Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims Database

Background: Trajectories of comorbidities among individuals at risk of Alzheimer’s disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention,...

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Main Authors: Lesley M. Butler, Richard Houghton, Anup Abraham, Maria Vassilaki, Gonzalo Durán-Pacheco
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-10-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2021.749305/full
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spelling doaj-2efc2bb7e8f3458e93a36423e8e74c2c2021-10-08T07:14:38ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-10-011510.3389/fnins.2021.749305749305Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims DatabaseLesley M. Butler0Richard Houghton1Anup Abraham2Maria Vassilaki3Gonzalo Durán-Pacheco4F. Hoffmann-La Roche Ltd., Basel, SwitzerlandF. Hoffmann-La Roche Ltd., Basel, SwitzerlandGenesis Research, Hoboken, NJ, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesF. Hoffmann-La Roche Ltd., Basel, SwitzerlandBackground: Trajectories of comorbidities among individuals at risk of Alzheimer’s disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention, and help slow the rate of cognitive and functional decline in AD. This case-control study was performed to compare the prevalence of comorbidities between AD cases and controls during the 5 years prior to diagnosis (or index date for controls); and to identify comorbidities with a differential time-dependent prevalence trajectory during the 5 years prior to AD diagnosis.Methods: Incident AD cases and individually matched controls were identified in a United States claims database between January 1, 2000 and December 31, 2016. AD status and comorbidities were defined based on the presence of diagnosis codes in administrative claims records. Generalized estimating equations were used to assess evidence of changes over time and between AD and controls. A principal component analysis and hierarchical clustering was performed to identify groups of AD-related comorbidities with respect to prevalence changes over time (or trajectory), and differences between AD and controls.Results: Data from 186,064 individuals in the IBM MarketScan Commercial Claims and Medicare Supplementary databases were analyzed (93,032 AD cases and 93,032 non-AD controls). In total, there were 177 comorbidities with a ≥ 5% prevalence. Five main clusters of comorbidities were identified. Clusters differed between AD cases and controls in the overall magnitude of association with AD, in their diverging time trajectories, and in comorbidity prevalence. Three clusters contained comorbidities that notably increased in frequency over time in AD cases but not in controls during the 5-year period before AD diagnosis. Comorbidities in these clusters were related to the early signs and/or symptoms of AD, psychiatric and mood disorders, cerebrovascular disease, history of hazard and injuries, and metabolic, cardiovascular, and respiratory complaints.Conclusion: We demonstrated a greater comorbidity burden among those who later developed AD vs. controls, and identified comorbidity clusters that could distinguish these two groups. Further investigation of comorbidity burden is warranted to facilitate early detection of individuals at risk of developing AD.https://www.frontiersin.org/articles/10.3389/fnins.2021.749305/fullAlzheimer’s diseasecomorbidityprincipal component analysishierarchical cluster analysisMarketScanMedicare
collection DOAJ
language English
format Article
sources DOAJ
author Lesley M. Butler
Richard Houghton
Anup Abraham
Maria Vassilaki
Gonzalo Durán-Pacheco
spellingShingle Lesley M. Butler
Richard Houghton
Anup Abraham
Maria Vassilaki
Gonzalo Durán-Pacheco
Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims Database
Frontiers in Neuroscience
Alzheimer’s disease
comorbidity
principal component analysis
hierarchical cluster analysis
MarketScan
Medicare
author_facet Lesley M. Butler
Richard Houghton
Anup Abraham
Maria Vassilaki
Gonzalo Durán-Pacheco
author_sort Lesley M. Butler
title Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims Database
title_short Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims Database
title_full Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims Database
title_fullStr Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims Database
title_full_unstemmed Comorbidity Trajectories Associated With Alzheimer’s Disease: A Matched Case-Control Study in a United States Claims Database
title_sort comorbidity trajectories associated with alzheimer’s disease: a matched case-control study in a united states claims database
publisher Frontiers Media S.A.
series Frontiers in Neuroscience
issn 1662-453X
publishDate 2021-10-01
description Background: Trajectories of comorbidities among individuals at risk of Alzheimer’s disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention, and help slow the rate of cognitive and functional decline in AD. This case-control study was performed to compare the prevalence of comorbidities between AD cases and controls during the 5 years prior to diagnosis (or index date for controls); and to identify comorbidities with a differential time-dependent prevalence trajectory during the 5 years prior to AD diagnosis.Methods: Incident AD cases and individually matched controls were identified in a United States claims database between January 1, 2000 and December 31, 2016. AD status and comorbidities were defined based on the presence of diagnosis codes in administrative claims records. Generalized estimating equations were used to assess evidence of changes over time and between AD and controls. A principal component analysis and hierarchical clustering was performed to identify groups of AD-related comorbidities with respect to prevalence changes over time (or trajectory), and differences between AD and controls.Results: Data from 186,064 individuals in the IBM MarketScan Commercial Claims and Medicare Supplementary databases were analyzed (93,032 AD cases and 93,032 non-AD controls). In total, there were 177 comorbidities with a ≥ 5% prevalence. Five main clusters of comorbidities were identified. Clusters differed between AD cases and controls in the overall magnitude of association with AD, in their diverging time trajectories, and in comorbidity prevalence. Three clusters contained comorbidities that notably increased in frequency over time in AD cases but not in controls during the 5-year period before AD diagnosis. Comorbidities in these clusters were related to the early signs and/or symptoms of AD, psychiatric and mood disorders, cerebrovascular disease, history of hazard and injuries, and metabolic, cardiovascular, and respiratory complaints.Conclusion: We demonstrated a greater comorbidity burden among those who later developed AD vs. controls, and identified comorbidity clusters that could distinguish these two groups. Further investigation of comorbidity burden is warranted to facilitate early detection of individuals at risk of developing AD.
topic Alzheimer’s disease
comorbidity
principal component analysis
hierarchical cluster analysis
MarketScan
Medicare
url https://www.frontiersin.org/articles/10.3389/fnins.2021.749305/full
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