A Mathematical Model of Alzheimer's Disease and the Apoe Gene

Macdonald, Angus S.; Pritchard, D

doi:10.2143/ast.30.1.504627

Cited by 32 publications

(37 citation statements)

References 82 publications

(141 reference statements)

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“…23 This degree of impairment is consistent with, for example, an individual who has just been diagnosed as suffering from Alzheimer's Disease (Macdonald & Pritchard (2000)). Results for such an individual are presented in Figure 5, which was constructed using the same set of parameters ( 5 2 = k , 10000 2 = d and 0488 .…”

Section: Adverse Mortality Selection: Impaired Livessupporting

confidence: 62%

Pensionmetrics 2: stochastic pension plan design during the distribution phase

Blake

Cairns

Dowd

2003

Insurance: Mathematics and Economics

135

134

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We consider the choices available to a defined contribution (DC) pension plan member at the time of retirement for conversion of his pension fund into a stream of retirement income. In particular, we compare the purchase at retirement age of a conventional life annuity (i.e., a bond-based investment) with distribution programmes involving differing exposures to equities during retirement. The residual fund at the time of the plan member's death can either be bequested to his estate or revert to the life office in exchange for the payment of survival credits while alive.The most important decision, in terms of cost to the plan member, is the level of equity investment. We also find that the optimal age to annuitise depends on the bequest utility and the investment performance of the fund during retirement.

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Section: Adverse Mortality Selection: Impaired Livessupporting

confidence: 62%

Pensionmetrics 2: stochastic pension plan design during the distribution phase

Blake

Cairns

Dowd

2003

Insurance: Mathematics and Economics

135

134

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“…In the case of the ApoE genotype, we can for example wonder if creating a distance being equal to one between all pairs of different genotypes is really optimal. Having d(( 2 , 2 ), ( 4 , 4 )) higher than d(( 2 , 2 ), ( 2 , 3 )) would not be absurd given the known biological impact of the ApoE alleles [5]. This example illustrates that the distance-based extension is not necessarily well suited for discrete clinical variables.…”

Section: Discussionmentioning

confidence: 99%

“…Three ApoE alleles exist ( 2 , 3 , 4 ), and since each individual carries two alleles, six ApoE genotypes are possible. The 4 allele has been shown to increase the risk of developing AD, whereas 2 decreases this risk [5]. Moreover an Aβ 42 protein analysis of cerebrospinal fluid (CSF) is provided.…”

Section: Datamentioning

confidence: 99%

Combining imaging and clinical data in manifold learning: Distance-based and graph-based extensions of Laplacian Eigenmaps

Fiot

Fripp

Cohen

2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

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Manifold learning techniques have been widely used to produce low-dimensional representations of patient brain magnetic resonance (MR) images. Diagnosis classifiers trained on these coordinates attempt to separate healthy, mild cognitive impairment and Alzheimer's disease patients. The performance of such classifiers can be improved by incorporating clinical data available in most large-scale clinical studies. However, the standard non-linear dimensionality reduction algorithms cannot be applied directly to imaging and clinical data. In this paper, we introduce a novel extension of Laplacian Eigenmaps that allow the computation of manifolds while combining imaging and clinical data. This method is a distance-based extension that suits better continuous clinical variables than the existing graph-based extension, which is suitable for clinical variables in finite discrete spaces. These methods were evaluated in terms of classification accuracy using 288 MR images and clinical data (ApoE genotypes, Aβ 42 concentrations and mini-mental state exam (MMSE) cognitive scores) of patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) study.

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“…Thus Macdonald & Pritchard (2000) trawled the large literature on AD (up to about 1998), and found just one study that reported age-related risks in enough detail. (b) Premium rates based on an epidemiological study are functions of the data underlying that study.…”

Section: Longevity Genes and Annuity Pricingmentioning

confidence: 99%

Pensions and genetics: can longevity genes be reliable risk factors for annuity pricing?

Macdonald

McIvor²

2010

Scandinavian Actuarial Journal

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We consider a number of gene variants that have been found to affect longevity. Their effects have been modelled using Cox or logistic regressions, whose fitted parameters have simple asymptotic sampling distributions. The expected present value of a life annuity allowing for these genetic risk estimates inherits a sampling distribution, which can be found by simulation. We find that possibly significant uncertainty about annuity premiums may be overlooked if the standard errors of parameters estimated in medical studies are ignored by medical underwriters. Such considerations may play an important part when the acceptability of using a risk factor in underwriting is conditional on proof of its relevance and reliability. This is the current position in respect of genetic information in many countries, most prominently in the UK.

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A Mathematical Model of Alzheimer's Disease and the Apoe Gene

Cited by 32 publications

References 82 publications

Pensionmetrics 2: stochastic pension plan design during the distribution phase

Pensionmetrics 2: stochastic pension plan design during the distribution phase

Combining imaging and clinical data in manifold learning: Distance-based and graph-based extensions of Laplacian Eigenmaps

Pensions and genetics: can longevity genes be reliable risk factors for annuity pricing?

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