Article Figures & Data
Figures
- Figure Stages of data collection and sample size of dietary and MRI data in the Lothian Birth Cohort of 1936
Tables
Data Supplement
Neurology® data supplements are not copyedited before publication. Published editorials and translations have been copyedited.
© 2017 American Academy of Neurology.
Files in this Data Supplement:- Data Supplement - Microsoft Word file
- Michelle Luciano, PhD,
- Janie Corley, PhD,
- Simon R. Cox, PhD,
- Maria C. Valdés Hernández, PhD,
- Leone C.A. Craig, PhD,
- David Alexander Dickie, PhD,
- Sherif Karama, MD, PhD,
- Geraldine M. McNeill, PhD,
- Mark E. Bastin, PhD,
- Joanna M. Wardlaw, MD, FRCP, FRCR and
- Ian J. Deary, PhD
- From the Centre for Cognitive Ageing and Cognitive Epidemiology (M.L., J.C., S.R.C., I.J.D.), Department of Psychology (M.L., J.C., S.R.C., I.J.D.), Brain Research Imaging Centre (M.C.V.H., D.A.D., M.E.B., J.M.W.), Centre for Clinical Brain Sciences (M.C.V.H., D.A.D., M.E.B., J.M.W.), and Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences (S.R.C., M.C.V.H., D.A.D., M.E.B., J.M.W.), University of Edinburgh; Rowett Institute of Nutrition and Health (L.C.A.C.) and The Institute of Applied Health Sciences (G.M.M.), University of Aberdeen, UK; and Montreal Neurological Institute and Hospital and Douglas Mental Health University Institute (S.K.), McGill University, Canada.
- Correspondence to Dr. Luciano: michelle.luciano{at}ed.ac.uk
Abstract
Objective: To assess the association between Mediterranean-type diet (MeDi) and change in brain MRI volumetric measures and mean cortical thickness across a 3-year period in older age (73–76 years).
Methods: We focused on 2 longitudinal brain volumes (total and gray matter; n = 401 and 398, respectively) plus a longitudinal measurement of cortical thickness (n = 323), for which the previous cross-sectional evidence of an association with the MeDi was strongest. Adherence to the MeDi was calculated from data gathered from a food frequency questionnaire at age 70, 3 years prior to the baseline imaging data collection.
Results: In regression models adjusting for relevant demographic and physical health indicators, we found that lower adherence to the MeDi was associated with greater 3-year reduction in total brain volume (explaining 0.5% of variance, p < 0.05). This effect was half the size of the largest covariate effect (i.e., age). Cross-sectional associations between MeDi and baseline MRI measures in 562 participants were not significant. Targeted analyses of meat and fish consumption did not replicate previous associations with total brain volume or total gray matter volume.
Conclusions: Lower adherence to the MeDi in an older Scottish cohort is predictive of total brain atrophy over a 3-year interval. Fish and meat consumption does not drive this change, suggesting that other components of the MeDi or, possibly, all of its components in combination are responsible for the association.
GLOSSARY
- BMI=
- body mass index;
- GMV=
- gray matter volume;
- ICV=
- intracranial volume;
- MeDi=
- Mediterranean-type diet;
- MMSE=
- Mini-Mental State Examination;
- NART=
- National Adult Reading Test;
- TBV=
- total brain volume
Understanding the contributors to healthy aging is an important task, especially if such contributors are modifiable. Diet is modifiable and literature documenting the relationship between a healthy diet and good physical health abounds.1 One focus of dietary research has been on the potential merits of a Mediterranean-type diet (MeDi): high consumption of fruit, vegetables, legumes, and cereals, olive oil as the primary source of fat, moderate consumption of fish, low to moderate intake of dairy products and wine (accompanying meals), and low intake of red meat and poultry. Increased adherence to the MeDi has been linked with lower inflammation, better cognitive function, and reduced risk of Parkinson disease and Alzheimer disease and mortality from cardiovascular disease and cancer.2,–,6
Studies of older individuals have shown associations between higher adherence to the MeDi and larger MRI-based brain volumes and cortical thickness.7,–,9 The largest study7 found that closer adherence to the MeDi in 468 multiethnic Americans without mild cognitive impairment (mean 80.1 years) was associated with larger (intracranial volume-adjusted) brain volumes. When examining individual food groups, their results indicated that higher fish and lower meat intake were the primary contributors to the observed effects on brain structure. Because their sample was cross-sectional, they could not test for an association with brain loss over time. In the present study, structural brain imaging data were available at 2 timepoints over an interval of roughly 3 years, enabling us to measure the prospective association between the MeDi and meat and fish consumption on multiple indices of brain structural aging.
METHODS
Participants.
The Lothian Birth Cohort of 1936 comprises 1,091 participants (49.8% women), all born in 1936, in the Edinburgh region of Scotland, most of whom had completed a mental ability test when they were aged 11 years.10 They were aged approximately 70 years at the baseline collection of cognitive and health data. Dietary data were collected at baseline (wave 1) by postal return of questionnaire in 967 participants: 124 were returned blank or not at all. Approximately 3 and 6 years later—waves 2 and 3—the still-available, able, and willing participants underwent a structural MRI brain scan; of those with dietary data, volumetric data were gathered for 562 individuals at wave 2 and 401 at wave 311; see the figure. Age at first MRI scan was 72.65 (±0.72) years and age at second brain scan was 76.36 (±0.64) years; henceforth, these are referred to as 73 and 76, respectively. Individuals lived independently in the community, were relatively healthy, and were free from a diagnosis of dementia at recruitment. Cognitive assessments were made at all 3 timepoints; that is, 2004–2007 (wave 1), 2007–2010 (wave 2), and 2011–2014 (wave 3).
Standard protocol approvals, registrations, and patient consents.
Ethics permission was obtained from the Multi-Centre Research Ethics Committee for Scotland (wave 1: MREC/01/0/56), the Lothian Research Ethics Committee (wave 1: LREC/2003/2/29), and the Scotland A Research Ethics Committee (waves 2, 3: 07/MRE00/58).
Mediterranean diet.
Data from the Scottish Collaborative Group 168-item Food Frequency Questionnaire, version 7,12,13 were used to construct the MeDi score. Exclusions were made for incomplete data (39 had >10 missing items) and for individuals with extreme energy intakes (<2.5th or >97.5th centile, n = 46) to obtain the most reliable food frequency data.12 For scoring of the MeDi, we closely followed accepted procedure.14 Briefly, individuals were given a value of 1 for each beneficial food component (fruit, vegetables, legumes, cereal, and fish) and a value of 0 for each detrimental component (meat, dairy). The ratio of daily consumption (in grams) of monounsaturated fatty acids to saturated fatty acids was a further beneficial component. Caloric-adjusted sex-specific medians were used as the boundary defining low and high consumption for each of the components. For beneficial components, scores at or above the median were assigned a value of 1, whereas for detrimental components, scores at or above the median were given a value of 0. Moderate alcohol consumption was another positively scored component. It was defined for men as between 10 and 50 g alcohol per day and for women between 5 and 25 g per day. The MeDi score (range 0–9) was calculated by summing the scores for each of the components, with higher scores indicating higher MeDi adherence.
Covariates.
Education was defined in terms of number of years of full-time education completed. At each test wave, height and weight were clinically measured (to calculate body mass index [BMI]), medical history (stroke, cardiovascular disease, high blood pressure, diabetes) was self-reported at interview, general cognitive ability was indexed by the first unrotated principal component from a battery of cognitive tests,15 premorbid IQ was estimated by the National Adult Reading Test (NART),16 and current cognitive impairment was assessed by the Mini-Mental State Examination (MMSE).17 APOE genotyping was performed on genomic DNA isolated from whole blood using TaqMan technology at the Wellcome Trust Clinical Research Facility Genetics Core, Western General Hospital, Edinburgh, UK. APOE ε4 allele absence vs presence was used in this study.
MRI.
The MRI acquisition and processing has been reported previously.11 Briefly, a GE (Cleveland, OH) Signa 1.5 T HDXT clinical scanner was used to collect structural T2-, T2*-, fluid-attenuated inversion recovery–, and T1-weighted brain MRI data at both waves. Intracranial volume (ICV) encompassed matter within the inner skull table containing venous sinuses and with a lower limit demarcated as the axial slice just inferior to the inferior boundary of the cerebellar tonsils and on or superior to the superior tip of the odontoid process. A semi-automatic method using T2*-weighted sequence was used to measure ICV with a first estimate gathered automatically using Analyze 9.0 (via the Object Extraction Tool). The cervical spinal cord inferior to the inferior boundary was then manually removed with the pituitary gland (if it had been included). Total brain volume (TBV) represented the difference in volume of the CSF, venous sinuses and meninges, and ICV. Gray matter volume (GMV) was extracted by subtracting CSF, white matter, and white matter hyperintensities from ICV. Variation in head size between individuals was controlled for by using residual scores from the regression of ICV on brain volumes, hereafter referred to as ICV-corrected. We measured cortical thickness using the fully automated CIVET image processing pipeline and qualitative quality control procedures developed at the Montreal Neurologic Institute.18 CIVET measures cortical thickness at 81,924 vertices (points where the perpendicular distance between gray matter and white matter surfaces is measured) across the cortex. For clarity, we refer to vertex as the points where cortical thickness is measured, not the cranial vertex. We calculated mean cortical thickness for each participant as the mean of the 81,924 vertex values.
Statistical analysis.
The main effect of MeDi on brain MRI measures at ages 73 and 76 years was tested using general linear regression where age at MRI testing and sex were controlled for in model 1, and where body mass index (BMI), diabetes, high blood pressure, history of stroke, cardiovascular disease history, years of education, and premorbid IQ were additionally controlled for in model 2. These covariates each showed a significant correlation with at least one of the dependent MRI measures. Education and premorbid IQ (which correlates 0.68 with actual childhood IQ in our sample19 but was preferred due to nonmissing data) were considered as potential confounders because they might contribute to differences in diet.20 Conversely, current cognitive impairment (Mini-Mental State Examination [MMSE]) and general cognitive ability were measured subsequent to diet, and are partly associated with brain change in this sample21; therefore, by adjusting for them, we would remove variation related to brain atrophy. In model 3, we included covariates that aligned best with the largest previously reported analysis of the MeDi and brain MRI measures7; these were age at MRI, sex, education, BMI, diabetes, general cognitive ability, and MMSE (to control for mild cognitive impairment). The covariates showed no sign of multicollinearity: the cognitive and education variables showed a maximum correlation of 0.53 with each other, and the variance inflation factor of the models was acceptable. APOE ε4 presence, which might relate to variation in brain volume and cortical thickness,22,23 was controlled for in supplementary analysis.
For change in MRI measures over time, the same models were repeated using the later MRI residual score (corrected for ICV) as the outcome variable but including the earlier MRI residual score (corrected for age and ICV) as a covariate in all models. This had the effect of removing stable MRI variance across the 2 timepoints, so that the remaining variance in the second MRI outcome measure reflected change and uncorrelated measurement error. The effect of meat and fish consumption on TBV and GMV was further tested to replicate previous findings.7,9,24 For the analysis of MRI measures on level scores (ages 73 and 76 years), all covariates collected at that testing wave were used, with the exception of the NART, where the baseline (age 70) measure was considered to be the better estimate of premorbid IQ. For the analysis of MRI change, covariates collected at the later (age 76 years) brain scanning occasion were used, but again with the exception of the NART, where the baseline (age 70) measure was preferred.
RESULTS
Table 1 shows the demographic, health, and cognitive profile of the full sample and grouped by lower and higher adherence to the MeDi at baseline collection. Whereas we provide significance tests for lower vs higher adherence to the MeDi, such results do not consider confounding from relevant covariates. They are shown primarily for comparison with a previous report7; regression analyses are performed using the continuous MeDi scale. The group with greater adherence to the MeDi had more carriers of APOE ε4 alleles, and they showed greater TBV and GMV at age 73 years and greater GMV and mean cortical thickness at age 76 years. To test for bias due to sample attrition at wave 3, we compared the mean quantitative MeDi score between those who returned for wave 3 testing (n = 402) vs those who did not (n = 161). No significant difference was found (t = −0.42, df = 300.81, p = 0.67; mean difference of 0.06), nor were differences observed for demographic, cognitive, and health traits, except cognitive ability, where scores were higher (≈0.18 of a SD) for individuals completing both testing waves (p = 0.046). See table e-1 at Neurology.org for detailed comparisons.
Demographic, health, cognitive, and brain MRI profile of the full cohort and grouped by lower vs higher adherence to the Mediterranean-type diet (MeDi)
In the fully adjusted linear regression models (table 2), the only variable significantly associated with the MeDi was change in TBV (significant in all 3 models). Subsequent analyses of TBV models 2 and 3 adjusting for APOE ε4 status produced a larger effect size, although this was based on 16 fewer cases due to missing APOE genotyping. The main models 2 and 3 showed respective MeDi standardized effects of 0.068 and 0.058, but, when controlling for APOE ε4 presence, effects increased to 0.075 and 0.066, respectively. In model 2, the MeDi effect was the largest after age at MRI and sex and, in model 3, it showed the fifth largest effect. Specific effects of meat and fish consumption on TBV and GMV level (age 73 years) and change (age 76 controlling for age 73) were nonsignificant (table 3), even when accounting for APOE ε4 status.
Unstandardized β coefficients (SE) and p values for the effect of the Mediterranean-type diet on structural brain MRI measures from model 1 (adjusted for age and sex), model 2 (adjusted for age, sex, body mass index, diabetes, stroke, blood pressure, cardiovascular disease, education, and National Adult Reading Test), and model 3 (adjusted for age, sex, education, body mass index, diabetes, cognitive ability, and Mini-Mental State Examination)
Unstandardized β coefficients (SE) and p value for the effects of meat and fish consumption on level (age 73 years) and change (from age 73 to 76 years) in total brain volume and gray matter volume for model 1 (adjusted for age and sex), model 2 (adjusted for age, sex, body mass index, diabetes, stroke, blood pressure, cardiovascular disease, education, and National Adult Reading Test), and model 3 (adjusted for age, sex, education, body mass index, diabetes, cognitive ability, and Mini-Mental State Examination)
DISCUSSION
Our study investigated MeDi adherence and brain change in older age. We found that greater adherence to the MeDi was associated with lower total brain atrophy over a 3-year period. MeDi was not associated with brain volume or mean cortical thickness measured at ages 73 and 76 years. Fish and meat consumption were not associated with TBV or GMV. Our finding might indicate that greater adherence to the MeDi is protective against total brain atrophy, explaining 0.5% of variance, an effect that was half the size of that due to normal aging in this sample. This effect was not detectable for GMV, suggesting that the effect on TBV was not a consequence of a larger specific influence on gray matter.
Our study also served as a replication of 3 previous studies that measured the effect of the MeDi on MRI measures at a single time point, so it is important to compare their results. Whereas associations between increased adherence to the MeDi and larger TBV, GMV, and WMV were previously reported,7 null effects were also found for these same measures in cognitively healthy 75-year-olds from Sweden.9 Using voxel-based morphometry, null findings for gray and white matter volumes in 146 older (≥65 years) French participants were also reported.25 Our findings are in line with the latter studies, which also focused on ethnically and age homogenous samples. The association between increased adherence to the MeDi and greater cortical thickness reported in a younger (mean age of 54 years) American sample of 52 cognitively healthy individuals8 was not replicated in 2 studies (including the present study)7 that were almost 10 times larger.
Whereas 2 previous studies7,9 found that lower consumption of meat was associated with greater TBV (and also GMV7), we do not replicate this effect. Without information on the distribution of meat consumption in these samples, we cannot speculate whether the difference is due to less variation in the quantity of meat consumed or deviation in the type (e.g., poultry vs red; fresh vs processed) of meat consumed in our sample. The previous association between increased fish consumption and larger GMV7 was again not replicated here. Differences in the type of fish (oily vs white) consumed between populations might be a factor for the discrepancy in results. With regard to total brain atrophy in our Scottish sample, low meat and high fish intake does not underlie the observed effect of the MeDi. It might be the case that other specific dietary components have direct functional effects on total brain atrophy, but given the interdependency of dietary components, the task of identifying specific foods and potentially nutrients with such an effect is formidable.26 Moreover, it has been argued that it is the additive effects of the contributing food components and the ways in which they interact (e.g., through food preparation) that offer increased benefits to health.27
In our sample, the APOE ε4 allele (Alzheimer disease risk allele) frequency was higher in those who adhered more to the MeDi. None of our sample had been diagnosed with Alzheimer disease so disease progression was unlikely having an influence on diet. In our sensitivity analysis, which additionally adjusted for APOE ε4 status, the effect of the MeDi on total brain atrophy became slightly larger. Interestingly, the effect size that we observed for MeDi was larger in the model without adjustment for current cognitive ability and potential dementia, an adjustment that removes part of the variance we are interested in predicting.
The strengths of our study were (1) the sample's age and ethnic homogeneity, which reduced associated confounding with brain change and diet; (2) the collection of MeDi 3 years prior to imaging measures, so that prospective, longer term effects could be evaluated; and (3) the availability of longitudinal imaging data to measure change in brain MRI measures. Potentially limiting is the relatively short time period between brain imaging measurements; a 3-year interval will not tap as much variation in brain change as would much longer intervals, resulting in a smaller detectable effect. Similarly, participants with better general cognitive ability completed repeat MRI testing, which may have constrained variation in associated brain volumes. A correction for multiple testing would deem our result nonsignificant, but the predictor variables were not independent and neither were the brain MRI outcome variables, thus, a correction for number of analyses performed would result in an overly conservative significance criterion. We prefer to focus on the effect size of our novel finding rather than the p value; additional studies, and ensuing meta-analyses, are needed to confirm whether the effect we report is reliable.
Our study shows a prospective protective association of the MeDi with brain atrophy in an ethnically and age-homogenous sample of relatively healthy older adults living in Scotland. Diet was measured 3 years before the first brain imaging measurement, so any neurologic disorders correlated with structural brain deficits are unlikely to be influencing diet choice. This might suggest that the MeDi effect is causal. Given that the effect was significant when controlling for education and premorbid IQ also suggests that the MeDi is not simply a function of healthier lifestyle choice in more educated or intelligent individuals, who tend to have larger brain volumes.28 A previous study of this cohort found that the association between Mediterranean diet (measured by a latent trait variable from a principal components analysis) and cognitive ability in older age was chiefly due to its relationship with childhood IQ.20 Because we sampled diet at a single timepoint, we cannot confirm whether during the period of brain atrophy people were still adhering to the same diet. However, the stability of MeDi scores has been shown to be high over intervals of more than 7 years for those with and without dementia.6 If our sample were not still adhering to this diet, that would only highlight the potentially long-lasting protective effects of the MeDi on brain structure during aging. If the stability of diet stretches back much further, then it is possible that diet sets the course for change in brain volume over time, although it does not affect level, which is presumably influenced more by static characteristics of the individual (e.g., genes). That the effect only operates on change is consistent with heritability studies showing that environmental influences (e.g., diet) on total brain volume change (57%) are larger than on level scores of total brain volume (estimates between 3% and 35%).29,30 We cannot judge whether longer adherence to the MeDi is associated with greater protection against brain atrophy. Future studies incorporating measures of individuals' lifelong adherence to a particular dietary pattern are needed.
AUTHOR CONTRIBUTIONS
Michelle Luciano: specific study concept, data analysis, interpretation, write-up. Janie Corley, Leone C.A. Craig, Geraldine M. McNeill: food frequency data analysis, critical revision of manuscript for intellectual content. David Alexander Dickie, Simon R. Cox, Maria C. Valdés Hernández, Sherif Karama, Mark E. Bastin: imaging processing, critical revision of manuscript for intellectual content. Joanna M. Wardlaw, Ian J. Deary: full study design and concept, study management, critical revision of manuscript for intellectual content.
STUDY FUNDING
The data were collected by a Research into Ageing programme grant; research continues as part of the Age UK–funded Disconnected Mind project. The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1), with funding from the BBSRC and Medical Research Council. Imaging and image analysis was performed at the Brain Research Imaging Centre (sbirc.ed.ac.uk/), Edinburgh, supported by the Scottish Funding Council SINAPSE Collaboration. Derivation of mean cortical thickness measures was funded by the Scottish Funding Council's Postdoctoral and Early Career Researchers Exchange Fund awarded by SINAPSE to David Alexander Dickie. L.C.A.C. acknowledges funding from the Scottish Government's Rural and Environment Science and Analytical Services (RESAS) division.
DISCLOSURE
The authors report no disclosures relevant to the manuscript. Go to Neurology.org for full disclosures.
ACKNOWLEDGMENT
The authors thank the Lothian Birth Cohort of 1936 participants and research staff and Dr. Alan C. Evans for input into the processing of cortical thickness data.
Footnotes
Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article. The Article Processing Charge was paid by the RCUK UK Open Access Fund.
Supplemental data at Neurology.org
- Received April 15, 2016.
- Accepted in final form October 27, 2016.
- © 2017 American Academy of Neurology
This is an open access article distributed under the terms of the Creative Commons Attribution Licence 4.0 (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Research Support, Commercial Entities:NONE
Research Support, Government Entities:GSK consultancy fee (<?2000, paid to my University)
Research Support, Academic Entities:Medical Research Council. ?Centre in Cognitive Ageing and Cognitive Epidemiology?, ?3,413,977 FEC over five years from August 2008. Renewed Sept 2013 ? Aug 2018. I Deary (Centre head), R Logie, J Seckl, J McCulloch, D Porteous, J Wardlaw (Imaging lead). Medical Research Council. ?Brain white matter imaging and cognitive ageing in the Lothian Birth Cohort of 1936:II.? ?764,304, 1st April 2011-31st Mar 2015. Prof IJ Deary (PI), Prof JM Wardlaw, Dr M Bastin, Prof J Starr. EU Joint Programme - Neurodegenerative Disease Research. ?Realising the potential of cohort studies to determine the vascular contribution to neurodegeneration?, ?47,416, 1st Oct 2014 to 31st Mar 2015. J Wardlaw, Medical Research Council. ?Brain white matter imaging and cognitive ageing in the Lothian Birth Cohort of 1936:III? ?1,300,000 (FEC), 31st Mar 2015 to 28th Feb 2019. IJ Deary (PI), JM Wardlaw, J Starr, M Bastin. Technology Strategy Board. ?Quantification of vascular disease burden to stratify dementia for diagnosis and care management?, ?1,048,661 (?306,946 to Edinburgh), 1st Apr 2015 to 31st Mar 2017. R Wolz (Ixico Technologies Ltd.), D Rueckert, J Wardlaw. EU H2020 SVDs@Target ?6million multicollaborator project
Research Support, Foundations and Societies:Scottish Funding Council SPIRIT Scheme. ?Novel and collaborative approaches to knowledge exchange in translational imaging?, ?1,124,928 - 1st Apr 2010-31st Dec 2014. Professor J Wardlaw, Dr J DeWilde, Dr A Murray, Dr R Lerski, Dr J Owen. Ref No. HR09021. SFC: SINAPSE-2 Continued development of the SINAPSE Network?, ?325,000, 31st Oct 2014 to 30th Sep 2019. D Wyer, J Wardlaw, A Murray, D Lerski, K Muir, D Donaldson, A O?Connor. EPSRC. ?Multi-modal retinal biomarkers for vascular dementia: developing enabling image analysis tools.? ?1,100,000, 1st Apr 2015 to 31st Mar 2018. E Trucco, J Wardlaw, T MacGillivray, F Doubal, I Deary, A Doney.
Stock/Stock Options/Board of Directors Compensation:The Row Fogo Trust. ?Research studies in stroke and other common brain diseases?, ?400,000, 1st Apr 2001 to 31st Jul 2009. ?316,000 extension until 2014. Renewed to 2018 ?380,000. Dr JM Wardlaw, Dr I Marshall, Dr M Bastin and the Stroke Research Group. Ref No: AD.ROW4.35, R35865. Age UK. ?The Disconnected Mind?, ?2,500,000 over 5 years from 1st April 2011. Deary, I.J., & Starr, J.M., & Wardlaw, J.M.
License Fee Payments, Technology or Inventions:NONE
Royalty Payments, Technology or Inventions:NONE
Stock/Stock Options, Research Sponsor:NONE
Stock/Stock Options, Medical Equipment & Materials:NONE
Legal Proceedings:NONE
NONE
Gifts:Member, Executive Committee and Steering Committee of the Medical Research Council Dementia Platform, 2013 to 2016. Member, Medical Research Council Cross-Board Advisory Group, 2011 to 2014. Member, Medical Research Council Population Health Sciences Group, 2011 to 2014. Member, Medical Research Council Neurosciences and Mental Health Board, 2011 to 2014.
Funding for Travel or Speaker Honoraria:NONE
Editorial Boards:NONE
Patents:Book Review Editor and Member, Editorial Board, Intelligence 1999 to 2015. Member, Editorial Board, European Journal of Personality 1996 to date. Member, Editorial Board, Personality and Individual Differences 1997 to date. Member, Editorial Advisory Board, Journal of Psychosomatic Research 1998 to date. Member, Board of Consulting Editors, Spanish Journal of Psychology 2002 to date. Member, Editorial Board, European Journal of Psychological Assessment 2005 to date. Member, Editorial Board, Psychological Science 2010 to date. Member, Editorial Board, Longitudinal and Life Course Studies, 2010 to date.
Publishing Royalties:NONE
Employment, Commercial Entity:Deary, I. J., Whalley, L. J., & Starr, J. M. (2009). A lifetime of intelligence: follow-up studies of the Scottish Mental Surveys of 1932 and 1947. Washington, DC: American Psychological Association. Matthews, G., Deary, I. J. & Whiteman, M. C. (2009). Personality traits (third edition). Cambridge: Cambridge University Press. (Translated into Romanian [2005].) Deary, I. J. (2001). Intelligence: A very short introduction. Oxford: Oxford University Press. (Translated into Arabic [2005], German [2013], Hebrew [2006], Hungarian [2003], Japanese [2004], Korean [2005], Polish [2012], Portuguese [2006] Romanian [2008], and Spanish [2004].)
Consultancies:NONE
Speakers' Bureaus:NONE
Other Activities:NONE
Clinical Procedures or Imaging Studies:NONE
Research Support, Commercial Entities:NONE
Research Support, Government Entities:NONE
Research Support, Academic Entities:Deary, I. J., Bastin, M., Starr, J. M., Wardlaw, J. M., & Penke, L. Brain white matter imaging and cognitive ageing in the Lothian Birth Cohort 1936: II. Medical Research Council. ?808,224. April 2011-March 2014. (G1001245.) Deary, I. J. Grant to establish Medical Research Council Centre for Cognitive Ageing and Cognitive Epidemiology (Director I. J. Deary). Medical Research Council. ?3,400,000 approx. August 2008-July 2013 (G0700704/84698.) Deary, I. J. Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2. Medical Research Council and Biotechnology and Biological Sciences Research Council. ?4,258,772. September 2013-August 2018. (MR/K026992/1). Deary, I. J., Luciano, M., Prendergast, J., Wardlaw, J. M., Bastin, M., &Starr, J. M. JPND: Brain Imaging, cognition, Dementia and next generation Genomics. Medical Research Council. ?592,325. May 2016-April 2019. (MR/N027558/1.)
Research Support, Foundations and Societies:NONE
Stock/Stock Options/Board of Directors Compensation:Deary, I. J., & Starr, J. M., & Wardlaw, J. M. The Disconnected Mind, Phase 2. Age UK. ?2,500,000. April 2011-March 2016. Deary, I. J., Starr, J. M., Wardlaw, J. M, & Bastin, M. E. The Disconnected Mind, Phase 3. Age UK. ?1,317,193. April 2016-March 2019.
License Fee Payments, Technology or Inventions:NONE
Royalty Payments, Technology or Inventions:NONE
Stock/Stock Options, Research Sponsor:NONE
Stock/Stock Options, Medical Equipment & Materials:NONE
Legal Proceedings:NONE
NONE