Techniques for assessing Alzheimer's disease progression

Charley Ann Budgeon, Kevin Murray, Berwin Turlach, David Baker, Victor L. Villemagne, Samantha Burnham

Research output: Contribution to journalLetter

Abstract

Background
Alzheimer’s disease (AD) is the leading cause of dementia with the incidence of this disease predicted to increase at least three fold by 2050. Curing this disease is a global priority. To achieve this an understanding of the entire disease course is necessary. However, data available on AD progression is quite sparse. Typically available data (AIBL and ADNI) only has around 5 years of follow-up and minimal information about disease stage. Previous research has suggested that progression from healthy to AD could span approximately 30 years1and postulates that disease markers will follow a sigmoidal shape2. However, little empirical evidence is available to support these findings.
Methods
To reconstruct and quantify the underlying longitudinal trajectories for disease markers were evaluated using individuals’ short-term follow-up data. Simulated sparse follow-up data for a number of individuals was created in order to investigate possible solutions, with the least deviation from the underlying simulated model considered most accurate. A four-step modeling approach was adopted that 1) determined individual slopes and anchor points, 2) fitted polynomials to the reciprocated data, 3) integrated and 4) inverted the fitted polynomial to obtain the longitudinal trajectories. Variations in the approach were conducted to determine the optimal method: mixed models were contrasted to linear regression in step 1) and different orders of polynomials were considered in step 2).

Results
Longitudinal trajectories of possible disease markers (Figure 1) were successfully reproduced from the sparse individual data. All methods accurately reproduced the underlying curve (0-3.6% difference between the simulated and predicted curves). The mixed model variations performed better than those using linear regression and there were minimal differences between the different order polynomials.

Conclusions
Simple modeling techniques are able to reconstruct generalized underlying longitudinal trajectories from individuals’ short-term follow-up data. The elucidation of these generalized underlying progression curves allow the timing associated with disease progression to be determined, providing imperative information for a wide range of applications including the design and timing of therapeutic interventions and planning for events such as residential care needs.

1 10.1016/S1474-4422(13)70044-9
2 10.1016/S1474-4422(09)70299-6
Original languageEnglish
Pages (from-to)P867-P868
Number of pages2
JournalAlzheimer's and Dementia
Volume11
Issue number7 (Supplement)
DOIs
Publication statusPublished - Jul 2015

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Disease Progression
Alzheimer Disease
Linear Models
Dementia
Incidence
Research

Cite this

Budgeon, Charley Ann ; Murray, Kevin ; Turlach, Berwin ; Baker, David ; Villemagne, Victor L. ; Burnham, Samantha. / Techniques for assessing Alzheimer's disease progression. In: Alzheimer's and Dementia. 2015 ; Vol. 11, No. 7 (Supplement). pp. P867-P868.
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title = "Techniques for assessing Alzheimer's disease progression",
abstract = "BackgroundAlzheimer’s disease (AD) is the leading cause of dementia with the incidence of this disease predicted to increase at least three fold by 2050. Curing this disease is a global priority. To achieve this an understanding of the entire disease course is necessary. However, data available on AD progression is quite sparse. Typically available data (AIBL and ADNI) only has around 5 years of follow-up and minimal information about disease stage. Previous research has suggested that progression from healthy to AD could span approximately 30 years1and postulates that disease markers will follow a sigmoidal shape2. However, little empirical evidence is available to support these findings.MethodsTo reconstruct and quantify the underlying longitudinal trajectories for disease markers were evaluated using individuals’ short-term follow-up data. Simulated sparse follow-up data for a number of individuals was created in order to investigate possible solutions, with the least deviation from the underlying simulated model considered most accurate. A four-step modeling approach was adopted that 1) determined individual slopes and anchor points, 2) fitted polynomials to the reciprocated data, 3) integrated and 4) inverted the fitted polynomial to obtain the longitudinal trajectories. Variations in the approach were conducted to determine the optimal method: mixed models were contrasted to linear regression in step 1) and different orders of polynomials were considered in step 2).ResultsLongitudinal trajectories of possible disease markers (Figure 1) were successfully reproduced from the sparse individual data. All methods accurately reproduced the underlying curve (0-3.6{\%} difference between the simulated and predicted curves). The mixed model variations performed better than those using linear regression and there were minimal differences between the different order polynomials.ConclusionsSimple modeling techniques are able to reconstruct generalized underlying longitudinal trajectories from individuals’ short-term follow-up data. The elucidation of these generalized underlying progression curves allow the timing associated with disease progression to be determined, providing imperative information for a wide range of applications including the design and timing of therapeutic interventions and planning for events such as residential care needs. 1 10.1016/S1474-4422(13)70044-92 10.1016/S1474-4422(09)70299-6",
author = "Budgeon, {Charley Ann} and Kevin Murray and Berwin Turlach and David Baker and Villemagne, {Victor L.} and Samantha Burnham",
year = "2015",
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doi = "10.1016/j.jalz.2015.08.057",
language = "English",
volume = "11",
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Techniques for assessing Alzheimer's disease progression. / Budgeon, Charley Ann; Murray, Kevin; Turlach, Berwin; Baker, David; Villemagne, Victor L.; Burnham, Samantha.

In: Alzheimer's and Dementia, Vol. 11, No. 7 (Supplement), 07.2015, p. P867-P868.

Research output: Contribution to journalLetter

TY - JOUR

T1 - Techniques for assessing Alzheimer's disease progression

AU - Budgeon, Charley Ann

AU - Murray, Kevin

AU - Turlach, Berwin

AU - Baker, David

AU - Villemagne, Victor L.

AU - Burnham, Samantha

PY - 2015/7

Y1 - 2015/7

N2 - BackgroundAlzheimer’s disease (AD) is the leading cause of dementia with the incidence of this disease predicted to increase at least three fold by 2050. Curing this disease is a global priority. To achieve this an understanding of the entire disease course is necessary. However, data available on AD progression is quite sparse. Typically available data (AIBL and ADNI) only has around 5 years of follow-up and minimal information about disease stage. Previous research has suggested that progression from healthy to AD could span approximately 30 years1and postulates that disease markers will follow a sigmoidal shape2. However, little empirical evidence is available to support these findings.MethodsTo reconstruct and quantify the underlying longitudinal trajectories for disease markers were evaluated using individuals’ short-term follow-up data. Simulated sparse follow-up data for a number of individuals was created in order to investigate possible solutions, with the least deviation from the underlying simulated model considered most accurate. A four-step modeling approach was adopted that 1) determined individual slopes and anchor points, 2) fitted polynomials to the reciprocated data, 3) integrated and 4) inverted the fitted polynomial to obtain the longitudinal trajectories. Variations in the approach were conducted to determine the optimal method: mixed models were contrasted to linear regression in step 1) and different orders of polynomials were considered in step 2).ResultsLongitudinal trajectories of possible disease markers (Figure 1) were successfully reproduced from the sparse individual data. All methods accurately reproduced the underlying curve (0-3.6% difference between the simulated and predicted curves). The mixed model variations performed better than those using linear regression and there were minimal differences between the different order polynomials.ConclusionsSimple modeling techniques are able to reconstruct generalized underlying longitudinal trajectories from individuals’ short-term follow-up data. The elucidation of these generalized underlying progression curves allow the timing associated with disease progression to be determined, providing imperative information for a wide range of applications including the design and timing of therapeutic interventions and planning for events such as residential care needs. 1 10.1016/S1474-4422(13)70044-92 10.1016/S1474-4422(09)70299-6

AB - BackgroundAlzheimer’s disease (AD) is the leading cause of dementia with the incidence of this disease predicted to increase at least three fold by 2050. Curing this disease is a global priority. To achieve this an understanding of the entire disease course is necessary. However, data available on AD progression is quite sparse. Typically available data (AIBL and ADNI) only has around 5 years of follow-up and minimal information about disease stage. Previous research has suggested that progression from healthy to AD could span approximately 30 years1and postulates that disease markers will follow a sigmoidal shape2. However, little empirical evidence is available to support these findings.MethodsTo reconstruct and quantify the underlying longitudinal trajectories for disease markers were evaluated using individuals’ short-term follow-up data. Simulated sparse follow-up data for a number of individuals was created in order to investigate possible solutions, with the least deviation from the underlying simulated model considered most accurate. A four-step modeling approach was adopted that 1) determined individual slopes and anchor points, 2) fitted polynomials to the reciprocated data, 3) integrated and 4) inverted the fitted polynomial to obtain the longitudinal trajectories. Variations in the approach were conducted to determine the optimal method: mixed models were contrasted to linear regression in step 1) and different orders of polynomials were considered in step 2).ResultsLongitudinal trajectories of possible disease markers (Figure 1) were successfully reproduced from the sparse individual data. All methods accurately reproduced the underlying curve (0-3.6% difference between the simulated and predicted curves). The mixed model variations performed better than those using linear regression and there were minimal differences between the different order polynomials.ConclusionsSimple modeling techniques are able to reconstruct generalized underlying longitudinal trajectories from individuals’ short-term follow-up data. The elucidation of these generalized underlying progression curves allow the timing associated with disease progression to be determined, providing imperative information for a wide range of applications including the design and timing of therapeutic interventions and planning for events such as residential care needs. 1 10.1016/S1474-4422(13)70044-92 10.1016/S1474-4422(09)70299-6

U2 - 10.1016/j.jalz.2015.08.057

DO - 10.1016/j.jalz.2015.08.057

M3 - Letter

VL - 11

SP - P867-P868

JO - ALZHEIMERS & DEMENTIA

JF - ALZHEIMERS & DEMENTIA

SN - 1552-5260

IS - 7 (Supplement)

ER -