94902fd23b3855d39f123e3f193e4011 pmc_33919625.pdf 3350bb3ca5287e5cdca41c0ad54115546e74575c pmc_33919625.pdf 6c6db7c7f07b085a8fbe4e7730c029fe4411bbdcd01c0e2447020b4b823e1cdb pmc_33919625.pdf Title: Determinants of Processing Speed Trajectories among Middle Aged or Older Adults, and Their Association with Chronic Illnesses: The English Longitudinal Study of Aging Subject: The aim of this study was to identify latent groups of similar trajectories in processing speed through aging, as well as factors that are associated with these trajectories. In the context of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project, data from the English Longitudinal Study of Aging (ELSA) (n = 12099) were analyzed. Latent groups of similar trajectories in the processing scores as well as their predictors and covariates were investigated, using group-based trajectory models (GBTM). The coefficient estimates for potential group predictors correspond to parameters of multinomial logit functions that are integrated in the model. Potential predictors included sex, level of education, marital status, level of household wealth, level of physical activity, and history of smoking, while time-varying covariates included incidence of cardiovascular disease (CVD), diabetes mellitus, depressive symptoms, and sleep disturbances. Four trajectories were identified and named after their baseline scores and shapes: High (4.4%), Middle/Stable (31.5%), Low/Stable (44.5%), and Low Decline (19.6%). Female sex, higher levels of education, mild level of physical activity, having been married, and higher level of wealth were associated with a higher probability of belonging to any of the higher groups compared to the Low/Decline that was set as reference, while presence of CVD, diabetes mellitus, and depressive symptoms were associated with lower processing speed scores within most trajectories. All the aforementioned factors might be valid targets for interventions to reduce the burden of age-related cognitive impairment. Keywords: aging; processing speed; ELSA; longitudinal analysis Author: Viktor Gkotzamanis, Giorgos Koliopanos, Albert Sanchez-Niubo, Beatriz Olaya, Francisco Félix Caballero, José Luis Ayuso-Mateos, Somnath Chatterji, Josep Maria Haro and Demosthenes Panagiotakos Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Sun Apr 25 11:44:46 2021 CEST ModDate: Sun Apr 25 17:42:57 2021 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 12 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 488552 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- DDMOHK+VnURWPalladioL Type 1 Custom yes yes no 10 0 LYRLQP+URWPalladioL-Ital Type 1 Custom yes yes no 15 0 CESTJV+URWPalladioL-Bold Type 1 Custom yes yes no 20 0 LJHQWM+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 BTFGED+VnURWPalladioL-Bold Type 1 Custom yes yes no 28 0 PJCGLK+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 59 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 63 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 67 0 EILUJF+CMSY10 Type 1 Builtin yes yes no 79 0 JHFCLP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 99 0 JHFCMA+PalatinoLinotype CID TrueType Identity-H yes yes yes 102 0 JHFCMB+PalatinoLinotype TrueType WinAnsi yes yes no 108 0 JHFCMD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 111 0 JHFFND+PalatinoLinotype,BoldItalic TrueType WinAnsi yes yes no 114 0 LWLIVD+EURM10 Type 1 Builtin yes yes no 140 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-10-02 03:20:40 CEST RepresentationInformation: pmc_33919625.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2023-03-31 12:09:47 CEST Size: 488552 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 257 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Design and Setting Destination: subsection.2.1 Item: Title: Measurements Destination: subsection.2.2 Children: Item: Title: Processing Speed Assessment Destination: subsubsection.2.2.1 Item: Title: Baseline and Follow-Up Assessments Destination: subsubsection.2.2.2 Item: Title: Statistical Analysis Destination: subsection.2.3 Item: Title: Results Destination: section.3 Children: Item: Title: Baseline Characteristics of the Study Participants Destination: subsection.3.1 Item: Title: Trajectories of Processing Speed Destination: subsection.3.2 Item: Title: Processing Speed Trajectories in Relation to Chronic Illnesses Destination: subsection.3.3 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: Destination: section.A. Item: Title: References Destination: specialtable.caption.8 Info: Title: Determinants of Processing Speed Trajectories among Middle Aged or Older Adults, and Their Association with Chronic Illnesses: The English Longitudinal Study of Aging Author: Viktor Gkotzamanis, Giorgos Koliopanos, Albert Sanchez-Niubo, Beatriz Olaya, Francisco Félix Caballero, José Luis Ayuso-Mateos, Somnath Chatterji, Josep Maria Haro and Demosthenes Panagiotakos Subject: The aim of this study was to identify latent groups of similar trajectories in processing speed through aging, as well as factors that are associated with these trajectories. In the context of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project, data from the English Longitudinal Study of Aging (ELSA) (n = 12099) were analyzed. Latent groups of similar trajectories in the processing scores as well as their predictors and covariates were investigated, using group-based trajectory models (GBTM). The coefficient estimates for potential group predictors correspond to parameters of multinomial logit functions that are integrated in the model. Potential predictors included sex, level of education, marital status, level of household wealth, level of physical activity, and history of smoking, while time-varying covariates included incidence of cardiovascular disease (CVD), diabetes mellitus, depressive symptoms, and sleep disturbances. Four trajectories were identified and named after their baseline scores and shapes: High (4.4%), Middle/Stable (31.5%), Low/Stable (44.5%), and Low Decline (19.6%). Female sex, higher levels of education, mild level of physical activity, having been married, and higher level of wealth were associated with a higher probability of belonging to any of the higher groups compared to the Low/Decline that was set as reference, while presence of CVD, diabetes mellitus, and depressive symptoms were associated with lower processing speed scores within most trajectories. All the aforementioned factors might be valid targets for interventions to reduce the burden of age-related cognitive impairment. 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