Cohort Profile: The PATH through life project

Anstey, Kaarin J.; Christensen, Helen; Butterworth, Peter; Easteal, Simon; Mackinnon, Andrew; Jacomb, Trish; Maxwell, Karen; Rodgers, Bryan; Windsor, Tim D.; Cherbuin, Nicolas; Jorm, Anthony F

doi:10.1093/ije/dyr025

Cited by 215 publications

(214 citation statements)

References 34 publications

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“…We used a random sample from a large group of study participants who consented to be contacted regarding an MRI scan (n = 2,076). The study participants had a relatively high level of education and social-economic status [11], which may result in lower atrophy estimates than would be expected in a less educated cohort. Furthermore, as is common with large, longitudinal imaging studies, different scanners and different scan parameters were used across waves of data collection (outlined in methods), although a robust and published method was used to effectively control for these effects [15,16] and we have previously shown that, in this sample, scanner model did not bias measurement of cortical thickness [16].…”

Section: Discussionmentioning

confidence: 98%

“…Participants were sampled from the Personality and Total Health through Life (PATH) project, a large population-based longitudinal study of aging aimed at investigating the course of mood disorders, cognition, health and other individual characteristics across the lifespan [11]. PATH surveyed 2,530 individuals, aged 40-44 years at baseline, who were residents of the city of Canberra and the adjacent town of Queanbeyan, Australia, and who were randomly recruited through the electoral roll (enrolment to vote is compulsory for Australian citizens).…”

Section: Study Populationmentioning

confidence: 99%

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Increasing Body Mass Index at Midlife is Associated with Increased Cortical Thinning in Alzheimer’s Disease-Vulnerable Regions

Shaw

Abhayaratna

Anstey

2017

JAD

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Abstract. Higher body mass index (BMI) at midlife is associated with greater decreases in cognitive function at older age as well as increased Alzheimer's disease (AD) risk, compared to those with normal BMI. Here, we tested whether BMI at midlife was associated with cortical thinning in brain regions known to be affected in early AD. We examined a large sample (n = 404) of midlife individuals (44-49 years) from the PATH population-based study. Individuals were scanned with magnetic resonance imaging (1.5T) on up to three occasions over eight years. Change in cortical thickness was modeled as a linear function of BMI and change in BMI longitudinally. Being obese was associated with thinner right frontal cortex at baseline (44-49 years). Across all individuals, increasing BMI over the 8-year study period was associated with increased cortical thinning in posterior cingulate bilaterally, as well as right lingual gyrus, anterior cingulate, and the peri-calcarine sulcus. Accelerated age-related cortical atrophy at midlife, particularly in posterior cingulate, is consistent with increased risk of AD in individuals with high BMI at this age. The findings suggest that management of body weight at midlife could reduce the risk of AD.

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Section: Discussionmentioning

confidence: 98%

Section: Study Populationmentioning

confidence: 99%

Increasing Body Mass Index at Midlife is Associated with Increased Cortical Thinning in Alzheimer’s Disease-Vulnerable Regions

Shaw

Abhayaratna

Anstey

2017

JAD

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“…Subjects were sampled from wave four of the oldest cohort (aged 60-64 at baseline) of the Personality and Total Health Through Life (PATH) study [29]. The PATH study is a representative, communitybased longitudinal cohort study which commenced in 2001.…”

Section: Subjectsmentioning

confidence: 99%

“…Follow-up assessments were every four years up to 12 years. Participants are residents of the city of Canberra and the adjacent town of Queanbeyan, Australia, and were randomly recruited through the electoral roll [29]. Enrolment to vote is compulsory for Australian citizens.…”

Section: Subjectsmentioning

confidence: 99%

Neuropsychiatric Symptoms and Cognitive Impairment: Understanding the Importance of Co-Morbid Symptoms

Mortby

Burns

Eramudugolla

et al. 2017

JAD

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Abstract.Background: Neuropsychiatric symptoms (NPS) are common in older adults with cognitive impairment, yet little is known about population-based prevalence and clinical implications of co-morbid symptom presentation across the spectrum from normal cognition to dementia. Objective: To characterize the prevalence of NPS and explore the clinical implications of co-morbid symptom presentation. Methods: Cross-sectional study of 1,417 older adults (aged 73-79) with dementia (n = 40); with mild cognitive impairment (MCI; n = 133); who are 'cognitively normal, but-at-risk' (CN-AR; n = 397); and who are cognitively normal (n = 847). NPS were assessed by the Neuropsychiatric Inventory. Cluster analyses were conducted using a latent class analysis (LCA). Results: NPS are highly prevalent across the cognitive function spectrum (30.8%-80%). NPS were associated with a 3-fold increased risk of dementia, a 2-fold increased risk of MCI, and a 1.5-times increased risk of CN-AR. Each additional co-morbid symptom was associated with an additional 1.5-times increased risk of dementia, but not MCI or CN-AR. LCA revealed four distinctive sub-populations: 1) frontal/low comorbidity; 2) high prevalence/high comorbidity; 3) affective/low comorbidity; and 4) sleep/low comorbidity. Conclusion: Our findings confirm previous reports on the prevalence of NPS in community-based samples and are consistent with the profiles of NPS domain characteristics of MCI and dementia. Number of co-morbid NPS and not symptom clusters are associated with increased risk of dementia. Understanding such patterns will help inform our understanding of mild behavioral disorders and assist with clinical assessment.

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“…The FMM and MRF results are then combined to create a spatially coherent and conservative estimate of the FDR. Empirical validation is then conducted via simulations and an application to the PATH data of Anstey et al (2012).…”

Section: Overviewmentioning

confidence: 99%

Finite mixture models for regression problems

Nguyen¹

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Finite mixture models (FMMs) are a ubiquitous tool for the analysis of heterogeneous data across a broad number of fields including agriculture, bioinformatics, botany, cell biology, economics, fisheries research, genetics, genomics, geology, machine learning, medicine, palaeontology, psychology, and zoology, among many others. Due to their flexibility, FMMs can be used to cluster data, classify data, estimate densities, and increasingly, they are also being used to conduct regression analysis and to analyze regression outcomes. There is now an expansive literature on the usage of FMMs for regression, as well as a broad demand for the development of such methods for the analysis of new and complex data.This thesis begins with a summary of the current literature on FMMs and their applications to regression problems. Here, the mixture of regression models (MRMs), cluster-weight models (CWMs), mixtures of experts (MoEs), and mixtures of linear of mixed effects models (MLMMs), as well as other variants of FMMs for regression analysis are introduced. Various properties such as denseness and identifiability, as well as maximum likelihood (ML) estimation techniques such as the expectation-maximization (EM) and minorization-maximization (MM) algorithms are discussed, and a review is presented regarding asymptotic inference and model selection in FMMs. A new result on the characterization of a t linear CWM (LCWM) is also presented. Some new applications of FMMs to regression problems are then discussed.Firstly, a series of models based on FMMs are presented for the clustering and classification of sparsely sampled bivariate functional data. These methods are named mixture of spatial spline regression (MSSR) and MSSR discriminant analysis (MSSRDA). MSSR is constructed using the theory of MLMMs and spatial splines, and an EM algorithm for the ML estimation of the model is presented. MSSRDA is then constructed by combining MSSR with the mixture discriminant analysis framework for classification. The methods are tested on their ability to cluster and classify simulated data. An example application to handwritten digits recognition is then presented. Here, it is shown that MSSR and MSSRDA perform comparably to currently available methods, and outperform said methods in missing data scenarios.Secondly, an FMM is used to produce a false discovery rate (FDR) control procedure for magnetic resonance imaging (MRI) data. In MRI data analysis, millions of hypotheses are often tested simultaneously, resulting in inflated numbers of false positive results. Many of the available FDR techniques for MRI data either do not take into account the spatial structure or rely on difficult to verify assumptions and user-specified parameters. To address these shortcomings, the Markov random field (MRF) FDR (MRF-FDR) technique is presented. MRF-FDR uses a Gaussian mixture model (GMM) to perform FDR control based on empirical-Bayesian principles. An MRF is then used to make the outcome of the GMM spatially coherent. The MRF is fitted using ...

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Cohort Profile: The PATH through life project

Cited by 215 publications

References 34 publications

Increasing Body Mass Index at Midlife is Associated with Increased Cortical Thinning in Alzheimer’s Disease-Vulnerable Regions

Increasing Body Mass Index at Midlife is Associated with Increased Cortical Thinning in Alzheimer’s Disease-Vulnerable Regions

Neuropsychiatric Symptoms and Cognitive Impairment: Understanding the Importance of Co-Morbid Symptoms

Finite mixture models for regression problems

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