Study design

Data were drawn from the Personality and Total Health (PATH) Through Life project, which is a longitudinal community study following three narrow age cohorts from Canberra and the neighbouring town of Queanbeyan in South-eastern Australia. The core aims of the PATH study are to delineate the course, risk and protective factors for depression, anxiety, substance use and cognitive ability across adulthood. A detailed description of the study is available elsewhere [27]. This analysis is focused on the oldest cohort, born between 1937 and 1941 and aged 60–64 years at the time of the baseline interview in 2001. This sample of participants was randomly drawn from the electoral roll (registration on the electoral roll is compulsory for Australian citizens) to produce a wave 1 sample of 2,551 (from a response rate of 58 %). Approximately 90 % of these participants who were still alive agreed to be re-interviewed 4 years later for the wave 2 assessment (n = 2,222). At each wave, participants took part in a structured interview (usually at their home or the Australian National University) that included a questionnaire completed using a hand-held computer, and physical and cognitive tests administered by a trained interviewer. Measures ranged from sociodemographic characteristics through to physical health, mental health, substance use, personality and cognition. Only measures used in the present study are described below.

This project draws on data from two PATH substudies. The magnetic resonance imaging (MRI) substudy has been detailed elsewhere [28, 29]. Of the 2,551 baseline participants, 2,076 agreed to be contacted for an MRI assessment, with 622 of these participants randomly selected and offered a scan. Of these, 431 also underwent an MRI scan at wave 2 and, after exclusion of those with an MRI abnormality, stroke or epilepsy, there were 341 respondents with MRI data from two waves.

At baseline, participants were also invited to take part in a Diet and Health substudy and given a self-completion questionnaire (including a food frequency questionnaire, detailed below), which was to be returned by mail when completed. Overall, 1,753 respondents (69 %) of PATH participants in the oldest cohort completed and returned the booklet. This analysis, therefore, is restricted to the 255 respondents who provided survey and MRI data at both waves, and completed and returned the food frequency questionnaire. A series of analyses compared the characteristics of this subsample to the larger sample of respondents not in this subsample but who had participated in the wave 2 PATH interview. The results showed no difference in terms of gender (p = .16), hypertension (p = .46), cognitive functioning (mini-mental state examination p = .14, spot-the-work p = .54), anxiety (p = .23) or depression symptoms (p = .22), life satisfaction (p = .99), reported diabetes (p = .39), or current smoking status (p = .18). Those included in the analytic sample were less likely to report heart problems (p = .04) or poor self-rated health (p = .02) and were more likely to report being married (p = .03) and to participate in regular moderate or vigorous exercise (p < .001).

After complete description of the study to the subjects, written informed consent was obtained from all participants prior to each wave of data collection in the PATH project. The study was approved by the Human Research Ethics Committee of The Australian National University.

Measures

Sociodemographic and health covariates

Demographic covariates included age (in years) and gender. Educational attainment was operationalized by a binary variable indicating whether respondents had completed their high school certificate, and employment status differentiated between those who were working full-time or part-time, were unemployed and looking for work, or were no longer participating in the work force. Engagement in regular moderate or vigorous exercise was adapted from an approach used in the UK Whitehall II study [30], and the questionnaire assessed current smoking status. Health covariates included hypertension (respondents were classified as hypertensive if their systolic or diastolic blood pressure averaged over two readings were higher than 90 or 140 mmHg respectively, or if they reported use of antihypertensive medication), self-reported current diabetes, current depressive symptoms (Goldberg Depression Scale [31]) and reported use of antidepressant medication. Participants who reported a history of stroke or transient ischemic attack were excluded from the analyses.

Diet

Dietary intake was assessed using a version of the validated Commonwealth Scientific and Industrial Research Organisation Food Frequency Questionnaire (FFQ) [32]. The FFQ included a list of foods and standard serving sizes, and respondents indicated their habitual frequency of consumption on an 11-point scale (from never to three times a day) and indicated divergence from usual serving size. Dietary analysis of the data produced estimates of daily nutrient intake and (critical for this analysis) daily grams of each food item consumed. As detailed elsewhere [10], principal components analysis was used to summarize the information from the 188 distinct food items into meaningful scales representing dietary patterns. Two orthogonal factors labelled “prudent” (healthy) diet (characterized by the consumption of fresh vegetables, salad, fruit and grilled fish) and “Western” (unhealthy) diet (characterized by the consumption of roast meat, sausages, hamburgers, steak, chips, crisps and soft drinks) were identified. For each factor, higher scores represented greater levels of consumption, with a 1-point difference on each scale corresponding to one standard deviation (SD).

MRI images

All participants were imaged with a 1.5 T Philips Gyroscan ACS-NT scanner (Philips Medical Systems, Best, the Netherlands) for T1-weighted three-dimensional structural MRI in coronal orientation using a fast-field echo sequence. For wave 1, repetition time (TR) = 28.05, echo time (TE) = 2.64 ms, flip angle = 30°, matrix size = 256 × 256, field of view (FOV) = 260 × 260 mm, slice thickness = 2.0 mm and mid-slice to mid-slice distance = 1.0 mm, yielding over-contiguous coronal slices. For wave 2, TR = 8.93 ms, TE = 3.57 ms, flip angle = 8°, matrix size = 256 × 256 and FOV = 256 × 256 mm. Slices were contiguous with a slice thickness of 1.5 mm. Hippocampal and amygdalar volumes were determined by manually tracing the periphery of the region of interest (ROI) on each slice of a T1-weighted scan in coronal orientation using Analyze 5.0 (Brain Imaging Resource, Mayo Clinic, Rochester, MI, USA). The outlining of the hippocampus and amygdala always proceeded from anterior to posterior and was traced according to the protocol outlined by Watson et al. [33–35]. We repeated 16 volume estimations on 10 randomly selected scans, and interclass correlations between raters was in excess of 0.95 for all structures. Intracranial volume (ICV) was computed with the Freesurfer 5.3 package [36] for wave 1 and wave 2 images.

Statistical analysis

After presenting descriptive data on baseline characteristics of the sample, the association between dietary factors and left and right hippocampal volumes over time was modelled using generalized estimating equation (GEE) models with a normal distribution, identity link function and exchangeable within-person working correlation structure. GEE models were used owing to the lack of independence of observations within respondents over the two time points (repeated measures). Hippocampal volume was normalized for ICV using the formula Vol adj = vol – b × (ICV – mean ICV), where b is the regression coefficient of ROI volume on ICV. To correct for potential wave-specific procedural effects, the (centred) difference in overall ICV between wave 1 and wave 2 was also included as a covariate in these models [28]. Missing data for the items included in the current analysis was minimal (only four individuals reported missing data, with 2.1 % of data missing) and this was imputed by mean substitution or carrying forward (or backward) data. Additional models tested for an interaction between each of the dietary factors and wave on hippocampal volume to evaluate whether diet was associated with differential atrophy over time.

The robustness of the findings were evaluated through sensitivity analyses using a random intercept (rather than GEE) modelling approach, excluding respondents with missing data, inspection and exclusion of respondents with larger residual scores, and use of the log of hippocampal volume as the outcome measure. In all cases, results were consistent with those reported.