Discuss the Difference Between an Exploratory Analysis and a Confirmatory


Discuss the Difference Between an Exploratory Analysis and a Confirmatory

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Discuss the Difference Between an Exploratory Analysis and a Confirmatory

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

J Nutr Health Aging Volume 21, Number 9, 2017


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





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 (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 in 24 hour urine samples. Urinary iodine was calculated employing a ISO9000 accredited laboratory and mass spectrometry. There was moderate / fair agreement between the SCG FFQ and dietary records (rs=0.488, kw=0.222, with low (16%) gross misclassification to the opposite tertile). The agreement between SCG FFQ and urinary excretion was weaker (rs=0.329, with low (18%) gross misclassification to the opposite tertile).

With the assumption that extreme, or very specific, consumption patterns are likely to express a trait possibly reflecting the long term intake of specific nutrients, we specifically focus on groups representing opposite ends of the iodine intake spectrum: low iodine consumers (those with low intakes of dairy foods and fish, and dairy avoiders) and moderate-high iodine consumers (those with medium or high intakes of dairy foods and fish).

For the purpose of the analyses, the following contrasting groups were formed: i) Group A (low intake of dairy and fish): Those in the lowest

quintile for dairy consumption (less than 151 gram per day) and the lowest two quintiles for fish consumption (less than 37 g per day)

ii) Group B (dairy avoiders): Those never consuming any milk, and not belonging to group A, C or D.

iii) Group C (medium intake of dairy and fish): Those in the middle quintile for dairy consumption (204 to 320 g per day) and quintiles 3 and 4 for fish consumption (37 to 50 g per day)

iv) Group D (high intake of dairy and fish): Those in the highest quintile for dairy and fish consumption (over 432 g and 50 g per day, respectively)

For these individuals, energy (KJ/day), fat (g/day), proteins (g/day), cholesterol (g/day), saturated fats (g/day) and sodium (mg/day) were derived from the food frequency questionnaire, in addition to the daily intake of iodine.

Thyroid function Thyroid stimulating hormone (TSH) and free thyroxine

(free T4) were measured as described in (18). Briefly, analysis were carried out using a two-step immunoassay. The laboratory reference range for TSH was 0.2 to 4.5 mU/l, with coefficients of variability ranging from 3.0% to 3.5%, and for free T4 it was 9 to 21 pmol/L, with coefficients of variability ranging from 5.1% to 8.9%.

Statistical analysis In all analyses, volumetric MRI data were standardised

by head size (i.e. expressed in percentage with respect to the intracranial volume) and adjusted by age in days at the time of the MRI scan. To examine the associations between diet and cognition and brain health related indicators at older age, and the associations between the haematological parameters that relate to the thyroid function in waves 1 and 2 (i.e. acquired 3 years apart), we applied robust univariate regression analysis using iteratively reweighted least squares with a bisquare weighting function from MATLAB R2014a Statistical Toolbox and an adding bootstrap, and repeated the analyses without excluding outliers (ANOVA). False Discovery Rate (FDR) was applied to adjust for multiple comparisons. Kruskal-Wallis and Mood’s Median tests were used to evaluate differences in the cognitive and imaging parameters between the four groups with extreme/middle levels of iodine consumption. To explore whether childhood intelligence was associated with dietary iodine consumption at age 72 years we used a general linear model with age 11 IQ as predictor and iodine (extracted from the SCG-FFQ) as the response. Gender at each data collection wave was used as covariate in all analyses. All our results were corroborated using IBM-SPSS Statistics 21.


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Sample characteristics

Imaging and cognition Valid imaging data were available for 61-64% of the

participants classified according to their iodine consumption. As Table 1 shows, the descriptive values of the imaging variables for the subsample with extreme/middle iodine intake/ avoidance (n=189, 87 men and 102 women) are similar to those from the whole sample (n=1091). The subtle inter- hemispheral differences on the hippocampal, cerebellar cortex and ventricular volumes in the subsample follow the same pattern of the whole sample: slightly more atrophy (i.e. reduced volume) in the left hemisphere compared to the right, but this difference was not significant (previously reported (19)). The median and distribution of the imaging and cognitive measures did not significantly differ between the four iodine consumption groups: low intake of dairy and fish (Group A, n=63), dairy avoiders (Group B, n=22), medium intake of dairy and fish (Group C, n=76) and high intake of dairy and fish (Group D, n=28).

Iodine-rich foods and iodine intake Total fish and dairy intake for the whole cohort is shown in

Table 2. There was a broad range of intakes for most iodine-

rich food groups, except shellfish, canned fish and fish-products (such as fish cakes and fish-containing pasties), which were consumed at a lower level, and avoided by a large proportion of the population under consideration (n=882).

Iodine-rich food avoiders were a minority, with 3.8% never consuming milk, 1.4% never consuming fish, 1.7% consuming low amounts of fish and dairy (less than 200g fish per week, less than 200g dairy per day, of which less than 100g should be milk). Furthermore, 0.2% combined low dairy consumption with no fish at all, and 0.8% avoided both dairy and fish totally.

In the subsample with extreme/middle iodine intake/ avoidance no overlap was observed between the individuals (n=189) categorised under the four groups with patterns of iodine intake representative of a trait (Table 3).

On this subsample, intake of iodine containing nutritional supplements was reported by 10 participants (n=4 in group A, n=2 in group B, n=2 in group C and n=2 in group D). In general, reported iodine intake was sufficient (median 193 µg/ day), IQR 109.3, as per the UKs recommended diary iodine intake(20) of 140µg/day, ranging from 61.6 µg to 524.4 µg per day. A quarter (n=47, 24.9%) had an iodine intake below the reference nutrient intake for iodine (140 µg/day). The median (IQR) total iodine intake µg/day was 120.0 µg/day (59.7) for Group A, 169.8 µg/day (56) for Group B, 207.4 µg/day (63.9) for Group C and 352.7 µg/day (128.3) for Group D.

Table 1 Descriptive statistics of the imaging and cognitive variables in the whole sample (n=1091) and in the subsample with extreme/

middle iodine intake/avoidance (n=189). For variables normally distributed (†), the mean and standard deviation (SD) are given. For not normally distributed variables, median and interquartile range (IQR) are given instead

Imaging variables Full Cohort (n=1091) Present Subsample (n=189)

Valid data (n) Mean (SD)or Median (IQR) Valid data (n) Mean (SD)or Median (IQR)

Brain ventricular volume (ml) Lateral right 671/1091 (62%) 13.93 (9.30) 117 / 189 (62%) 14.44 (8.46)

Lateral left 15.26 (10.70) 15.10 (10.73)

Third 1.74 (0.90) 1.79 (0.88)

Fourth 0.27 (0.25) 0.30 (0.24)

Subarachnoid space volume (ml) 669/1091 (61%) 189.59 (75.25) 117 / 189 (62%) 190.83 (66.28)

Hippocampal volume (ml) (†) Right hippocampus 660/1091 (60%) 3.33 (0.46) 117 / 189 (62%) 3.34 (0.68)

Left hippocampus 3.09 (0.46) 3.06 (0.56)

White matter hyperintensity volume (ml) 678/1091 (62%) 7.70 (13.20) 120 / 189 (64%) 6.44 (14.98)

Cerebellar volume (ml) (†) White matter right 647/1091 (59%) 11.36 (1.64) 115 / 189 (61%) 11.51 (1.68)

Cortex right 43.13 (4.78) 43.65 (4.95)

White matter left 11.32 (1.68) 11.44 (1.72)

Cortex left 42.40 (4.70) 42.50 (4.88)

Cognitive variables

g 856/1091 (78%) 0.04 (1.29) 151/189 (80%) 0.07 (0.96) (†)

g-speed 838/1091 (77%) 0.11 (1.26) 147/189 (78%) 0.08 (0.93) (†)

g-memory 840/1091 (77%) 0.13 (1.34) 148/189 (78%) 0.03 (0.89) (†)

Age 11 IQ (†) 1028/1091 (94%) 100.00 (14.99) 183/189 (7%) 102.26 (13.62)

Note: (†) refers to normally distributed variable data


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Thyroid function The thyroid function of euthyroid subjects (i.e. having

normal thyroid gland function) are reported in(18), along with associations with cognition. The haematological parameters related to the thyroid function at old age, measured 3 years apart, were strongly and significantly associated (standardised β = 0.55 (TSH) and 0.51 (free T4), p4.5mU/L, T44.5mU/L, T4>9pmol/L


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Table 5 Results of the associations between dietary variables that relate to iodine and imaging and cognitive variables in the subsample

with extreme/middle iodine intake/avoidance, before FDR correction. Given: standardised coefficient β (p-value)

Imaging variables (% volume in ICV)

Iodine (ug/day)

Iodine + supplements


Milk (g/day)

Other dairy (g/day)

All dairy ( g/day)

White fish (g/day)

Oily fish (g/day)

Shell fish (g/day)

Fish products (g/day)

Fish canned (g/day)

Cerebellum WM_R 0.04 (0.69) 0.04 (0.63) 0.02 (0.81) -0.07 (0.47) 0.02 (0.83) -0.06 (0.48) 0.07 (0.44) -0.03 (0.73) -0.02 (0.83) 0.08 (0.41)

Cerebellum Cortex_R 0.05 (0.62) 0.05 (0.59) -0.04 (0.66) 0.002 (0.98) 0.01 (0.92) -0.03 (0.77) 0.03 (0.75) -0.11 (0.23) -0.004 (0.96) 0.04 (0.66)

Cerebellum WM_L -0.02 (0.86) 0.025 (0.79) 0.02 (0.82) -0.1 (0.29) -0.02 (0.79) -0.11 (0.22) 0.02 (0.84) -0.07 (0.47) -0.07 (0.47) -0.004 (0.97)

Cerebellum Cortex_L 0.06 (0.55) 0.06 (0.54) -0.03 (0.74) -0.01 (0.88) 0.03 (0.77) 0.001 (0.99) 0.06 (0.56) -0.11 (0.24) -0.04 (0.65) 0.04 (0.64)

Lateral Ventricle_R 0.24 (0.008)* 0.24 (0.01)* 0.12 (0.18) 0.14 (0.13) 0.21 (0.02)* 0.04 (0.63) 0.16 (0.08) -0.05 (0.59) 0.06 (0.53) -0.08 (0.37)

Lateral Ventricle_L 0.29 (0.002)* 0.30 (0.001)** 0.08 (0.38) 0.14 (0.14) 0.21 (0.02)* 0.05 (0.56) 0.20 (0.03)*† -0.03 (0.73) 0.007 (0.94) -0.05 (0.60)

3rd Ventricle 0.17 (0.07) 0.12 (0.18) 0.009 (0.92) 0.02 (0.78) 0.05 (0.55) 0.1 (0.29) 0.11 (0.22) -0.11 (0.23) -0.004 (0.97) -0.05 (0.60)

4th Ventricle 0.12 (0.20) 0.13 (0.16) 0.08 (0.37) 0.10 (0.29) 0.05 (0.63) 0.03 (0.78) 0.01 (0.90) 0.02 (0.88) -0.09 (0.32) -0.05 (0.56)

Sub- arachnoid space -0.07 (0.43) -0.055 (0.56) -0.12 (0.19) -0.19 (0.04)* -0.14 (0.12) 0.06 (0.48) 0.03 (0.75) 0.03 (0.77) -0.06 (0.5) -0.14 (0.14)

Hippo-campus R -0.005 (0.96) 0.025 (0.79) -0.04 (0.65) 0.06 (0.52) 0.03 (0.77) 0.07 (0.45) 0.008 (0.93) -0.13 (0.15) 0.03 (0.72) -0.11 (0.26)

Hippo-campus L 0.007 (0.94) -0.008 (0.94) 0.008 (0.93) 0.08 (0.42) 0.07 (0.47) 0.07 (0.47) 0.03 (0.77) -0.10 (0.30) 0.08 (0.39) -0.04 (0.63)

WMH 0.12 (0.21) 0.09 (0.33) 0.07 (0.48) -0.05 (0.61) 0.07 (0.48) 0.13 (0.16) 0.005 (0.96) -0.03 (0.73) 0.28 (0.002)* -0.06 (0.51)

Cognition at mean age 72.7 years g

0.03 (0.74) 0.04 (0.65) 0.07 (0.37) 0.11 (0.18) 0.07 (0.41) -0.06 (0.50) 0.12 (0.17) 0.19 (0.02)* -0.08 (0.35) 0.13 (0.12)

g_speed -0.02 (0.79) -0.04 (0.64) 0.07 (0.42) 0.05 (0.53) 0.01 (0.87) -0.06 (0.49) -0.02 (0.84) 0.13 (0.13) 0.05 (0.58) 0.09 (0.30)

g_memory 0.04 (0.63) 0.06 (0.47) 0.05 (0.59) 0.04 (0.60) 0.08 (0.34) -0.05 (0.52) 0.10 (0.23) 0.06 (0.46) 0.06 (0.45) 0.23 (0.005)*

Note: L and R refer to the Right and Left brain hemispheres respectively; † Became non-significant when outliers were excluded (robust regression with bootstrap)

Table 6 Results of the robust associations between general dietary variables and imaging and cognitive variables in the subsample with

extreme/middle iodine intake/avoidance, before FDR correction. Given: standardised coefficient β (p-value)

Imaging variables (% volume in ICV) KJ Fat Proteins Cholesterol Saturated fats Sodium

Cerebellum WM_R 0.70 (0.45) 0.04 (0.70) 0.06 (0.49) 0.09 (0.31) 0.04 (0.66) 0.03 (0.75)

Cerebellum Cortex_R 0.15 (0.10) 0.16 (0.08) 0.09 (0.34) 0.14 (0.14) 0.17 (0.07) 0.14 (0.14)

Cerebellum WM_L 0.09 (0.32) 0.06 (0.54) 0.07 (0.42) 0.12 (0.19) 0.07 (0.48) 0.05 (0.63)

Cerebellum Cortex_L 0.13 (0.15) 0.16 (0.09) 0.08 (0.37) 0.11 (0.22) 0.17 (0.07) 0.10 (0.27)

Lateral Ventricle_R 0.32 (


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