Memory in individuals with mild cognitive impairment in relation to APOE and CSF Aβ42

2010 ◽  
Vol 22 (4) ◽  
pp. 598-606 ◽  
Author(s):  
Valgeir Thorvaldsson ◽  
Arto Nordlund ◽  
Ivar Reinvang ◽  
Kaj Blennow ◽  
Henrik Zetterberg ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Memory Performance ◽  
Interactive Effect ◽  
Amyloid Β ◽  
Total Sample ◽  
Increased Risk ◽  
Latent Curve ◽  
Latent Curve Models ◽  
Ε4 Allele

ABSTRACTBackground: The ε4 allele of the apolipoprotein E (APOE) gene and low levels of cerebrospinal fluid (CSF) amyloid β-proteins 42 (Aβ) have previously been associated with increased risk of cognitive decline in old age. In this study we examine the interaction of these markers with episodic memory in a sample identified as having mild cognitive impairment (MCI).Methods: The sample (N = 149) was drawn from the Gothenburg MCI study and measured according to three free recall tests on three occasions spanning over four years. Second-order Latent Curve Models (LCM) were fitted to the data.Results: Analyses accounting for age, gender, education, APOE, Aβ42, and interaction between APOE and Aβ42 revealed that the ε4 allele was significantly associated with level of memory performance in the presence of low Aβ42 values (≤452 ng/L). Associations between memory performance and Aβ42 were significant among the ε4 carriers but not among the non-carriers. The Aβ42 marker was, however, significantly associated with changes in memory over the study time period in the total sample.Conclusion: The findings support the hypothesis of an interactive effect of APOE and Aβ42 for memory decline in MCI patients.

Latent Curve Models

2017 ◽  
pp. 294-304
Author(s):  
Joop J. Hox ◽  
Mirjam Moerbeek ◽  
Rens van de Schoot
Keyword(s):  
Latent Curve ◽  
Latent Curve Models

scholarly journals THE INFLUENCE OF LONELINESS AND RURAL RESIDENCE ON DEPRESSION IN LATER LIFE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S828-S828
Author(s):  
Na Sun ◽  
Cassandra Hua ◽  
Xiao Qiu ◽  
J Scott Brown
Keyword(s):  
Older Adults ◽  
Rural Areas ◽  
Urban Areas ◽  
Later Life ◽  
Rural Residence ◽  
Time Data ◽  
Latent Curve ◽  
Rural Residency ◽  
Latent Curve Models ◽  
Over Time

Abstract Loneliness is associated with depression among older adults. Limited research has examined the role of rurality in relationship to loneliness and depression; the extant research has mixed findings. The socioemotional selectivity theory states that as people age the quality of relationships become more important than the quantity (English & Carstensen, 2016). Individuals in rural areas may have a low quantity of relationships but deeper social ties within the community; thus, they may be less likely to become depressed over time. The association between loneliness and depression may be amplified for people in non-rural areas because they are surrounded by other people but lack close relationships that are most important during the aging process. This study examines the effect of living in rural areas on loneliness on predicting baseline depression and loneliness, as well as changes in these outcomes over time. Data are from the 2006-2014 waves of Health Retirement Study. Regression models examine the relationship between depression loneliness and rural residence controlling for health conditions and demographic characteristics. Latent curve models examine the disparity in trajectories of loneliness and depressive symptoms by urban and rural residence. Older adults who feel lonely (p<.001) and in urban areas (p<.0.05) are more likely to be depressed. Furthermore, the effect of loneliness on depression is weakened by rural residence (p<.05). It is salient to understand the protective effect of rural residency on depression among older adults in the U.S. We discuss implications for policy.

Using LISREL to Fit Nonlinear Latent Curve Models

2008 ◽  
Vol 15 (2) ◽  
pp. 346-369 ◽  
Author(s):  
Shelley A. Blozis ◽  
Jeffrey R. Harring ◽  
Gerhard Mels
Keyword(s):  
Latent Curve ◽  
Latent Curve Models

scholarly journals Mixture Modeling for Lifespan Developmental Research

2019 ◽  
Author(s):  
Alexandre J.S. Morin ◽  
David Litalien
Keyword(s):  
Mixture Models ◽  
Structural Equation ◽  
Equation Modeling ◽  
Modeling Framework ◽  
Mixture Regression ◽  
Growth Mixture ◽  
Multiple Group ◽  
Latent Curve ◽  
Latent Curve Models ◽  
Over Time

As part of the Generalized Structural Equation Modeling framework, mixture models are person-centered analyses seeking to identify distinct subpopulations, or profiles, of participants differing quantitatively and qualitatively from one another on a configuration of indicators and/or relations among these indicators. Mixture models are typological (resulting in a classification system), probabilistic (each participant having a probability of membership into all profiles based on prototypical similarity), and exploratory (the optimal model is typically selected based on a comparison of alternative specifications) in nature, and can take different forms. Latent profile analyses seek to identify subpopulations of participants differing from one another on a configuration of indicators and can be extended to factor mixture analyses allowing for the incorporation of latent factors to the model. In contrast, mixture regression analyses seek to identify subpopulations of participants’ differing from one another in terms of relations among profile indicators. These analyses can be extended to the multiple-group and/or longitudinal analyses, allowing researchers to conduct tests of profile similarity across different samples of participants or time points, and latent transition analyses can be used to assess probabilities of profiles transition over time among a sample of participants (i.e., within person stability and change in profile membership). Finally, growth mixture analyses are built from latent curve models and seek to identify subpopulations of participants following quantitatively and qualitatively distinct trajectories over time. All of these models can accommodate covariates, used either as predictors, correlates, or outcomes, and can even be extended to tests of mediation and moderation.

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