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Abstract

The potential protective role of education for dementia is an area of major interest. Almost all older people have some pathology in their brain at death but have not necessarily died with dementia. We have explored these two observations in large population-based cohort studies (Epidemiological Clinicopathological Studies in Europe; EClipSE) in an investigation of the relationships of brain pathology at death, clinical dementia and time in education, testing the hypothesis that greater exposure to education reduces the risk of dementia. EClipSE has harmonized longitudinal clinical data and neuropathology from three longstanding population-based studies that included post-mortem brain donation. These three studies started between 1985 and 1991. Number of years of education during earlier life was recorded at baseline. Incident dementia was detected through follow-up interviews, complemented by retrospective informant interviews, death certificate data and linked health/social records (dependent on study) after death. Dementia-related neuropathologies were assessed in each study in a comparable manner based on the Consortium to Establish a Registry for Alzheimer's Disease protocol. Eight hundred and seventy-two brain donors were included, of whom 56% were demented at death. Longer years in education were associated with decreased dementia risk and greater brain weight but had no relationship to neurodegenerative or vascular pathologies. The associations between neuropathological variables and clinical dementia differed according to the 'dose' of education such that more education reduced dementia risk largely independently of severity of pathology. More education did not protect individuals from developing neurodegenerative and vascular neuropathology by the time they died but it did appear to mitigate the impact of pathology on the clinical expression of dementia before death. The findings suggest that an understanding of the mechanisms leading to functional protection in the presence of pathology may be of considerable value to society.

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Socioeconomic status and cardiovascular disease: risks and implications for care.

Andy Wielgosz, Wei Luo, Robyn Clark … (2009)

Socioeconomic status (SES) refers to an individual's social position relative to other members of a society. Low SES is associated with large increases in cardiovascular disease (CVD) risk in men and women. The inverse association between SES and CVD risk in high-income countries is the result of the high prevalence and compounding effects of multiple behavioral and psychosocial risk factors in people of low SES. However, strong and consistent evidence shows that parental SES, childhood and early-life factors, and inequalities in health services also contribute to elevated CVD risk in people of low SES who live in high-income countries. In addition, place of residence can affect CVD risk, although the data on the influence of wealth distribution and work-related factors are inconsistent. Studies on the effects of SES on CVD risk in low-income and middle-income countries is scarce, but evidence is emerging that the increasing wealth of these countries is beginning to lead to replication of the patterns seen in high-income countries. Clinicians should address the association between SES and CVD by incorporating SES into CVD risk calculations and screening tools, reducing behavioral and psychosocial risk factors via effective and equitable primary and secondary prevention, undertaking health equity audits to assess inequalities in care provision and outcomes, and by use of multidisciplinary teams to address risk factors over the life course.

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Epidemiological Pathology of Dementia: Attributable-Risks at Death in the Medical Research Council Cognitive Function and Ageing Study

Fiona Matthews, Carol Brayne, James Lowe … (2009)

Introduction Assessment of brain pathology in the consensus protocols for pathological diagnosis of dementia has been based on semiquantitative methods [1]–[3]. These protocols aspire to distinguish demented and nondemented individuals using thresholds of plaques, neurofibrillary tangles (NFTs), infarcts, and Lewy bodies, so that pathology becomes the “gold standard” for diagnosis. This approach has progressed understanding of clinical phenotypes, genetics, biochemistry, and molecular pathogenesis associated with cognitive decline in older people. Trials of disease modifying therapies are already in progress and proponents of a vascular basis for cognitive dysfunction propose secondary prevention strategies in older people [4]. The scale of the clinical and social problem presented by dementia in ageing populations presents an urgent need to assess the likely impact and cost effectiveness of new, potentially expensive, therapies, and to develop robust biomarkers for diagnosis and progression. Understanding the population impact of therapies that modify the pathobiology of dementia requires an understanding of the burden of cognitive dysfunction directly attributable to a particular pathology. Recently reported trials in Alzheimer disease (AD), alleging divergent outcomes for inhibition of amyloid gamma-secretase and tau aggregation, exemplify this need [5],[6]. These are issues about which conventional case-control cohorts and studies in secondary referral populations, memory clinics, or community volunteers are less informative than the population approach used here. Selection biases are associated with non–population-based studies of older people and lead to unknown effects so that their conclusions may not generalise to the whole population. Dementia is associated with a high prevalence of mixed Alzheimer, vascular, and other pathologies, and the thresholds of severity that clearly distinguish between an AD brain and the brain of a nondemented individual only capture around 20% of demented people [7]–[9]. True population-based studies of dementia combining longitudinal assessment of clinical state with post mortem brain donation are rare but offer the only means at present of investigating the population-level impact of pathology on cognition [10]. The Cognitive Function and Ageing Study (CFAS) autopsy donor cohort is now of sufficient size to facilitate true “epidemiological neuropathology.” Here we present estimates of the attributable risk of dementia at death associated with specific neuropathological features in this cohort. Methods Ethics Statement All procedures received approval from a multicentre Research Ethics Committee. MRC Cognitive Function and Ageing Study (MRC CFAS) is a population-based longitudinal study of people, in their 65th year and over, enrolled from the population-based registers of primary care physicians in six sites in England and Wales [11]. In 1990 these registers provided full geographical population coverage including people living in institutional settings. In each of five centres random samples generated a recruited cohort of 2,500 individuals per centre (82% response rate) with equal numbers below and above 75 y. Trained interviewers conducted interviews with participants, including basic sociodemographic questions, cognitive examination, and items from the Geriatric Mental State (GMS) organicity scale, activities of daily living, physical health, and medication (see www.cfas.ac.uk). A 20% stratified sample underwent detailed assessment (GMS [12], augmented CAMCOG [13]) repeated after 2 y. This assessment group included those individuals with cognitive impairment and a random sample from the same centre. There were two re-interview cycles of all survivors and several follow-ups in the assessment group only. Study flow is shown in Figure 1. Diagnoses were made using the validated AGECAT algorithm [14]. 10.1371/journal.pmed.1000180.g001 Figure 1 MRC CFAS design. Numbers of interviews and donations from interview waves. In the sixth centre (Liverpool) 5,200 people aged 65 y and over, in equal numbers across 5-y bands, were recruited with a population sampling base. This study (ALPHA) started before the other sites but its design and methods enable it to be integrated into CFAS. Interviews in ALPHA were based on GMS and study flow is shown in Figure 2. 10.1371/journal.pmed.1000180.g002 Figure 2 MRC Alpha Study (Liverpool) design. Numbers of interviews and donations from each interview wave. Individuals, families, and carers in the assessment group were approached by trained liaison officers and invited to participate in counselling around brain donation. Those who agreed to brain donation were provided with information to allow staff or family involved in the final illness to notify the death and initiate brain donation. Donations still proceeded, wherever possible, for cases coming to autopsy under the coroner. There were 456 individuals in this analysis, representing all completed brain donations before 1st August 2004. The sample includes 207 individuals previously described (two families contributing to that previous cohort subsequently revoked consent and the data were removed) [7]. Dementia status was established using multiple information sources including AGECAT, notification of dementia in death certificates, a retrospective informant interview (RINI; www.cfas.ac.uk) with relatives and carers after death, and the probability of being demented before death from a Bayesian analysis of all individuals modelling the prevalence and incidence of dementia in CFAS. We could not assign dementia status in 30 individuals in whom the study diagnosis was “not dementia.” These respondents were not included in the analysis because their last interview was more than 6 mo before death, no RINI was available, and dementia was not mentioned on the death certificate. The neuropathology protocol used standardised assessment of paraffin-embedded tissues to record data using the Consortium to Establish a Registry of Alzheimer's Disease (CERAD) protocol [2]. CERAD data were augmented by a strategy for evaluating white matter lesions (WML) in the post mortem brain previously validated against histopathology [15]. Neuropathology was assessed without knowledge of clinical, interview, or RINI data. Acceptable inter-rater reliability ( 94 186 1 — 176 2 — 6 2 — P 9 y 5,697 32 — 3,170 28 — 122 27 — p = 0.97 Social class Missing 1,371 8 — 1,035 9 — 57 13 — — Nonmanual 6,574 36 — 3,717 32 — 165 36 — — Manual 10,303 56 — 6,713 59 — 234 51 — p = 0.02 Age group at death (y) 94 — — — 895 8 — 59 — 82–92 P 10). 10.1371/journal.pmed.1000180.t002 Table 2 Number of individuals with neuropathology findings in medial temporal and neocortical regions. Neuropathology Severity Hippocampus Percent Entorhinal Percent Frontal Temporal Parietal Occipital Overall Percent Neuritic plaques None 191 42 178 39 185 171 194 200 143 31 Mild 97 21 122 27 140 111 114 74 104 23 Moderate 123 27 106 23 91 123 108 80 139 31 Severe 39 9 41 9 37 50 38 23 70 15 Missing 6 1 9 2 3 1 2 79 0 0 Diffuse plaques None 203 45 142 31 134 130 151 143 115 25 Mild 128 28 110 24 106 95 107 94 95 21 Moderate 84 18 124 27 94 122 92 100 112 25 Severe 16 4 51 11 103 92 88 37 120 26 Missing 25 5 29 6 19 17 18 82 14 3 Tangles None 51 11 39 9 298 230 295 299 216 47 Mild 118 26 94 21 101 124 94 38 129 28 Moderate 134 30 185 41 40 62 47 26 70 15 Severe 147 32 128 28 11 37 11 9 39 9 Missing 6 1 10 2 6 7 9 84 2 1 Neuronal loss None 287 63 287 63 432 430 430 384 425 93 Mild 67 15 53 12 10 12 7 3 19 4 Moderate 39 9 46 10 0 0 0 0 0 0 Severe 37 8 34 7 0 0 0 0 0 0 Missing 26 6 36 8 14 14 19 69 12 3 Lewy bodies None 432 95 409 90 433 424 428 380 425 93 Mild 6 1 13 3 8 16 4 2 18 4 Moderate 0 0 4 1 0 0 0 0 0 0 Severe 0 0 1 0 0 0 0 0 0 0 Missing 18 4 29 6 15 16 24 74 13 3 10.1371/journal.pmed.1000180.t003 Table 3 Number of individuals with neuropathology findings in subcortical nuclei. Neuropathology Severity Substantia Nigra Nucleus Basalis Raphé Locus Ceruleus Dorsal Vagus Overall Percent Plaques None 389 202 271 283 194 353 77 Mild 2 30 8 1 0 36 8 Moderate 0 13 0 0 0 13 3 Severe 0 0 0 0 0 0 0 Missing/not measured 65 211 177 172 262 54 12 Tangles None 314 93 157 166 207 177 39 Mild 89 104 78 105 11 122 26 Moderate 13 49 52 42 0 77 17 Severe 20 47 36 14 0 71 15 Missing 20 163 133 129 238 9 2 Neuronal loss None 217 205 298 216 230 183 40 Mild 175 61 23 84 27 175 38 Moderate 36 23 2 27 14 67 15 Severe 16 6 11 3 25 6 Missing 12 161 133 118 182 6 2 Lewy bodies None 409 286 327 313 258 406 89 Mild 19 10 2 15 7 20 4 Moderate 11 1 0 9 10 18 4 Severe 8 0 0 1 0 9 2 Missing 9 159 127 118 181 3 1 10.1371/journal.pmed.1000180.t004 Table 4 Number of individuals by neuropathology and dementia status at death. Neuropathological Findings Severity No Dementia n = 183 Percent Dementia n = 243 Percent Uncertain n = 30 Percent Neocortex: neuritic plaques None 83 45 47 19 13 43 Mild 51 28 44 18 9 30 Moderate 43 24 89 37 7 23 Severe 6 3 63 26 1 3 Neocortex: diffuse plaques None 54 30 38 16 9 30 Mild 43 24 42 18 4 13 Moderate 56 31 60 26 9 30 Severe 28 15 91 39 8 27 Missing 12 — 12 — 0 — Neocortex: NFT None 114 63 81 33 21 67 Mild 59 33 63 26 7 22 Moderate 8 4 60 25 2 11 Severe 0 0 39 16 0 0 Neocortex: atrophy None 101 57 57 25 15 50 Mild 51 29 52 23 12 40 Moderate 24 14 94 41 2 7 Severe 1 1 28 12 0 0 Missing 6 — 12 — 1 — Hippocampus: neuritic plaques None 106 58 67 28 18 60 Mild 37 20 57 24 3 10 Moderate 33 18 83 35 7 23 Severe 6 3 31 13 2 7 Missing 1 — 5 — 0 — Hippocampus: diffuse plaques None 101 56 89 40 13 43 Mild 47 26 70 32 11 37 Moderate 26 15 53 24 5 17 Severe 5 3 10 5 1 3 Missing 4 — 21 9 0 — Hippocampus: NFT None 34 19 12 5 5 17 Mild 68 37 39 16 11 37 Moderate 53 29 72 30 9 30 Severe 27 15 115 48 5 3 Missing 1 — 5 — 0 0 Hippocampus: atrophy None 117 69 71 33 16 57 Mild 33 19 47 22 9 32 Moderate 17 11 78 36 2 7 Severe 2 1 18 8 1 4 Missing 14 — 29 — 2 2 Entorhinal cortex: neuritic plaques None 100 55 61 26 17 57 Mild 49 27 64 27 9 30 Moderate 28 15 75 32 3 10 Severe 5 3 35 15 1 3 Missing 1 — 8 — 0 — Entorhinal cortex: diffuse plaques None 81 46 51 23 10 33 Mild 42 24 58 26 10 33 Moderate 44 25 73 33 7 23 Severe 11 6 37 17 3 10 Missing 5 — 24 — 0 — Entorhinal cortex: NFT None 27 15 7 3 5 17 Mild 55 30 30 13 9 30 Moderate 77 43 98 42 10 33 Severe 22 12 100 43 6 20 Missing 2 — 8 — 0 — Lewy bodies — 10 5 35 14 3 10 Brain weight kg – median — 1.24 — 1.11 — 1.15 — Age at death (y) 1 1.2 0.8–1.7 0.5 — — — — Brain weight for sex (g) Low 5.7 3.2–10 18 (three with MMSE >26) when they were last measured, including five (45%) who had a RINI. Median time from interview to death in those without a RINI interview was 12 mo. Only two had symptomatic cognitive impairment, but not consistently, and one had depression. The pathologies exhibited by these individuals were SVD (n = 11), low brain weight (n = 8), atrophy (n = 7), severe plaques (n = 5), and moderate NFT (n = 4). Conversely 68 individuals (M∶F 24∶44; age at death, 71–103 y) had a dementia diagnosis before death but showed only modest neuropathology. Sixty (88%) showed moderate or severe cognitive impairment before death. Of the other eight all had died at least 15 mo after the last interview and dementia was confirmed by RINI (n = 6) or death certificate (n = 3). The neuropathology was generally mild and included Lewy bodies (n = 4). Two individuals had severe atrophy of the hippocampus. Neuropathology in the brainstem, not included in the model, was present in 36 individuals (NFT, n = 24; plaques, n = 4; neuronal loss, n = 15). These factors did not improve the overall model when tested across all individuals. Other neuropathological findings in these individuals include Progressive Supranuclear Palsy, hippocampal hypoxic injury, head injury, and mesial temporal sclerosis. The outcome of interest in this analysis was dementia and it therefore does not address cognitive impairment short of dementia in which the factors reported here would also be expected to play a role. AR of Dementia for Pathological Features The risk of dementia associated with specific thresholds of pathology is shown in Table 5. Each estimated AR at death adjusts for all others such that 96% of the overall risk is explained. Nearly 20% of this risk is due to the effect of age. Factors conveying more than 8% each of the dementia risk were: NFT in the neocortex; age; neuritic plaques; SVD; moderate/severe atrophy; low brain weight. Alzheimer pathologies together (plaques, tangles, and CAA) account for ∼25% of dementia risk, and vascular pathologies ∼21%. Other neuropathological factors each convey between 2%–5% of the risk. Neuropathology in the Nondemented Many nondemented individuals manifest “high risk” pathologies. A moderate NFT score in the neocortex is rare (4%), and a severe NFT score absent, but multiple vascular disease (24%) and SVD (47%) are common. Neuropathological factors, age, and brain weight only account for 34% of the variability within the model, despite high estimates of AR. This apparent anomaly underscores their relatively poor predictive value in making a diagnosis of dementia. Prediction from Neuropathology Alone Univariate modelling of the relationship between dementia and neuropathological findings, excluding age and brain weight, showed a large additional risk associated with having NFT in the hippocampus. However this adjusted out in multivariable analysis. The model based on neuropathology data alone has higher sensitivity (83%), but lower specificity (76%). From the 399 in this model, 80% were correctly classified as either demented or not. The AR at death for neuropathological features was modified only slightly by excluding age and brain weight. The major contributor to dementia risk remained NFT (28%; neocortical, 14%; hippocampal, 14%). Atrophy (20%) and CAA (11%) were more important. Vascular factors (17%), neocortical plaques (7%), and Lewy bodies (4%) remained the same. No interactions were detected. Sensitivity Analysis Imputation of variables with missing data was used to test the robustness of the model against both missing outcome variables (30 individuals whose dementia status was not coded) and pathology variables. The multivariable modelling after imputation showed few differences from the original model in Table 5. The neuropathology factors chosen to be represented in the model were checked using ten imputation datasets. Factors associated with dementia were remarkably stable within each imputation dataset. The only factors that appeared to differ were whether Lewy bodies (excluded from five datasets), severe plaques (excluded from four datasets), age (excluded from two datasets), and hippocampal atrophy (excluded from one dataset) should be included in the model. Two factors that were not previously in the models became important: NFT in the hippocampus and neuronal loss in the brainstem. Neuronal loss in the brainstem appeared important in individuals previously misclassified, but did not improve the model using the original data where there was missing data in the covariates and outcome variable. The full model with all these factors is shown in Table 6. The estimations of AR at death were very similar for the imputation datasets. The inclusion of brain stem neuronal loss (AR 13%) and NFT in the hippocampus (AR 5%) emerged from small reductions (1%) in the majority of factors though hippocampal atrophy (8% to 4%) and old age (11% to 8%) were more affected. Analysis adjusting for demographic differences between the brain donor cohort and the rest of the population that died showed only slight change in AR at death for factors most associated with older age (old age, atrophy, and neocortical NFT), whilst vascular disease, low brain weight, plaques, and CAA all showed small increases (1%–2%). Only low brain weight (from 11% to 18%) and atrophy (from 8% to 2%) were affected by the age difference between the donor cohort and all those who died in the population. A further sensitivity analysis only in those assessed less than 1 y prior to death was undertaken and all associations increased in strength, suggesting any bias is conservative, and all AR estimates were consistent with the confidence intervals presented. 10.1371/journal.pmed.1000180.t006 Table 6 Sensitivity analysis: Imputation models. Neuropathological Findings Multivariable Model Original Imputed Imputation Model OR 95%CI OR 95% CI OR 95%CI AR 95% CI Age at death <80 y 1.0 — 1.0 — 1.0 — — — 80–89 y 2.5 1.1–5.8 2.3 1.1–4.8 2.1 1.0–4.5 7 1–14 ≥90 y 3.4 1.4–8.3 4.3 2.0–9.5 4.2 1.8–9.6 8 2–16 Brain weight for sex Low 4.1 1.9–9.2 4.3 2.0–9.2 4.3 1.9–9.6 12 5–19 Average 2.1 1.0–4.2 1.8 0.9–3.4 1.7 0.9–3.4 4 0–9 High 1.0 — 1.0 — 1.0 — — — Neuritic plaques in neocortex None or mild 1.0 — 1.0 — 1.0 — — — Moderate or severe 9.7 2.1–43 4.4 1.6–12 3.9 1.3–11 7 3–17 Tangles in neocortex None 1.0 — 1.0 — 1.0 — — — Mild 1.0 0.5–1.8 1.0 0.6–1.8 0.7 0.4–1.4 — — Moderate or severe 7.1 2.3–22 6.3 2.6–15 4.6 1.8–12 10 5–17 Tangles in hippocampus None or mild — — — — 1.0 — — — Moderate or severe Not included — Not included — 1.8 1.0–3.3 5 0–12 CAA None 1.0 — 1.0 — 1.0 — — — Mild 1.8 0.8–3.8 1.7 0.9–3.3 1.5 0.7–3.0 — — Moderate or severe 2.9 1.2–6.8 3.5 1.7–7.4 3.8 1.7–8.2 4 0–9 Lewy bodies No 1.0 — 1.0 — 1.0 — — — Yes 3.5 1.3–9.3 3.7 1.6–8.9 2.2 0.9–5.7 2 0–5 Overall vascular pathology None 1.0 — 1.0 — 1.0 — — — Infarcts/haemorrhage 2.4 0.4–12 1.9 0.3–11 1.4 0.2–9.4 — — SVD/DWML/lacunes 3.7 1.5,9.6 3.1 1.4–6.9 3.3 1.4–7.5 — — Both 4.8 1.9–12 4.0 1.8–9.2 4.2 1.8–9.9 20 8–33 Hippocampal atrophy None 1.0 — 1.0 1.0 1.0 — — — Mild 1.8 0.9,3.7 1.5 0.8–2.9 1.3 0.6–2.6 — — Moderate 3.4 1.5–7.5 2.8 1.4–5.6 1.9 0.9–4.2 4 0–10 Severe — — — — — — — — Brainstem neuronal loss None — — — — 1.0 1.0 — — Mild — — — — 2.7 1.5–4.9 6 1–12 Moderate — — — — 3.3 1.4–8.0 7 0–13 Severe Not included — Not included — 9.9 1.8–54 7 0–13 Discussion MRC CFAS shows that it is possible to set up and sustain a brain donation programme from a geographically dispersed, population-based study, which is not biased in terms of gender, social class, education, institutionalisation, or access to health care. The resulting brain donor sample is of sufficient size to generate meaningful estimates of AR at death associated with specific pathologies and contributes significantly to understanding the pathobiology of dementia on the basis of “epidemiological neuropathology.” It also allows the separation of factors that might be amenable to modification from others that may not. The main contributors to AR at death for dementia in MRC CFAS were age (18%), small brain (12%), neocortical neuritic plaques (8%) and neurofibrillary tangles (11%), small vessel disease (12%), multiple vascular pathologies (9%), and hippocampal atrophy (10%). Other significant factors include cerebral amyloid angiopathy (7%) and Lewy bodies (3%). Earlier CFAS analysis showed that Alzheimer pathology and vascular disease are frequently found in both demented and nondemented people [7]. In the present more detailed analysis, with larger numbers, a moderate or severe neocortical NFT score emerged as the best pathological discriminator between the demented and nondemented groups. SVD emerged as an independent contributor to dementia risk in keeping with the evidence that SVD is the substrate for “subcortical vascular dementia” and contributes a major part of the burden of vascular cognitive impairment in the population [19]. We included deep subcortical WMLs within the vascular disease variable on the basis of evidence that they arise through vascular mechanisms [20]. The estimates of AR at death reveal the relative importance of conventional pathological measures at the population level and show a range of pathological features contributing independently to dementia. The major independent effects of age and relative low brain weight are interesting. The findings imply that other factors, not captured in this standardised approach to pathological analysis, are determinants of cognitive trajectory in older people. These may include synaptic integrity and the concentrations of peptide oligomers [21],[22] but also interindividual variation in diverse factors that determine the neurobiological basis of “brain reserve,” both innate (synaptic and neuronal density achieved into adult life, potential for neurogenesis, synaptic plasticity) and acquired (educational attainment, sustained intellectual, social, or physical activity in mid-life and old age) [23]. Limitations of the Study In these six population samples from England and Wales the baseline response rate was good and unlikely to have been severely biased. Considerable attrition over time determines that those who remain in the study tend to have been younger and fitter at enrolment [24]. The cohort reported here is based on individuals who were selected for more detailed assessment at the baseline and year 2 waves. Selection to this group is weighted towards the cognitively impaired, but with random selection from the full population, and created an older sample than the remainder of the baseline sample who died within the study period. Causes of death were similar in the two groups. Because the characteristics of both samples are known, a sensitivity analysis backweighting for this process (and for biases arising from selection into the neuropathology cohort) adequately adjust for these sampling effects, though not for unknown biases. The number of interviews achieved for each individual during the study varied (96% had at least two and 30% had five or more). The AGECAT algorithm, applied to the data at last interview, has been validated against clinical diagnoses and shown to be comparable to Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised (DSM-IIIR) [25]. Fieldwork interviews were rarely started in acutely ill individuals so that diagnoses are unlikely to be influenced by confusional states. Although dementia on death certificates, insensitive but highly specific, was used to find incident dementia between last interview and death it was not used to indicate that an individual was not demented [26],[27]. Clinical judgements from informant reports were based on DSM-III-R, consistent with the previous validation studies using the GMS instrument. The extent of misclassification that would be necessary to create the findings observed here would need to be extreme and, when including only those with most recent interview data, the results are not affected. Our previous report, clinically allocating differential diagnoses with a predominance of mixed pathology, remains robust [7]. The factors identified in this analysis coexist and may interact mechanistically. Our analysis does not allow us to elucidate causal directions, which are better investigated using longitudinal analysis and in experimental work. A formal analysis of interactions between vascular and degenerative pathologies will be reported separately but the models here did not reveal interactions. The low prevalence of Lewy bodies in this sample (∼10%), reflects methods that were not optimised for the detection and screening of α-synucleinopathy because the neuropathology protocol predates the recognition of dementia with Lewy bodies, and the discovery of α-synuclein in Lewy bodies. Recent data on a subset of this cohort show synucleinopathy in 37% but no strong association with dementia [28]. Our data on brain weight are based on comparisons within sex but this measure does not distinguish atrophy from innate smallness, an issue that can only be addressed by systematic measurement of total cranial volume. Nor does it distinguish the contribution of vascular and neurodegenerative processes, or other correlates such as synaptic and dendritic loss that were not routinely measured in the pathology protocol. Standardised and validated assessment of vascular pathology is also needed in studies of the pathological correlates of dementia [29]. Perhaps the greatest difficulty in interpreting these data is that they derive from individuals who have died. People with dementia live for a variable length of time during which burdens of neuropathology are assumed to change. To extrapolate from this sample to an equivalent cross section of the living older population is problematic but, in the absence of methods to achieve in vivo measurement of all pathologies, this is the closest estimate it is currently possible to produce. In due course these data can be combined with modelling of in vivo population pathology derived from techniques to assess vascular and neuropathological changes (e.g., amyloid positron emission tomography [PET] scans). The pathological features that are associated with dementia in this analysis are well supported by data from other large community-based and population-based studies. There is general agreement from studies of older people in the UK and the US that dementia is predominantly associated with mixed vascular and Alzheimer lesions together with other contributions of lesser degree (e.g., synucleinopathy) [30]–[32]. Those studies also contribute important insights into the potential interactions of vascular and degenerative pathologies that are not dealt with in the present analysis [30]–[35]. Some studies have emphasised the significance of microscopic infarcts compared to macroscopic infarcts in explaining the relationship between pathology and dementia [30],[32],[34], whereas others have not demonstrated an independent association of dementia risk with microscopic infarction [31],[35]. In the present analysis we did not treat microinfarcts as a single pathological variable. Rather we chose to incorporate them into a global assessment of significant intrinsic SVD that also included microscopic evidence of severe arteriolar sclerosis and the presence of severe white matter attenuation. The Adult Changes in Thought study (ACT) has estimated the OR for dementia associated with Lewy bodies to be 5.1 (95% CI 1.37–18.96) on the basis of α-synuclein immunocytochemistry compared to 3.5 (95% CI 1.3–9.3) in this study using a less reliable method of detection. The estimates of AR at death for Lewy body pathology are 10% in ACT and 3% in MRC CFAS as reported here. In a subgroup of this CFAS cohort we demonstrated synucleinopathy in 37% of donated brains [28]. Other large cohorts have reported no clear predictive relationship between Lewy body pathology and dementia [36]. Interpretation of data on Lewy body pathology in published multivariable analyses is further complicated by the recent recognition of “amygdala predominant disease” that may not be reliably detected using some screening protocols. Another pathology recently emphasised in older people is hippocampal sclerosis (HS), which has been shown to contribute a relative risk for dementia of 2.43 (95% CI 1.01–5.85). This is a microscopic diagnosis that was not included as a separate variable in our data. While we did include macroscopic hippocampal atrophy, and found that it contributes 10% of the AR at death for dementia, it is important for future studies to determine the correlation between macroscopic changes and the microscopic features of HS, which are also not yet the subject of diagnostic consensus or interlaboratory validation. The present study supports the view that interventions that modify neuropathology related to dysmetabolism of specific proteins (βA4, tau) have the potential to impact on the population burden of dementia. In the context of presymptomatic treatment many individuals without risk of developing dementia would also be treated unless the predictive ability of clinical tests improves dramatically. However the estimates in this analysis indicate that individual pathologies contribute only modestly to the overall risk of dementia and emphasise the need to develop a range of protective strategies. Other factors, potentially less amenable to intervention play a role including age, and underlying innate or acquired factors relating to brain reserve, which, along with the effects of multiple pathological comorbidities, all play a part in the manifestation of dementia at the level of the population as a whole.