Using data from the Charls Survey's fourth and fifth waves, this study analyzed demographic variables, health status, and chronic medical history of 1,921 middle-aged and elderly people. A predictive model of depression risk in older adults with SCD has been constructed. Figure 8 illustrates the overall conceptual framework of this study, summarizing the accuracy of the predictive model, presenting the findings, and highlighting the potential clinical applications of the model.
A conceptual framework for discussion in this study.
Of the three machine learning models evaluated, both Xgboost and RF showed clear yet complementary benefits in predicting depression risk in older adults with SCD. In particular, education levels emerged as the top-rank predictor for SHAP analysis of both models.
These findings suggest that the level of education in patients may also play an important role in affecting individuals' depression rates with SCD. This finding is similar to the results of a study conducted in Japan.19Patients with low education levels may lack sufficient cognitive reserve to address the challenges of cognitive decline, increasing the risk of depression. Furthermore, people with low education levels may exacerbate their depressive symptoms due to limited social networks and lack of health-related knowledge.
Furthermore, other studies have shown that education levels can significantly predict cognitive ability levels.20In theory, enhancing patient educational achievement from an uneducated background could help avoid cognitive degradation. Therefore, interventions that promote education in older adults may contribute to improving cognitive functioning in these patients. In conclusion, relevant institutions should emphasize later education for older people. It is important to provide professional psychological care and guidance through social channels for individuals with low education and income levels. Social support through media campaigns, financial support, and other means can help reduce the risk of depression in people in need.
SHAP analysis revealed that arthritis is ranked as the second most important predictor in the boosted XGBoost model and third in the RF model, suggesting that arthritis is also a risk factor for DS in individuals with SCD. Chronic pain can contribute to the risk of depression through inflammatory mechanisms.
As a chronic condition, arthritis causes persistent pain and limited activity, limiting daily social interactions and involvement over time. This leads to negative emotions and loneliness, and ultimately manifests as depressive symptoms. A study by Su et al, which used machine learning to predict the risk of depression in elderly patients over two years, found that arthritis, along with other factors, plays an important role in the development of depression. Chronic disease patients are prone to depression21,22which means that the presence of arthritis or other such conditions can exacerbate mental health problems in patients with existing depressive symptoms. Therefore, greater caution should be given to screening and treating depression in individuals at risk of chronic disease. Collaboration between government agencies, healthcare organizations, and local communities is important for patient rehabilitation, including providing effective disease management strategies, financial support and psychological support. In conclusion, close associations with arthritis, depression, and other chronic diseases require comprehensive disease management strategies. Our goal is to reduce the risk of depression in arthritis patients and improve the overall quality of life through a broad approach that integrates physiological, psychological and social strategies.
Three other important predictors were digestive health, place of residence, and duration of sleep. There is a significant correlation between gastrointestinal abnormalities and risk of DS in patients with SCD. One potential explanation is that individuals with digestive problems often experience long-term discomfort, require long-term medication, leading to poor quality of life and negative effects on mental health. A study conducted in the US found that individuals reporting gastrointestinal, respiratory, and cardiovascular problems were more likely to exhibit depressive symptoms. This suggests that mental health problems caused by these three chronic diseases may outweigh those associated with other chronic diseasestwenty three. Furthermore, changes in gastrointestinal metabolites under pathological conditions may affect brain activity through the gut brain axistwenty four. Maintaining a healthy digestive system helps to prevent neuroinflammation, thereby protecting against cognitive decline and is also an important factor in slowing the onset and progression of depression.
Therefore, the following strategies are essential to reduce DS in patients with SCD: timely treatment and management of gastrointestinal diseases. Additionally, healthcare professionals should provide comprehensive care plans, including psychological support, lifestyle guidance, and other means to help patients manage mental stress associated with chronic illness. It is important to emphasize the link between gut and brain health to reduce the likelihood of depression and neuroinflammation in SCD patients.
The results of this study show that the incidence of DS is high among rural patients, which is consistent with findings from similar studies.twenty five. This could be due to limited rural healthcare resources, inadequate social support, and lower standard of living. Furthermore, the social and cultural environment of rural areas can affect patients' understanding and perceptions of illness, which can exacerbate mental health issues. Therefore, while assessing individual patient factors, relevant agencies should focus on strengthening rural public services and healthcare systems to reduce the likelihood of depression in these populations.
Sleep time plays an important role in the development of DS in SCD patients. Research has shown that both reduced sleep time and long rest periods can lead to the development of depressive symptoms26,27. Longitudinal studies that lasted more than two years in older people in community settings found that sleep disorders are an important contributor to persistent depression28. This may be due to the close relationship between sleep and mood regulation, and poor quality sleep directly affects brain activity, impairing stress adaptation and increasing the risk of depression. In fact, related studies suggest that long-term instability in sleep duration can impair cognitive function in older people, leading to cognitive decline.29.
This highlights the strong association between poor sleep habits and cognitive impairment in older people30sleep disorders can unconsciously contribute to cognitive decline, and often lead to DS. Neurological and clinical studies have shown that lack of sleep disrupts the continuous flow of cerebrospinal and interstitial fluid, leading to degradation of brain function and permanent cognitive impairment31,32. Sleep deprivation can also increase tau protein synthesis, reduce brain-derived neurotrophic factors, and stimulate the formation of new neurons and blood vessels33. Chronic sleep deprivation can worsen synaptic plasticity in the hippocampus, leading to cognitive decline34. Furthermore, disruption of sleep patterns may indicate daytime brain fatigue, which has a negative effect on multiple cognitive areas. Therefore, sleep disorders in SCD patients can exacerbate cognitive impairment and mental distress, leading to the development of depressive symptoms.
There is a significant increase in interest in adopting virtual reality (VR) when managing the condition of older patients. People with disabilities can discover beneficial physical efforts supported by virtual reality (VR). This action helps reduce malignant emotional responses. Encourage patients to both loneliness, strengthen their mental health, and physical and mental recovery.
In summary, while Random Forest (RF) models may be suitable for clinical screening applications requiring high sensitivity, the boosted XgBoost models offer excellent stability considering comprehensive performance metrics. Our findings underscore the importance of conducting early screening of high-risk populations in clinical and community health settings. In particular, it emphasizes targeting individuals who target clinical and community health environments through targeted health education programs. Development of web-based risk assessment tools by deploying the optimal model within a theoretical framework can drive widespread implementation. These digital solutions allow healthcare providers to conduct efficient depression risk assessments, design personalized intervention strategies based on individualized risk profiles, and ultimately drive precision medicine approaches for the prevention and management of depression.
limit
This study has several limitations. First, the Charls data used in this study are endemic to the elderly population in China. This means that the findings may not be directly applicable to seniors in other countries or regions. Furthermore, the effectiveness of machine learning models is essentially linked to data quality and related factors. Although machine learning techniques were employed for function selection in this study, some potentially important variables may not be fully considered. These factors can affect the reliability of the model's predictions. Future research could improve model predictability by incorporating more potential influencing factors, utilizing advanced machine learning techniques, and combining data from different countries.
