Diet All study participants (n=1091) were asked to complete the

971 Received March 15, 2017 Accepted for publication May 16, 2017

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Introduction

Iodine is one of the three key micronutrient for which deficiency is highlighted as a major public health issue by the World Health Organisation, and the most preventable cause of mental retardation and brain damage (1). While the role of iodine in neurodevelopment has become better understood in early life, there is little evidence available regarding the lifelong impact of iodine on brain function. European countries are usually assumed to have sufficient dietary iodine intake, but the UK has been classified as insufficient (2, 3). This is a particular threat to pregnant women and their offspring, since insufficient early exposure to iodine leads to blunted mental capacity. Indeed, the offspring of mothers taking part in the ALSPAC study (www.bristol.ac.uk/alspac/) had lower IQ at age 8 if maternal iodine in pregnancy had been in the lowest quartile (4). Childhood IQ is known to be one of the key determinants of later life cognition and wellbeing, and is associated with mortality, morbidity and frailty in old age (5).

Iodine is obtained mainly through the diet, with no ongoing iodine-fortification programme in the UK. The main sources of iodine in the British diet are milk and dairy products, as well as fish and seafood. While cross-sectional surveys revealed mild insufficiency in the population (1), recent studies have highlighted that most women struggle to reach the recommended iodine daily intake (150 µg/day), a recommended intake that increases during pregnancy to 250 µg/day (6).

Iodine deficiency, mainly in children and young adults, has been suggested to cause certain brain proteins to be down- regulated in particular brain regions, anterior commissure axons and mRNA expression to be reduced, and dendrite size to be altered resulting in potential premature cell apoptosis. Additionally, iodine deficiency may cause a reduction in cerebellar cell size and decreased myelination throughout the central nervous system (7), and, therefore, may be related to brain atrophy and brain white matter damage. Altogether, such changes are likely to affect cognitive functions. Preservation of mental / cognitive capacities is key in having a healthy long

DIETARY IODINE EXPOSURE AND BRAIN STRUCTURES AND COGNITION IN OLDER PEOPLE. EXPLORATORY ANALYSIS

IN THE LOTHIAN BIRTH COHORT 1936

M. DEL C. VALDÉS HERNÁNDEZ1,2*, J. KYLE3, J. ALLAN4, M. ALLERHAND1, H. CLARK3, S. MUÑOZ MANIEG1,2, N.A. ROYLE1,2, A.J. GOW1,5, A. PATTIE1,6, J. CORLEY1,6, M.E. BASTIN1,2,

J.M. STARR1, J.M. WARDLAW1,2, I.J. DEARY1,6, E. COMBET7*

1. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; 2. Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; 3. Department of Nutrition, University of Aberdeen, Aberdeen, UK; 4. Department of Psychology, University of Aberdeen, Aberdeen, UK;

5. Department of Psychology, Heriot-Watt University, Edinburgh, UK; 6. Department of Psychology, University of Edinburgh, Edinburgh, UK; 7. Human Nutrition, School of Medicine, College of Medical, Veterinary and Lifesciences, University of Glasgow, Glasgow, UK. * Both authors equally contributed to this work. Corresponding author: Dr. Maria C. Valdés

Hernández, Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, 49 Little France Crescent, Chancellor’s Building, Edinburgh, EH16 4SB, UK. Telephone: +44-131-4659527, Fax: +44-131-3325150, E-mail: M.Valdes-Hernan@ed.ac.uk

Abstract: Background: Iodine deficiency is one of the three key micronutrient deficiencies highlighted as major public health issues by the World Health Organisation. Iodine deficiency is known to cause brain structural alterations likely to affect cognition. However, it is not known whether or how different (lifelong) levels of exposure to dietary iodine influences brain health and cognitive functions. Methods: From 1091 participants initially enrolled in The Lothian Birth Cohort Study 1936, we obtained whole diet data from 882. Three years later, from 866 participants (mean age 72 yrs, SD ±0.8), we obtained cognitive information and ventricular, hippocampal and normal and abnormal tissue volumes from brain structural magnetic resonance imaging scans (n=700). We studied the brain structure and cognitive abilities of iodine-rich food avoiders/low consumers versus those with a high intake in iodine-rich foods (namely dairy and fish). Results: We identified individuals (n=189) with contrasting diets, i) belonging to the lowest quintiles for dairy and fish consumption, ii) milk avoiders, iii) belonging to the middle quintiles for dairy and fish consumption, and iv) belonging to the middle quintiles for dairy and fish consumption. Iodine intake was secured mostly though the diet (n=10 supplement users) and was sufficient for most (75.1%, median 193 µg/day). In individuals from these groups, brain lateral ventricular volume was positively associated with fat, energy and protein intake. The associations between iodine intake and brain ventricular volume and between consumption of fish products (including fish cakes and fish-containing pasties) and white matter hyperintensities (p=0.03) the latest being compounded by sodium, proteins and saturated fats, disappeared after type 1 error correction. Conclusion: In this large Scottish older cohort, the proportion of individuals reporting extreme (low vs. high)/medium iodine consumption is small. In these individuals, low iodine-rich food intake was associated with increased brain volume shrinkage, raising an important hypothesis worth being explored for designing appropriate guidelines.

Key words: Diet, iodine, brain, cognition, MRI, ageing, white matter hyperintensities.

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life, as well as enabling society to achieve its full productivity potential. However, it is not known how different exposures to dietary iodine throughout life influences brain health and cognition in the elderly.

Here, we investigate the link between estimated dietary iodine intake, brain structural measurements from magnetic resonance imaging (MRI) and cognitive abilities in the Lothian Birth Cohort 1936 (LBC1936) (8) with the hypothesis that individuals most likely to have a sustained sufficient intake of iodine-rich foods in their diets have better preserved brain health in late adulthood and, consequently, better cognitive performance. This study aims to estimate whether very low or high iodine intake throughout life is associated with cognitive abilities and brain health in later life. Acknowledging the difficulties in assessing lifelong exposure to nutrients, the analysis is carried out by relating dietary measures based on iodine-rich food intake from individuals with specific dietary patterns more likely to be sustained through longer periods of time: fish/dairy avoiders and low consumers, versus groups with medium (sufficient) intake and high consumers) to measures of cognitive function, brain atrophy and brain white matter damage in later life. We also explored whether childhood intelligence (IQ) is associated with iodine consumption levels in late adulthood, thus, enabling to inform the development of evidence-based recommendations for the design and targeting of dietary interventions. Finally, since iodine is a critical component of the thyroid hormones, we analyse the stability of the thyroid functioning across the three years elapsed from the collection of the dietary data and the cognitive and brain imaging data, through the analysis of relevant laboratory data obtained at both time points.

Materials and Methods

Participants From the LBC1936, which comprises community-dwelling

surviving members of the Scottish Mental Survey of 1947(8), 1091 individuals (548 men and 543 women) with an average age of 69.5 (SD=0.8) years completed cognitive tests, and provided personality, demographic, health, lifestyle, habitual diet information (participants completed a 165 item Food Frequency Questionnaire) and blood samples on a first wave of data collection, between 2004 and 2007. On a second wave of data collection, 866 participants (mean age 72.7 years, SD 0.8 years) repeated almost all assessments from wave 1 with the exception of the dietary questionnaire, and a subgroup (n=700) had an MRI brain scan. The main causes for withdrawal at wave 2, as reported elsewhere(9), were: death (n=19), lost contact (n=20), health reasons (n=64), dementia (n=7), care roles (n=13) and lack of time (n=17). This study uses dietary information (wave 1), laboratory data obtained from the analyses of the blood samples (waves 1 and 2), and cognitive and imaging data (wave 2). The research was carried out in compliance with the Helsinki Declaration. Written informed

consent was obtained from all participants under protocols approved by the Lothian (REC 07/MRE00/58) and Scottish Multicentre (MREC/01/0/56) Research Ethics Committees.

MRI acquisition and processing MRI scans were acquired using a 1.5T GE Signa Horizon

HDxt clinical scanner (General Electric, Milwaukee, WI, USA) operating in research mode and using a self-shielding gradient set with maximum gradient of 33 mT/m, and an 8-channel phased-array head coil. The imaging acquisition and processing protocol is fully described in(10). For this particular study, we used hippocampal, ventricular, subarachnoid space, cerebellar and white matter hyperintensity volumes, all adjusted for intracranial volume, as it has been reported that these brain imaging parameters could be influenced by deficient iodine intake(7). They were obtained from a high resolution T1-weighted (T1W), and whole brain T2- (T2W), T2*- (T2*W) and fluid attenuated inversion recovery (FLAIR)-weighted MRI sequences.

B r i e f l y , b r a i n v e n t r i c u l a r b o u n d a r i e s w e r e s e m i – automatically delineated from the T1W volume scan using a region-growing thresholding method from the Region of Interest tool in Analyze 9.0TM (AnalyzeDirect, Mayo Clinic) software. Hippocampi were also segmented from the high- resolution T1W volume scan using an automatic atlas-based segmentation pipeline that uses FSL tools: SUSAN(11), FLIRT(12) and FIRST(13), followed by manual editing when required. Intracranial volume was obtained semi-automatically from thresholding the T2*W sequence using the Object Extraction tool in Analyze 9.0TM, followed by manual removal of erroneously included structures and rectification of the inferior limit at the level of the odontoid peg. A validated multispectral image segmentation method: MCMxxxVI(14) implemented on a freely available tool: bric1936 (www. sourceforge.net/projects/bric1936), was used to extract white matter hyperintensities (WMH) and cerebrospinal fluid from the colour data fusion of co-registered T2*W and FLAIR images. Superficial subarachnoid space (SSS, the space between the inner edge of the dura and the brain cortical surface) volume was calculated as the difference between the total cerebrospinal fluid and the ventricular volumes. Finally, cerebellar white matter and cortical volumes were obtained automatically using FreeSurfer (http://freesurfer.net/).

Cognitive testing For this study, we used cognitive measures obtained at the

time of the MRI scan / wave 2 (mean age 72.7, SD 0.8 years). These cognitive variables, described in (8), were: a general cognitive factor (g), general processing speed (g-speed) and general memory (g-memory). These cognitive ability measures (i.e. g, g-speed and g-memory) were generated using principal component analysis from batteries of well-validated cognitive tests. To derive g, six subtests of the WAIS-IIIUK (15) (Digit Symbol, Digit Span Backward, Symbol Search, Letter-Number

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Sequencing, Block Design & Matrix Reasoning) were used. g-memory was derived from five subtests from the WMS-IIIUK (16) (Logical Memory Total Immediate & Delayed Recall, Verbal Paired Associates Immediate & Delayed Recall, & Spatial Span Total Score) and two subtests from the WAIS- IIIUK (Letter-Number Sequencing & Digit Span Backward). g-speed was obtained from two reaction time tests (Simple Reaction Time & Choice Reaction Time), an Inspection Time test(8), and two WAIS-IIIUK subtests (Digit Symbol & Symbol Search). Childhood intelligence was derived from scores on the Moray House test taken by the participants at age 11 years(8).

Diet All study participants (n=1091) were asked to complete the

Scottish Collaborative Group Food Frequency Questionnaire (SCG-FFQ) at home and return it by post. Of these, 98 were not returned, 26 were returned blank, and 39 had more than 10 missing items and were therefore excluded from the analyses. Individuals with extreme energy intakes (<2.5th or >97.5th centile, n=46) were also excluded to obtain the most reliable food frequency data(17). The SCG-FFQ is a self-report instrument validated for older adults(17), where respondents rate the frequency of consumption of standard portions of 175 different foods and drinks over the last 2-3 months (rarely/never, 1-3 per month, 1 per week, 2-3 per week, 4-6 per week, 1 per day, 2-3 per day, 4-6 per day or 7+ per day) and responses are used to estimate typical micro and macro nutrient intakes. For this study, consumption (g/day) of specific foods with high iodine content was extracted (i.e. milk, other dairy, fish (white, oily, canned and fish products), shellfish), and the habitual daily intake of iodine was calculated. Intake of dietary supplements was also reported. To assess the ability of the SCG-FFQ in estimating iodine intake, a separate dietary assessment was carried out: iodine intakes estimated after 50 Scottish participants completed the SCG FFQ were compared with 4-day diet records and excretion of iodine i