322 - Heterogeneity in dynamic change of cognitive function among Chinese elderly: A growth mixture model

Objectives:Our aim is to use the growth mixture model (GMM) to distinguish different trajectories of cognitive change in Chinese geriatric population and identify risk factors for cognitive decline in each subpopulation.Methods:We obtained data from the Chinese Longitudinal Health Longevity Survey, using the Chinese Mini-Mental State Examination (C-MMSE) as a proxy for cognitive function. We applied the GMM to identify heterogeneous subpopulations and potential risk factors.Results:Our sample included 2850 older adults, 1387 (48.7%) male and 1463 (51.3%) female with age range of 62 to 108 (average of 72.3). Using GMM and best fit statistics, we identified two distinct subgroups in respect to their longitudinal cognitive function: cognitively stable (91.4%) group with 0.42 C-MMSE points decline per 3 years, and cognitively declining (8.6%) group with 4.76 C-MMSE points decline per 3 years. Of note, vision impairment and hearing impairment had the highest associations with cognitive decline, with stronger association found in the cognitively declining group than the cognitively stable group. Cognitive activities were protective in both groups. Diabetes was associated with cognitive decline in cognitive declining group. Physical activities, social activities and intake of fresh vegetables, fruits, and fish products were protective in cognitive stable group.Conclusions:Using GMM, we identified heterogeneity in trajectories of cognitive change in Chinese elders. Moreover, we found risk factors specific to each subgroup, which should be considered in future studies.

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Heterogeneity in the dynamic change of cognitive function among older Chinese people: A growth mixture model

International Journal of Geriatric Psychiatry ◽

10.1002/gps.5334 ◽

2020 ◽

Vol 35 (10) ◽

pp. 1123-1133

Author(s):

Peiyuan Qiu ◽

Miao Zeng ◽

Weihong Kuang ◽

Steven Siyao Meng ◽

Yan Cai ◽

...

Keyword(s):

Cognitive Function ◽

Mixture Model ◽

Dynamic Change ◽

Chinese People ◽

Growth Mixture Model ◽

Growth Mixture ◽

Older Chinese

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One size fits all? Why we need more sophisticated analytical methods in the explanation of trajectories of cognition in older age and their potential risk factors

International Psychogeriatrics ◽

10.1017/s1041610209990937 ◽

2009 ◽

Vol 22 (2) ◽

pp. 291-299 ◽

Cited By ~ 34

Author(s):

Graciela Muniz Terrera ◽

Carol Brayne ◽

Fiona Matthews ◽

Keyword(s):

Risk Factors ◽

Cognitive Impairment ◽

Cognitive Decline ◽

Potential Risk ◽

Analytical Methods ◽

Cognitive Change ◽

Population Based ◽

Growth Mixture ◽

Potential Risk Factors ◽

Rate Of Decline

ABSTRACTBackground: Cognitive decline in old age varies among individuals. The identification of groups of individuals with similar patterns of cognitive change over time may improve our ability to see whether the effect of risk factors is consistent across groups.Methods: Whilst accounting for the missing data, growth mixture models (GMM) were fitted to data from four interview waves of a population-based longitudinal study of aging, the Cambridge City over 75 Cohort Study (CC75C). At all interviews global cognition was assessed using the Mini-mental State Examination (MMSE).Results: Three patterns were identified: a slow decline with age from a baseline of cognitive ability (41% of sample), an accelerating decline from a baseline of cognitive impairment (54% of sample) and a steep constant decline also from a baseline of cognitive impairment (5% of sample). Lower cognitive scores in those with less education were seen at baseline for the first two groups. Only in those with good performance and steady decline was the effect of education strong, with an increased rate of decline associated with poor education. Good mobility was associated with higher initial score in the group with accelerating change but not with rate of decline.Conclusion: Using these analytical methods it is possible to detect different patterns of cognitive change with age. In this investigation the effect of education differs with group. To understand the relationship of potential risk factors for cognitive decline, careful attention to dropout and appropriate analytical methods, in addition to long-term detailed studies of the population points, are required.

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PATTERNS OF HETEROGENEITY IN COGNITIVE CHANGE: GROWTH MIXTURE MODEL ANALYSES IN NINE EUROPEAN SAMPLES

The Gerontologist ◽

10.1093/geront/gnv608.03 ◽

2015 ◽

Vol 55 (Suppl_2) ◽

pp. 308-308

Keyword(s):

Mixture Model ◽

Cognitive Change ◽

Growth Mixture Model ◽

Growth Mixture

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Cognitive decline trajectories and influencing factors in China: a non-normal growth mixture model analysis

Archives of Gerontology and Geriatrics ◽

10.1016/j.archger.2021.104381 ◽

2021 ◽

pp. 104381

Author(s):

Weiye Yu ◽

Rong Chen ◽

Minqiang Zhang ◽

Zonglong Li ◽

Fangxin Gao ◽

...

Keyword(s):

Cognitive Decline ◽

Mixture Model ◽

Influencing Factors ◽

Normal Growth ◽

Model Analysis ◽

Growth Mixture Model ◽

Growth Mixture

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Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts

PLoS ONE ◽

10.1371/journal.pone.0248844 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0248844

Author(s):

Hai Nguyen ◽

Dario Moreno-Agostino ◽

Kia-Chong Chua ◽

Silia Vitoratou ◽

A. Matthew Prina

Keyword(s):

Mixture Model ◽

Multinomial Logistic Regression ◽

Healthy Ageing ◽

Rapid Decline ◽

Stable Group ◽

Growth Mixture Model ◽

Lifestyle Risk Factors ◽

Growth Mixture ◽

Group 2 ◽

Lifestyle Risk

Objectives In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. Setting and participants Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA). Methods Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors. Results Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively). Conclusion Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity.

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Joint Growth Trajectories of Bullying Perpetration and Victimization Among Korean Adolescents: Estimating a Second-Order Growth Mixture Model–Factor-of-Curves With Low Self-Control and Opportunity Correlates

Crime & Delinquency ◽

10.1177/0011128719890271 ◽

2019 ◽

Vol 66 (9) ◽

pp. 1296-1337

Author(s):

Sujung Cho ◽

Jin Ree Lee

Keyword(s):

Mixture Model ◽

Second Order ◽

Self Control ◽

Growth Trajectories ◽

Stable Group ◽

Growth Mixture Model ◽

Korean Adolescents ◽

Bullying Perpetration ◽

Growth Mixture ◽

Bully Victims

Joint growth trajectories of bullying perpetration and victimization were examined using 5-year panel data (2004–2008) from a sample of 2,844 South Korean adolescents between the ages of 11 and 15 (fourth to eighth grade). The second-order growth mixture model revealed three distinct subgroups: bully-victims to low bully-victims transition (9.9%); moderate bully-victims to victim transition (6.8%); and a limited involvement/stable group (83.3%). Respondents with less self-control who associated with delinquent peers were more likely to be members of both the bully-victims to low bully-victims transition and the moderate bully-victims to victim transition groups, compared with the limited involvement/stable group. Relative to the limited involvement/stable group, adolescents with less self-control were more likely to be members of both transition groups even after controlling for opportunity measures. Delinquent peer associations partially mediated these associations.

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