florbetapir PET‐MRI scans, from which amyloid status (positive/negative), whole brain volume (WBV), total intracranial volume (TIV) and hippocampal volumes (HV) were derived. The T1‐weighted sequence was passed through the Brain‐age algorithm (https://github.com/james‐cole/brainageR), deriving brain predicted‐age (BPA) and brain‐predicted age difference (brain‐PAD; BPA minus chronological age). Serum neurofilament light (NFL) concentration was measured via Simoa immunoassay. A Preclinical Alzheimer’s Cognitive Composite Score (PACC) was calculated as a mean of z‐scores of the Mini‐mental state exam (MMSE), logical memory delayed recall, digit symbol substitution score and the Face‐Name test. Life course metrics (childhood cognitive scores, education level and Framingham Risk scores) were obtained from previous cohort assessments. Multivariate regression models were used to investigate whether life course metrics predict BPA, as well as whether NFL levels, brain volumes, or cognitive scores correlated with BPA, adjusting for chronological age.
Result
There was a significant difference between the 229 females assessed (mean BPA 65.2 years) compared with the 239 males assessed (mean BPA 70.7). BPA was independently associated with serum NFL concentration (p = 0.071) and inversely with whole brain volume (p < 0.001). Life course factors did not predict brain age.
Conclusion
The results showed a significant association of BPA, a cross‐sectional imaging metric, with a biochemical marker of neuronal damage (NFL) and sex. BPA has utility as an imaging metric that can integrate multiple modalities contributing to biological age, with potential as a predictive biomarker of cognitive decline.
Abstract
Background
While
APOE
‐ε4 carriers are at higher risk of Alzheimer’s disease (AD), there is evidence that
APOE
‐ε4 may have some beneficial effects across the life‐span, including on ...cognition. It is unclear how such effects may relate to subtle memory decline during the preclinical phase of AD. Two previous studies reported that
APOE
‐ε4 carriers recalled object locations more accurately than non‐carriers on the “What was where?” visual short‐term memory binding test (10.1016/j.cortex.2016.12.016; 10.1016/j.neurobiolaging.2018.09.017), but these studies did not account for preclinical AD pathology.
Method
The “What was where?” task (Figure 1) was administered to participants in Insight 46 – a sub‐study of the British 1946 birth cohort – who were all born during the same week (aged 69‐71 at assessment (Table 1)). Outcomes included object identification and a sensitive analogue measure of localisation error (the distance between the location reported by the participant and the true location). Two‐dimensional mixture models (10.31234/osf.io/q57fm) were used to isolate three sources of localisation error: imprecision, guessing, and misbinding (swapping an object’s location with that of a different object). β‐Amyloid status (positive / negative) was determined from
18
F‐Florbetapir‐PET. Multivariable regression models were used to investigate differential effects of
APOE
genotype (ε4‐carrier / non‐carrier) and β‐amyloid status on performance in 398 cognitively‐normal participants, adjusting for confounders including a prospectively‐collected measure of childhood cognitive ability.
Result
APOE
‐ε4 and β‐amyloid had opposing effects on object identification, with
APOE
‐ε4 predicting better recall and β‐amyloid‐positivity predicting poorer recall.
APOE
‐ε4 carriers also recalled object locations more precisely, but a subtle detrimental effect of β‐amyloid on localisation was seen only among non‐carriers (Table 2
,
Figure 2). Childhood cognitive ability also predicted performance over 60 years later (Table 2).
Conclusion
In this large population‐based sample of cognitively‐normal ∼70‐year‐olds, a positive association between
APOE
‐ε4 and short‐term visual memory was observed. For the localization measure, this appeared to be protective against a subtle deficit associated with β‐amyloid pathology. This is consistent with the antagonistic pleiotropy hypothesis – whereby a gene controls both beneficial and detrimental traits – and provides novel evidence that these effects persist into older age, even among individuals who may be in the preclinical stages of AD.
Background
Alzheimer’s (AD) and cerebrovascular disease are common causes of cognitive impairment in later life and often co‐exist. Understanding how AD and vascular pathologies act independently or ...together to influence neurodegeneration in later life is important for the development of effective treatments and clinical trial design.
Method
219 cognitively normal participants underwent cognitive testing, structural MRI and 18F‐florbetapir amyloid‐PET scans at two visits approximately two years apart. Changes in whole brain, ventricular and hippocampal volumes between time‐points were determined using the Boundary Shift Integral (BSI) (doi:10.1016/j.neuroimage.2009.12.059). Baseline white matter hyperintensity volume (WMHV) was generated using BaMoS (doi:10.1109/TMI.2015.2419072). Baseline amyloid SUVRs were derived with eroded subcortical white matter as the reference region and a composite grey matter target region. A cut‐point of 0.6104 was used to define amyloid positivity. Linear regression was used to investigate relationships of amyloid and WMHV with atrophy rates. Specifically, models were fitted with BSI as the outcome, scan interval as the explanatory variable, and interactions between scan interval and i) the explanatory variable of interest and ii) each of the covariates (age at baseline scan, sex and total intracranial volume). Amyloid and WMHV were assessed separately and then together within the same model. An interaction between amyloid, WMHV and scan interval was also tested in a further model.
Result
199 cognitively normal participants (mean baseline age 70.1±0.4 years; 47% female) had high‐quality imaging data (Table 1). Positive amyloid status was associated with greater rates of brain and hippocampal atrophy and ventricular expansion, with a positive relationship between SUVR and ventricular expansion and hippocampal atrophy (Table 2). Larger WMHV was associated with higher rates of brain and hippocampal atrophy and ventricular expansion (Table 2). None of these associations were meaningfully altered by including amyloid and WMHV within the same model (Table 3). There was no evidence of an interaction between amyloid and WMHV for any BSI measure (interaction p>0.13, all tests).
Conclusion
Markers of amyloid and presumed small‐vessel disease were independently associated with atrophy rates, and there was no evidence that either pathological process modified the effect of the other.
Abstract
Background
Mid‐life hypertension is an established risk factor for late‐life cognitive impairment. Whilst previous studies demonstrate mid‐life hypertension is associated with larger white ...matter (WM) hyperintensity volumes, Differences in normal appearing white matter (NAWM) microstructure may provide an earlier indication of WM injury. In a population‐based life‐course study of cognitively healthy individuals, we explored the relationship between blood pressure (BP) over 30 years and NAWM microstructural metrics in later life.
Method
Participants from Insight 46, a sub‐study of the 1946 British birth cohort, underwent multi‐modal MR imaging including T1, T2, FLAIR and multi‐shell diffusion‐weighted sequences at age 69‐71. Diffusion‐weighted images were processed by automated pipelines, NAWM masks were derived by subtracting the BaMoS‐derived white matter hyperintensity mask from GIF pipeline generated WM mask (eroded by 1 voxel) using FSL. Mean values of microstructural integrity metrics (fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI), orientation dispersion index (ODI)) were extracted from T1‐registered diffusion maps using FSL and NODDI toolbox. Individuals with a major brain or neurodegenerative disorder such as dementia, neuroinflammatory condition or stroke were excluded. Linear regression analyses examined relationships between systolic blood pressure (SBP) and diastolic blood pressure (DBP) at ages 36, 43, 53, 60‐64 and 69 and microstructural metrics at age 69‐71 adjusting for sex, age, socioeconomic class, educational attainment, childhood cognition and antihypertensive medication.
Result
379 participants were included (mean age at imaging 70.7 years, 50% female). Higher SBP at ages 53 and 69 was associated with lower FA and NDI; and higher MD, and SBP at 69 was associated with higher ODI. Similarly, higher DBP at ages 53, 60‐64 and 69 were associated with lower FA and NDI; and higher MD. There was no evidence of associations between BP at age 36 or 43 and NAWM diffusion metrics.
Conclusion
Higher systolic and diastolic blood pressure from age 53 onwards are shown to be associated with differences in diffusion‐based measures of white matter microstructural integrity later in life, suggesting that systolic or diastolic hypertension in over 50’s may contribute to cognitive impairment risk via alterations in NAWM microstructure differences in later life.
Background
Accelerated Forgetting (AF) is the phenomenon whereby material is retained normally over short intervals (minutes or hours) but forgotten abnormally rapidly over longer periods (days or ...weeks). AF has been observed in presymptomatic carriers of mutations causing familial Alzheimer’s disease (AD) (doi:10.1016/S1474‐4422(17)30434‐9). To our knowledge, no studies have investigated whether AF is sensitive to preclinical AD pathology in cognitively‐normal older adults.
Method
Participants in the Insight 46 study, a sub‐study of the British 1946 birth cohort, completed baseline cognitive and neuroimaging assessments at age 69‐71. For the follow‐up visits (∼29 months later), we complemented the clinic visit assessments of Complex Figure Drawing and the Face‐Name test (FNAME‐12) with a 7‐day delay version administered by telephone (Figure 1). AF scores were calculated as the percentage of material retained after 7 days, relative to retention after 30 minutes. Cerebral atrophy between baseline and follow‐up was quantified from T1‐weighted MRI using the Brain Boundary Shift Integral (BBSI). β‐amyloid status at baseline (positive / negative) was determined from 18F‐Florbetapir‐PET.
As follow‐up assessments are still underway, preliminary interim analyses have been conducted based on 195 cognitively‐normal individuals with complete neuroimaging data (see Table 1 for characteristics). Multivariable regression models were used to investigate the effects of β‐amyloid status and BBSI on AF, and to explore interactions between these two predictors, adjusting for potential confounders including prospectively‐collected measures of childhood cognitive ability and education.
Result
Despite no statistically‐significant differences after a 30‐minute delay, β‐amyloid‐positive participants retained a lower percentage of Complex Figure material over 7 days (71.8% vs. 80.7%, p=0.010) and a trend to a lower percentage of FNAME‐12 material (69.4% vs. 77.2%, p = 0.083) (Table 2, Figure 2). Higher education predicted better retention of the Complex Figure. Among β‐amyloid‐positive participants only, greater cerebral atrophy predicted poorer retention of the Complex Figure (Table 2, Figure 3).
Conclusion
These results provide novel evidence of AF in cognitively‐normal β‐amyloid‐positive 72‐year‐olds. AF may be a sensitive outcome measure for therapeutic trials in preclinical AD, as it may reveal subtle memory decline at an earlier stage than traditional assessments. The interaction between β‐amyloid pathology and cerebral atrophy merits longitudinal investigation.
Abstract
Background
Alzheimer’s (AD) and cerebrovascular disease are common causes of cognitive impairment in later life and often co‐exist. Understanding how AD and vascular pathologies act ...independently or together to influence neurodegeneration in later life is important for the development of effective treatments and clinical trial design.
Method
219 cognitively normal participants underwent cognitive testing, structural MRI and 18F‐florbetapir amyloid‐PET scans at two visits approximately two years apart. Changes in whole brain, ventricular and hippocampal volumes between time‐points were determined using the Boundary Shift Integral (BSI) (doi:10.1016/j.neuroimage.2009.12.059). Baseline white matter hyperintensity volume (WMHV) was generated using BaMoS (doi:10.1109/TMI.2015.2419072). Baseline amyloid SUVRs were derived with eroded subcortical white matter as the reference region and a composite grey matter target region. A cut‐point of 0.6104 was used to define amyloid positivity. Linear regression was used to investigate relationships of amyloid and WMHV with atrophy rates. Specifically, models were fitted with BSI as the outcome, scan interval as the explanatory variable, and interactions between scan interval and i) the explanatory variable of interest and ii) each of the covariates (age at baseline scan, sex and total intracranial volume). Amyloid and WMHV were assessed separately and then together within the same model. An interaction between amyloid, WMHV and scan interval was also tested in a further model.
Result
199 cognitively normal participants (mean baseline age 70.1±0.4 years; 47% female) had high‐quality imaging data (Table 1). Positive amyloid status was associated with greater rates of brain and hippocampal atrophy and ventricular expansion, with a positive relationship between SUVR and ventricular expansion and hippocampal atrophy (Table 2). Larger WMHV was associated with higher rates of brain and hippocampal atrophy and ventricular expansion (Table 2). None of these associations were meaningfully altered by including amyloid and WMHV within the same model (Table 3). There was no evidence of an interaction between amyloid and WMHV for any BSI measure (interaction p>0.13, all tests).
Conclusion
Markers of amyloid and presumed small‐vessel disease were independently associated with atrophy rates, and there was no evidence that either pathological process modified the effect of the other.
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