Background: Cognitive impairment, a significant problem in older adults, may be associated with diet. This study aims to examine the association between the dietary diversity score (DDS), dietary pattern (DP), and cognitive impairment in elderly Chinese. This research further explored the role of psychological balance (PB) as a mediator in the relationship between diet and cognitive impairment. Methods: A total of 14,318 older adults from the Chinese Longitudinal Healthy Longevity Study (CLHLS) in 2018 were included. Latent class analysis (LCA) was used to identify patterns in seven food varieties. Binary logistic regression models were used to determine factors associated with the DDS, DP, and cognitive impairment. The multiple mediation effect model was evaluated using model 6 in the PROCESS version 3.5 program. Results: Among the participants, 4294 (29.99%) developed cognitive impairment. Compared to people in food variety group two or lower, people with a high dietary diversity score (DDS) had lower odds of cognitive impairment. Compared to DP1, DP2 (OR = 1.24, 95%CI = 1.09 to 1.40) was associated with a higher risk of cognitive impairment, and DP4 (OR = 0.79, 95%CI = 0.69 to 0.89) was associated with a lower risk of cognitive impairment. PB mediated the relationship between DDS, DP, and cognitive impairment, with a mediating effect of 27.24% and 41.00%. Conclusions: A DP that is rich in fruits, vegetables, red meat, fish, eggs, beans, nuts, and milk was related to a lower risk of cognitive impairment. PB has an indirect impact on cognitive impairment. Our findings underscore the importance of promoting a diverse diet, which may contribute to a lower risk of cognitive impairment in older adults. The PB of the elderly should also be taken into consideration.

There is limited evidence regarding the factors correlated with dietary diversity (DD) and dietary pattern (DP) in rural residents of China. This study aims to identify the DD and DP of rural residents and their association with socio-demographic factors. A cross-sectional survey was conducted in Pingnan, China. The Food Frequency Questionnaire (FFQ) was applied to evaluate dietary intake. Latent class analysis (LCA) was used to identify patterns of six food varieties, including vegetables–fruits, red meat, aquatic products, eggs, milk, and beans–nuts. Generalized linear models and multiple logistic regression models were used to determine factors associated with the DD and DP. Three DPs were detected by LCA, namely “healthy” DP (47.94%), “traditional” DP (33.94%), and “meat/animal protein” DP (18.11%). Females exhibited lower DD (β = −0.23, p = 0.003) and were more likely to adhere to “traditional” DP (OR = 1.46, p = 0.039) and “meat/animal protein” DP (OR = 2.02, p < 0.001). Higher educational levels and annual household income (AHI) were positively associated with higher DD (p < 0.05) and less likely to have “traditional” DP and “meat/animal protein” DP (p < 0.05). Non-obese people exhibited higher DD (β = 0.15, p = 0.020) and were less likely to have “meat/animal protein” DP (OR = 0.59, p = 0.001). Our study reveals that females, those with lower educational levels and AHI, and obese people are more likely to have a lower DD and are more likely to adhere to “traditional” DP and “meat/animal protein” DP. The local, regional, and even national performance of specific diet-related health promotion measures and interventions must target these vulnerable populations to improve a healthier DD and DP.

Mental health issues pose a significant public health challenge. Anxiety and depression are among the most prevalent mental health problems. However, the a

Introduction This study examined the association between eating behavior and anxiety, depression, and comorbidity among rural residents. Methods This cross-sectional study used data from the Adult Chronic Disease and Nutrition Surveillance program in Pingnan. Healthy diet scores and dietary patterns were used to assess eating behavior. Two-Step Cluster Analysis (TCA) was employed to determine the dietary patterns. The Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9) were applied to estimate anxiety and depression symptoms, respectively. Binary logistic regression models were conducted to explore the effects of healthy diet scores and dietary patterns on anxiety, depression, and comorbidity. Results Binary logistic regression results showed that rural residents with a healthy diet score of 0 and 1 were associated with a higher detection rate for anxiety (OR = 2.53, 95% CI = 1.29–4.96; OR = 2.31, 95% CI = 1.23–4.36, respectively), depression (OR = 2.65, 95% CI = 1.24–5.66; OR = 2.47, 95% CI = 1.20–5.05, respectively), and comorbidity (OR = 3.15, 95% CI = 1.27–7.84; OR = 2.42, 95% CI = 1.01–5.78, respectively). Those who had “moderately healthy pattern” and “unhealthy pattern” were associated with a higher detection rate of anxiety (OR = 1.67, 95% CI = 1.20–2.34; OR = 1.69, 95% CI = 1.14–2.50, respectively), depression (OR = 1.67, 95% CI = 1.17–2.39; OR = 1.61, 95% CI = 1.05–2.45, respectively), and comorbidity (OR = 1.88, 95% CI = 1.20–2.95; OR = 2.15, 95% CI = 1.29–3.57, respectively). Conclusions Unhealthy eating behavior may be associated with a higher detection rate of anxiety, depression, and comorbidity in rural residents. It is critical to implement dietary intervention programs for rural residents to develop healthy eating behavior attributed to reductions in anxiety and depression.