Is Cancer Protective for Subsequent Alzheimer's Disease Risk? Evidence From the Utah Population Database - PubMed (original) (raw)
Is Cancer Protective for Subsequent Alzheimer's Disease Risk? Evidence From the Utah Population Database
Heidi A Hanson et al. J Gerontol B Psychol Sci Soc Sci. 2017.
Abstract
Objective: Several studies have suggested that cancer is associated with a reduced risk of the development of Alzheimer's disease (AD). This study seeks to improve our understanding of the association between cancer and the development of AD by showing how mortality selection alters this relationship.
Method: A retrospective cohort study was carried out examining 92,425 individuals (47,873 women and 44,552 men) from the Utah Population Database with and without a history of any primary cancer identified by the Utah Cancer Registry. All individuals were aged 65-79 years and free of dementia in 1992 and followed for upwards of 18 years (1992-2009) for AD ascertainment, which was identified using diagnostic information from Medicare claims data.
Results: We replicate previous results suggesting that cancer is associated with reduced risk of subsequent AD under specific statistical model specifications. However, these results should not be interpreted as evidence of an etiological association. We conclude that higher rates of overall mortality among individuals with cancer relative to those without cancer induce the widely reported putative protective association with cancer.
Conclusion: Careful consideration of model specification and the profound effects of mortality selection in the older adult population is essential when investigating the relationship between aging-related diseases such as cancer and AD. We show that cancer does not provide protection from AD as previously described in the literature. Social scientists seeking to understand social disparities in disease outcomes among older adults may therefore want to strongly consider the role of mortality selection which, if uncorrected, may generate biased associations.
Keywords: Alzheimer’s disease; Competing risks; Dementia; Mortality selection.
© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Figures
Figure 1.
Summary of previous findings showing a protective relationship between cancer and Alzheimer’s disease.
Figure 2.
Dementia incidence rates by history of pancreatic cancer in Utah. Source: Utah Population Database.
Figure 3.
Modeling cancer as a time-varying predictor eliminates the inverse relationship between cancer and Alzheimer’s disease (AD). Hazard rate ratios for the effect of cancer diagnosis on AD estimated using Cox regression. Panels A and B are run separately by sex and stratified by birth year. No additional covariates are included in the models. Panel (A) treats cancer as time-independent predictor and Panel (B) treats cancer diagnosis as time-varying predictor. TVEM = time-varying effect model (Models 1 and 2 in Figure 4). Source: Utah Population Database, Utah Cancer Registry, Centers for Medicare and Medicaid Services.
Figure 4.
Considering the time since diagnosis when evaluating the relationship between cancer and Alzheimer’s disease (AD). Hazard rate ratios for the effect of cancer diagnosis on AD estimated using Cox regression. Models displayed are run separately by sex and stratified by birth year. No other covariates are included in the models. Results displayed have separate variables for prebaseline (prevalent) and postbaseline (incident) cancer diagnosis (Model 3 in Figure 4). dx 10+years = cancer diagnosis 10 or more years prior to baseline; dx 5–9 years = cancer diagnosis 5–9 years prior to baseline; dx 0–4 years = cancer diagnosis 0–4 years prior to baseline; TVEM = time-varying effect for cancer diagnosed after baseline. Source: Utah Population Database, Utah Cancer Registry, Centers for Medicare and Medicaid Services.
Figure 5.
(A) The Fine and Gray method to control for the competing risk of death: a misspecified model with useful information. Hazard rate ratios for the effect of cancer diagnosis on Alzheimer’s disease (AD) estimated using the Fine and Gray method for competing risks. Models are run separately by sex and stratified by birth year. No other covariates are included in the models. (B) The Kalbfleisch and Prentice approach: a useful method for correcting for mortality selection. Hazard rate ratios for the effect of cancer diagnosis on AD estimated using Cox regression. Models are run separately by sex and stratified by birth year. Models are controlling for a lagged Charlson Comorbidity Index score, familial excess longevity, religious status, obesity, smoking history, and neighborhood measures of population and median family income. Results are nested within models displayed in Figure 6 with the addition of the aforementioned covariates. dx 10+years = cancer diagnosis 10 or more years prior to baseline; dx 5–9 years = cancer diagnosis 5–9 years prior to baseline; dx 0–4 years = cancer diagnosis 0–4 years prior to baseline; TVEM = time-varying effect for cancer diagnosed after baseline (Model 5 in Figure 4). Source: Utah Population Database, Utah Cancer Registry, Centers for Medicare and Medicaid Services.
Figure 6.
The Kalbfleisch and Prentice approach: prostate cancer risk by stage at diagnosis. Hazard rate ratios for the effect of prostate cancer diagnosis on Alzheimer’s disease estimated using Cox regression. Models are controlling for a lagged Charlson Comorbidity Index score, familial excess longevity, religious status, obesity, smoking history, and neighborhood measures of population and median family income and stratified by birth year. Cancer diagnosis is treated as a time-varying effect.
References
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