In the present study, we described the cognitive trajectories of 5-year follow-up in a nationally representative sample of community-dwelling middle-aged and older people in China and explored its potential determinants. 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.
Compared with “persistently-high” class, individuals with low baseline MMSE scores, memory-related disorders, younger, high education, or stroke-free were at more risk of being classified as low-increase class. While those with low baseline MMSE score, high CESD-10 score, memory-related disorders, the habit of smoking, low waist circumference, or illiteracy were more likely to have a trajectory of medium-decrease . Obviously, memory-related disorders can result in low MMSE score, and the baseline MMSE score determined the cognitive trajectory to a large extent. But compared to “persistently-high” class, why people who are younger, with high education, or in stroke-free probably developed to low-increase class? It seemed out of line, we gave the following possible explanations. Younger people may suffer acute disease that resulted in low cognitive function at baseline, but after rehabilitation treatment, their cognitive function will increase in a certain. In general, people with high education have abundant knowledge, in addition, they have more ways to reinforce cognitive recovery training. Stroke is associated with an increased risk of dementia , deteriorate patients’ cognitive function, unluckily, the cognitive damage due to dementia is hard to reversible, so people in stroke-free have more potential in cognitive resilience. According to the above findings, we put forward some proposals to improve cognition, delay cognitive decline and promote healthy aging, such as setting up universities for the elderly to promote education, encouraging the elderly to strengthen exercise and social interaction . Besides, balanced diet and nutrition, absence of smoking, good quality of sleep, reasonable weight control also mean a lot for preventing cognitive decline.
We found that ML could well distinguish cognitive trajectories, especially for MD vs. LI class and PH vs. LI class. While for MD vs. PH, ML performed less well. It implied that the existing predictors might not be suitable for prediction. We also found that the traditional logistic regression was almost superior to the ML methods in the present study, and the same results were also found in previous suicide studies .
The importance of the variables varied among different subgroups, but the five factors from feature selection were important for the prediction of the cognitive trajectory, which were the age, initial MMSE score , CESD-10 score, waist circumference, and sleep time, suggesting that these factors might be important for the prevention of cognitive decline. Previous studies have identified that cognitive function deteriorates naturally with age . Depressive symptoms were associated with cognitive decline , so CESD-10 score can be viewed as a predictor. High waist circumference suggested abdominal obesity, nowadays, urban residents often replace walking with transportation and thereby have insufficient exercise, furthermore, the habit of drinking alcohol and unhealthy diet, and so on, which are not only associated with abdominal obesity but also have an impact on cognition [30–31]. Previous study has showed that sleep quality was negatively correlated with cognitive impairment [32–33].
This study has the following advantages: Firstly, the population was selected from a large community-based cohort study with good representation, and the predictive data was easy to obtain, the cost is low, and there is almost no risk to the population. Secondly, the mixed growth model not only takes the population heterogeneity into account, but also the differences among individuals in the population, which is helpful to dynamically understand the changing trend of cognition in middle-aged and elderly people. Finally, machine learning methods were used to predict the future cognitive trend of the population, which was helpful for the early identification of high-risk groups as well as timely personalized intervention.