Trajectories of cognitive function and their determinants in older people: 12 years of follow-up in the Chinese Longitudinal Healthy Longevity Survey

Tu, Lihui; Lv, Xiaozhen; Yuan, Changzheng; Zhang, Ming; Fan, Zhipeng; Xu, Xiaolin; Zeng, Yi; Yu, Xinguang; Wang, Huali

doi:10.1017/s1041610220000538

Cited by 34 publications

(51 citation statements)

References 48 publications

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“…There were three distinct trajectories of cognition, namely, persistently-high, medium-decrease, and low-increase. Compared with previous studies [21][22], similarly, the persistently-high class accounted for most of the population, indicating that majority of participants kept stable cognition through their aging periods. However, we also found an uncommon trajectory of low-increase, which was featured for low cognitive score in baseline and gradually improvement subsequently.…”

Section: Discussioncontrasting

confidence: 63%

Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults

Jia

Xiang

et al. 2021

Preprint

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Background Cognition represents heterogeneous in trajectories with increasing age. Early identification of such trend is essential for the prevention of high risk population related to cognition decline. This study aimed to explore the heterogeneity and determinants of cognitive trajectories, and construct prediction models for distinguishing cognitive trajectories among the middle-aged and elderly Chinese people at a community level. Methods Data was retrospectively collected from the China Health and Retirement Longitudinal Study with a consecutive survey in 3 waves (2011, 2013, 2015). The Mini-mental State Examination (MMSE) scale was used for cognitive measurement. The heterogeneity of cognitive trajectories was identified through mixed growth model, and the determinants of cognitive trajectories were analyzed by logistic regression. Machine learning (ML) algorithms, namely logistic regression (LR), support vector machine (SVM), and random forest (RF), combined with recursive feature elimination were used to predict cognitive trajectories using epidemiological variables. Area under the receiver operating characteristic curve (AUROC) and brier score were used to assess discrimination and calibration, respectively. Results Three cognitive trajectories were identified: “persistently-high” (PH, 83.3%) class with a high cognitive score in baseline and remained high subsequently, “medium-decrease” (MD, 6.6%) class with a medium cognitive score in baseline and a rapid decline subsequently, “low-increase” (LI, 10.1%) with the lowest baseline cognitive score and a rapid growth subsequently. For classification of PH vs. LI and MD vs. LI, the AUROCs of three ML methods were almost larger than 0.90, and brier scores were almost less than 0.10. For PH vs. MD, ML performed less well with the maximum AUROCs close to 0.68, while the calibration was still good. The initial MMSE score was the most significant predictors for distinguishing MD vs. LI and PH vs. LI, while age was the most important predictors in distinguishing MD vs. PH. Conclusions Prediction of cognitive trajectories and identification of its key predictors are of great significance for understanding the heterogeneity of cognitive trajectories as well as interventions for cognitive decline.

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Section: Discussioncontrasting

confidence: 63%

Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults

Jia

Xiang

et al. 2021

Preprint

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“…The 2classes model were more popular in studies such as distinguishing AD converter vs. non-converter, and stable vs. decline [11,32,33]. The results revealed that stable-function class accounted for most of the population [5,34], indicating that majority of participants kept stable cognitive function through their aging periods.…”

Section: Discussionmentioning

confidence: 99%

“…Speci cally, leisure activities included housework, personal outdoor activities, garden work, reading newspapers or books, raising domestic animals or pets, playing cards or mahjong, watching TV or listening to the radio, and taking part in some social activities. According to previous study [5], each item had 5 levels and was coded as follows: "almost every day" (coded as 1), "not daily, but once a week" (coded as 2), "not weekly, but at least once a month" (coded as 3), "not monthly, but sometimes" (coded as 4), and "never" (coded as 5). The total scores of the above 8 items ranged 8-40.…”

Section: Measurement Of Predictorsmentioning

confidence: 99%

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Trajectories of Cognitive Function and Their Machine Learning-based Prediction Models in Older Adults: a 16 Years Follow-up in the Chinese Longitudinal Healthy Longevity Survey

Jia

Xiang

et al. 2021

Preprint

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Background: Cognitive decline shows heterogeneous with increasing age. The trajectory of cognitive function is yet to be fully investigated in older Chinese. Early identification of such trend is essential for the prevention of high-risk population related to cognitive decline. This study aimed to explore the heterogeneity and determinants of cognitive trajectories, and construct prediction models for distinguishing cognitive trajectories among the elderly Chinese at a community level.Methods: This study included 3502 older adults aged 65-105 years at their first observations in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018. The Chinese version Mini-Mental State Examination (MMSE) was used for measuring cognitive function. The heterogeneity of cognitive trajectories was identified through mixed growth model, and the determinants of cognitive trajectories were analyzed by logistic regression. Under a consideration of feature selection, machine learning algorithms, namely logistic regression (LR), support vector machine (SVM), and an integrated algorithm combining LR and SVM (stacking), were used to predict cognitive trajectories using epidemiological variables. Area under the receiver operating characteristic curve (AUROC) and brier score were used to assess discrimination and calibration, respectively. Results: Two distinct cognitive trajectories were identified according to the changes of MMSE scores: stable-function (93.6%), and rapid-decline (6.4%). Older age, female gender, Han ethnicity, having no schooling, rural residents, low frequency leisure activities, and low baseline ADL score were associated with the rapid decline in cognitive function. For classification of two trajectories, the performance of LR, SVM and stacking algorithms with feature selection were comparable, of which achieved 0.66 of balanced accuracy, 0.74 of sensitivity, 0.73 of F1 score, 0.66 of AUROC, and 0.25 of brier score. Among the predictors, age and psychological well-being were the most effective variables for distinguishing two trajectories.Conclusions: Two cognitive trajectories were identified among older Chinese. The identified determinants of trajectories could be targeted for constructing early risk prediction models.

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“…The identification of risk factors and preventative strategies are primary objectives in research on impaired cognitive functioning in older adults (Brayne, 2007;Treichler and Jeste, 2019). Indeed, general health-related risk factors have been identified during midlife and old age (Launer, 2005;Launer, 2007;Kaufman and Perales-Puchalt, 2019;Lee et al, 2019;Shaaban et al, 2019;Tu et al, 2020;Walker et al, 2019). Nonetheless, a recent shift has seen studies that examine the role of early-life risk factors to increment current knowledge of risk factors (Grainger et al, 2019;Launer, 2007;Whalley et al, 2006;Williamson and Leroi, 2019).…”

Section: Introductionmentioning

confidence: 99%

Childhood infectious diseases and old age cognitive functioning: a nationally representative sample of community-dwelling older adults

Rotstein

Levine

2020

Int. Psychogeriatr.

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Background: Cumulative evidence suggests that health-related risk factors during midlife and old-age are associated with cognitive impairment. However, studies are needed to clarify the association between early-life risk factors and impaired cognitive functioning to increment existing knowledge. Objective: To examine the association between childhood infectious diseases and late-life cognitive functioning in a nationally representative sample of older adults. Participants: Eligible respondents were 2994 community-dwelling individuals aged 65–85. Measurements: Cognitive functioning was assessed using the Mini-Mental State Examination (MMSE). Childhood infectious diseases (i.e. chicken pox, measles, and mumps) were self-reported. The study covariates were age, sex, highest educational level achieved, smoking status, body mass index, and depression. The primary statistical analysis examined the association between the number of childhood infectious diseases and total MMSE scores, accounting for all study covariates. Regression models of progressive complexity were examined for parsimony. The robustness of the primary results was tested in 17 sensitivity analyses. Results: The most parsimonious model was a linear adjusted model (Bayesian Information Criterion = 12646.09). Late-life cognitive functioning significantly improved as the number of childhood infectious diseases increased (β = 0.18; 95% CI = 0.11, 0.26; p < 0.001). This effect was not significantly attenuated in all sensitivity analyses. Conclusion: The current study results are consistent with prior ecological findings indicating that some childhood infectious diseases are associated with better cognitive functioning in old-age. This points to an early-life modifiable risk factor associated with older-life cognitive functioning. Our results may reflect selective mortality and/or beneficial effects via hormetic processes.

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Trajectories of cognitive function and their determinants in older people: 12 years of follow-up in the Chinese Longitudinal Healthy Longevity Survey

Cited by 34 publications

References 48 publications

Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults

Machine learning-based prediction of cognitive trajectories among middle-aged and elderly Chinese adults

Trajectories of Cognitive Function and Their Machine Learning-based Prediction Models in Older Adults: a 16 Years Follow-up in the Chinese Longitudinal Healthy Longevity Survey

Childhood infectious diseases and old age cognitive functioning: a nationally representative sample of community-dwelling older adults

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