In this study, we focused on the current status of cognitive and mental health among elderly individuals in the community, while also exploring potential influencing factors of cognitive function. According to our survey data, the AD8 scale score was (1.05 ± 1.71), which is higher than the score of (0.99 ± 1.68) reported by Jin Shan et al. [20] in Guangdong's Shenzhen city. This indicates that the cognitive level of elderly individuals in Guangxi community is lower than that of their counterparts in Shenzhen, which may be attributed to Guangxi's mountainous location and the lower economic and cultural levels of its elderly population compared to Shenzhen. In this study, 16.44% of the elderly population exhibited cognitive dysfunction, which is roughly consistent with the range of 9.9–35.2% reported internationally [21]. Furthermore, 23.95% of the elderly were diagnosed with borderline cognitive impairment. Although they have not been strictly diagnosed with cognitive dysfunction, these individuals face potential risks and may develop into cognitive dysfunction or even more severe cognitive disorders such as Alzheimer's disease in the future. This underscores the ubiquity and severity of cognitive health issues among the elderly population. Attention to elderly individuals with borderline cognitive impairment not only benefits their individual health and well-being but also contributes to the overall improvement of society's cognitive health level.
Depression and anxiety are among the top ten causes of global disability [22], and over 80% of individuals with mental disorders reside in low- and middle-income countries (LMICs) [23]. In this study, the PHQ9 scale score was (1.22 ± 2.30), with 7.15% of participants being diagnosed with depression. Similarly, the GAD7 scale score was (0.70 ± 1.89), with 4.51% exhibiting symptoms of anxiety. In most studies, the prevalence of depression among elderly patients is high, but there are significant variations in reported rates due to differences in methods and populations, ranging from 1–32% [24–26]. According to some research, the prevalence of depression among elderly individuals in Guangdong, China, is 2.79%, while the prevalence of anxiety is 1.39% [27]. In India, the crude prevalence of both depression and anxiety is 3.3% [28].
This study delved into the impacts of gender, age, category, years of education, marital status, PHQ9, and GAD7 on the cognitive function of community-based elderly individuals. The results indicate a positive correlation between age, PHQ9, and GAD7 scores with AD8 scores. This suggests that as individuals age, they experience increasing levels of emotional distress and anxiety, which correlate with a decline in cognitive function. Conversely, there is a negative correlation between years of education and AD8 scores, indicating that a higher level of education can help maintain cognitive function in older adults.
During the aging process, the brain inevitably undergoes various structural and functional changes [29]. Macroscopically, brain atrophy is a prominent feature of aging, and its occurrence rate increases with age. However, while brain atrophy is an inevitable consequence of aging, it is still possible to delay its progression and protect cognitive function through certain intervention measures. A higher level of education has a positive impact on the cognitive function of older adults. Education not only enhances an individual's knowledge base but also exercises advanced cognitive functions such as abstract thinking and logical reasoning. Through long-term learning and thinking, the brain of older adults can remain active, thus delaying the process of cognitive decline. Additionally, education can help older adults better cope with life challenges and stress, improving their psychological resilience and further protecting their cognitive function.
Studies have shown that both childhood education and lifelong learning are closely associated with a lower risk of dementia [30]. This may be because education promotes the growth and connectivity of neurons, enhancing the plasticity and adaptability of the brain. Therefore, for older adults, maintaining habits of continuous learning and thinking is an important pathway to improve their cognitive function and prevent dementia.
By constructing a structural equation model, this study delved into the complex relationships among age, depressive symptoms (PHQ9), anxiety symptoms (GAD7), and cognitive function (AD8). It further revealed the mediating role of mental health between age and cognitive function. According to the coefficients of the model paths, we found that age had a significant positive impact on PHQ9, PHQ9 on GAD7, GAD7 on AD8, and age on AD8, which was consistent with our expectations. These positive effects indicate that as individuals age, they are more likely to experience depressive and anxious emotional issues, and these emotional problems further affect their cognitive function.
It's noteworthy that the influence of age on GAD7 is negative, which seems to contradict common sense. However, this could be due to changes in the pressure sources and coping strategies faced by older adults as they age. With the increase in age, older adults may gradually adapt to various challenges in life, or the sources of anxiety they face may decrease due to a narrowing social circle. Nevertheless, this negative effect cannot fully explain the underlying mechanism, and further research is needed to explore it.
Through the application of the Bootstrap method to test mediation effects, we found that the total effect, direct effect, and indirect effects were all significant. This indicates that mental health plays a crucial mediating role between age and cognitive function. Specifically, PHQ9 and GAD7, as mediating variables, not only have significant individual impacts on AD8 but also form complex indirect effect paths through their mutual influence. Among them, the indirect effects of the two paths, age→PHQ9→AD8 and age→PHQ9→GAD7→AD8, are positive and significant, suggesting that age affects cognitive function in older adults through its influence on depressive symptoms, and anxiety symptoms amplify this process. However, the indirect effect of the age→GAD7→AD8 path is negative, possibly due to an "offsetting" effect between the negative influence of age on GAD7 and the positive influence of GAD7 on AD8. This again reminds us that the relationship between mental health and cognitive function in older adults is highly complex, requiring comprehensive consideration of multiple factors for a full understanding.
In summary, this study not only reveals the complex relationships among age, depression, anxiety, and cognitive function but also emphasizes the mediating role of mental health in these relationships. These results are significant for understanding the mechanisms underlying cognitive decline in older adults and for developing effective intervention measures. Future research can further explore other potential mediating variables and influencing factors to construct a more comprehensive model to guide health management and cognitive function improvement in older adults.