Abstract
Background
This study examines how the prevalence of depression and anxiety symptoms differ by socioeconomic status and explores their associations with diabetes in rural southwest China.
Methods
Data were collected from a cross-sectional health interview and examination survey of 5,005 adults aged ≥ 35 years in rural southwest China. Height, weight, waist circumference, and fasting blood glucose measurement were taken. Depression and anxiety symptoms were assessed using Zung’s Self-rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS), respectively. An individual socioeconomic position (SEP) index was constructed using principal component analysis.
Results
The overall prevalence of depression, anxiety, comorbid depression and anxiety symptoms, and diabetes was 5.4%, 12.8%, 4.0%, and 9.6%, respectively. Females had higher prevalence of depression (7.3% vs. 3.4%, P < 0.01), anxiety (17.4% vs. 8.1%, P < 0.01), and comorbid depression and anxiety symptoms (5.8% vs. 2.3%, P < 0.01) than males. Han ethnicity participants had a higher prevalence of depression, anxiety, and comorbid depression and anxiety symptoms than ethnic minority participants (P < 0.01). Individuals with a lower education level and lower SEP were more likely to experience depression, anxiety, and comorbid depression and anxiety symptoms (P < 0.01), while individuals with good access to medical services were more likely to exhibit depression symptoms and comorbid depression and anxiety symptoms (P < 0.05). Multivariable logistic regression analysis found that individuals with depression symptoms (OR = 1.78, 95% CI: 1.25 to 2.53), anxiety symptoms (OR = 1.66, 95% CI: 1.30 to 2.16), and comorbid depression and anxiety symptoms (OR = 1.61, 95% CI: 1.07 to 2.44) all had a greater probability of having diabetes (P < 0.01); depression symptoms had the strongest association with diabetes prevalence (P < 0.01).
Conclusions
There are significant socioeconomic differences in the prevalence of depression and anxiety in rural southwest China, and both depression and anxiety symptoms have strong associations with diabetes. Future diabetes prevention and management strategies should focus on individuals with depression, anxiety, and comorbid depression and anxiety symptoms.
Keywords: Anxiety symptoms, Depression symptoms, Diabetes, Socioeconomic status, China
Introduction
Mental health disorders, particularly depression and anxiety, are a major public health problem globally. According to the World Health Organization (WHO), about 1 in 8 people worldwide suffer from mental disorders, both in men and women, and anxiety and depression are the most common [1]. Moreover, the 2021 Global Burden of Disease (GBD) study found people across the world are increasingly suffering from depression and anxiety; currently, approximately 4.36% of the global population suffers from depression while 4.71% suffers from an anxiety disorder [2]. In China, the burden of mental health disorders is severe and growing [3]. Anxiety disorders are most prevalent (7.6%), followed by depression (6.8%) [4]. These conditions burden both individuals and the healthcare system.
More than half of China’s population lives in rural areas, and previous studies found mental health status of urban residents to be better than that of rural residents in China [3]. Psychological issues are especially prevalent in rural regions due to the intricate interplay of various social and environmental factors, alongside the scarcity, inequity, and inefficiency of medical resources [5, 6]. Moreover, mental health issues faced by rural residents are frequently disregarded, particularly in remote areas; a lack of health literacy, stigma, and discrimination often are barriers in recognizing symptoms and seeking appropriate care [1, 7]. These challenges further exacerbate psychological illnesses and social burdens and have contributed to the growing prevalence of depression and anxiety among adults in rural China.
Socioeconomic factors significantly impact mental health, including income, education, and access to healthcare services, in turn shaping mental illness [8–10]. Specifically, a socioeconomically disadvantaged status not only heightens the risk of mental health issues, but also affects disease management and recovery outcomes; a systematic review uncovered a link between a higher risk of common mental disorders and poverty, lower educational attainment, and income loss in low- and middle-income countries [9]. Furthermore, limited access to healthcare services can worsen these issues, potentially leading to poorer disease trajectories and outcomes [10]. These findings underscore the importance of identifying and addressing socioeconomic risk factors to enhance mental health outcomes. However, data on the association of depression and anxiety and socioeconomic factors in China, particularly in rural populations, is limited.
Additionally, the intersection of mental health disorders and chronic physical diseases is a field of growing interest. Diabetes, in particular, is recognized as a psychosomatic disease with multifactorial causes. Increasing evidence suggests that the comorbidity of diabetes and mental disorders, including depression and anxiety, is common [11, 12]. Some studies have demonstrated that psychological factors may play a role in the adverse onset and outcomes of diabetes [13, 14]. The combined effects of mental illness and diabetes may lead to increased costs of treating the disease, higher mortality rates in the population, and a significant burden of disease on both individuals and society [15–17]. The mechanisms behind these relationships are complex, some evidence suggests that biochemistry induced by psychiatric disorders, in particular over-activation of innate immunity leading to cytokine-mediated inflammatory responses and dysregulation of the hypothalamo-pituitary-adrenal axis may contribute to insulin resistance, increased risk of diabetes prevalence and death [16, 18–20]. Therefore, understanding the relationship between diabetes and mental health issues, along with identifying high-risk factors, is crucial for early prevention and management of both diabetes and its associated psychological problems. However, limited data exist on the relationship between depression and anxiety symptoms and the prevalence of diabetes among rural adults in China, particularly in remote areas. Compared to previous studies, our research comprehensively analyzed this relationship, providing regionally representative empirical evidence. It aimed to help policymakers better understand the needs of vulnerable groups and optimize mental health services and chronic disease management strategies.
Situated in the southwestern region of China, Yunnan Province is a high-altitude mountainous province with China’s largest number of ethnic minorities [21]. It remains relatively underdeveloped, particularly in remote rural areas. There is limited evidence on socioeconomic disparities in mental health among the general population in underdeveloped rural southwestern China. Individuals’ perceptions about mental health and diabetes may vary significantly across different cultural, ethnic, and social backgrounds. Moreover, the association between depression and anxiety symptoms and diabetes in this specific population has not been well-established.
Thus, the present study aimed to explore the socioeconomic disparities in depression and anxiety symptoms and their associations with diabetes in rural southwest China.
Methods
Study area and population
This study was conducted in three counties of Yunnan Province from 2023 to 2024 using a cross-sectional health interview and examination survey. The study employed a multistage stratified random sampling method in rural Yunnan to ensure representativeness. In the first stage, 129 Counties were categorized into three economic levels (high, medium, and low) based on per capita gross domestic product (GDP), and one county was randomly selected from each level, yielding three counties. In the second stage, two townships were randomly selected from each of these three categories, for a total of six townships. In the third stage, three villages were randomly selected from each township by using the probability proportional to size (PPS) method, totaling 18 villages. In the fourth stage, eligible participants, defined as residents aged 35 years or older who had lived locally for at least five years, were randomly chosen from village rosters.
The formula for a cross-sectional study was used to calculate the sample size for each selected village: 
. Where p is the prevalence of diabetes in Chinese population (12.4%) [22], 
is the margin of error (to estimate prevalence with a precision 
is equal to half of the prevalence of diabetes), and deff is the effect of design (deff=2).
Data collection and measurement
Data were collected through a health interview and examination survey [23] by professionally investigators mostly affiliated with Kunming Medical University. A workshop was conducted to train and assess investigators, covering questionnaire survey methods, technical essentials, anthropometric measurement techniques, and data quality control, ensuring that standardized data collection.
Each participant who gave informed consent was interviewed face-to-face using a pre-tested and structured questionnaire that covered general demographic characteristics (including gender, age, ethnicity, educational level, and annual household income), walking time to the nearest village hospital, and family history of diabetes. Depression and anxiety symptoms were evaluated using the Self-Rating Depression Scale (SDS) [24] and Self-Rating Anxiety Scale (SAS) [25], which have been shown to be reliable in previous Chinese studies. Each scale is comprised of 20 items rated on a 4-point scale, ranging from “1” (not at all) to “4” (very much so). The total raw score of 20 items is multiplied by 1.25 and then rounded to obtain the standard score. All symptoms were self-reported by the participants.
All participants also underwent an on-site physical examination. Fasting blood glucose (FBG), height, and weight were measured by standardized protocol, and recorded by physicians from the First Affiliated Hospital of Kunming Medical University. FBG levels were measured using a rapid glucose meter (ACCU-CHEK, Roche, Germany) by obtaining peripheral blood from the finger-stick of each participant in the morning, following at least 8 h of overnight fasting. Persons who reported not to be fasting were invited to receive an additional FBG teston another day. Height and weight were measured using standardized equipment and procedures as described in the World Health Organization (WHO) STEPS manual [26]. Body Mass Index (BMI) was calculated by dividing weight (kg) by height squared (m2). Quality controllers checked the collected data promptly after the field data collection, including both logical and completeness checks.
Definitions
According to norms specific to the Chinese population, the SDS standard score ≥ 53 was categorized as having depression symptoms, while the SAS standard score ≥ 50 was categorized as having anxiety symptoms. These thresholds have been validated and widely adopted in previous Chinese studies [27–29].
Diabetes was defined as FBG ≥ 7.0 mmol/l (≥ 126 mg/dl), self-reported previous diagnosis of diabetes by a healthcare professional, and/or reported use of antidiabetic medications during the two weeks prior to the study [30]. Overweight was defined as having a BMI between 24 kg/m2 and 28 kg/m2, while obesity was indicated by a BMI of 28 kg/m2 or higher, in accordance with WHO definitions for Asian adults [31].
Ethnicity was classified into two groups: Han majority and minority ethnic groups. The minority ethnic groups included populations whose religious, cultural, or linguistic backgrounds differed from the Han ethnic majority.
Education levels were divided into two levels: illiterate and primary school (grades 1–6) or higher. Illiteracy refers to individuals aged 15 years and older who cannot read or write a simple sentence. Annual household income was classified as low or high, with the median value as the cutoff point. Access to medical services was considered good if the walking time from home to the nearest village hospital was less than 30 min and poor if it was ≥ 30 min.
Ethical approval
The study was approved by the Ethics Committee of Kunming Medical University (Approval No. KMMU2023MEC108) before the commencement of the research. Written informed consent was obtained from all participants.
Statistical analysis
Descriptive analysis techniques, chi-square test, t-test, multivariate logistic regression, and principal component analysis (PCA) were utilized in this study. Categorical variables were presented as counts and percentages while continuous variables were expressed as mean ± standard deviation (SD). The chi-square test was used to compare categorical variables among different groups. The t-test was employed to compare continuous variables across different groups. Multivariate logistic regression analysis was conducted to examine the relationships between depression and anxiety symptoms, and diabetes. The associations were expressed as odds ratios (OR) with 95% confidence intervals (CI) calculated. All data analyses were performed using SPSS 24.0 software. Statistical significance was determined based on two-tailed p-values, with p-values < 0.05 considered statistically significant.
PCA was used to construct a composite index of individual socioeconomic position (SEP) on education, annual household income, and access to medical services. A Bartlett’s test of sphericity was used to check correlation adequacy (P < 0.0001). A Kaiser-Meyer-Olkin (KMO) statistic with P ≥ 0.7 measured sample adequacy. Principal components were extracted with Kaiser’s criterion of eigenvalues set at ≥ 1.
Results
In total, 5,200 individuals aged ≥ 35 were randomly invited to participate in the survey. Among them, 5,005 consented, resulting in an overall response rate of 96.25%.
The results of Bartlett’s test (P < 0.0001) and KMO Measure (0.71) indicated correlations between variables that were sufficiently large to perform the PCA. As the eigenvalue of the first component exceeded 1, explaining 52.1% of the total variance, only one component was retained to define the SEP index. The component score coefficient of education, annual household income, and access to medical services was 0.71, 0.59, and 0.53, respectively. The SEP index score was calculated based on the coefficients of the three variables and further categorized into two levels: low and high.
Table 1 shows participants’ demographic characteristics, mean value of anthropometric measurements, and SDS and SAS scores. Among the 5,005 study participants, 2,476 (49.5%) were male and 2,529 (50.5%) were female. There was no significant difference in the age distribution of males versus females (P > 0.05). Overall, 20.2% of the participants were of minority ethnicity. The adult illiteracy rate was 19.1%. Males had higher levels of education, annual household income, and SEP than females (P < 0.05). In contrast, females had a higher prevalence of obesity and higher scores of depression and anxiety symptoms than males (P < 0.01).
Table 1.
Demographic characteristics and mean value of anthropometric measurements and SDS and SAS scores of the study population
| Characteristic | Male (n = 2476) |
Female (n = 2529) |
All (n = 5005) |
|---|---|---|---|
| Age (%) | |||
| 35–44 years | 175 (7.1) | 212 (8.4) | 387 (7.7) |
| 45–54 years | 497 (20.1) | 520 (20.6) | 1017 (20.3) |
| 55–64 years | 681 (27.5) | 661 (26.1) | 1342 (26.8) |
| 65–74 years | 681 (27.5) | 666 (26.3) | 1347 (26.9) |
| ≥ 75 years | 442 (17.9) | 470 (18.6) | 912 (18.2) |
| Ethnicity (%) | |||
| Han | 2051 (82.8) | 1941 (76.7) | 3992 (79.8) |
| Minority | 425 (17.2) | 588 (23.3) | 1013 (20.2) |
| Education level (%) | |||
| Illiterate | 246 (9.9) | 709 (28.0)** | 955 (19.1) |
| Primary (grade 1–6) or higher | 2230 (90.1) | 1820 (72.0) | 4050 (80.9) |
| Annual household income (%) | |||
| Low | 1528 (61.7) | 1644 (65.0)* | 3172 (63.4) |
| High | 948 (38.3) | 885 (35.0) | 1833 (36.6) |
| Access to medical services (%) | |||
| Good | 834 (33.7) | 868 (34.3) | 1702 (34.0) |
| Poor | 1642 (66.3) | 1661 (65.7) | 3303 (66.0) |
| SEP (%) | |||
| Low | 1136 (45.9) | 1340 (54.1)** | 2554 (51.0) |
| High | 1418 (56.1) | 1111 (43.9) | 2451 (49.0) |
| Obesity (%) | 167 (6.7) | 227 (9.0)** | 394 (7.9) |
| Height (cm, mean ± SD) | 164 ± 6.7 | 153 ± 6.5** | 159 ± 8.6 |
| Weight (kg, mean ± SD) | 61.9 ± 10.6 | 53.9 ± 10.1** | 57.9 ± 11.1 |
| BMI (kg/m2, mean ± SD) | 22.8 ± 3.3 | 22.9 ± 3.7 | 722.9 ± 3.5 |
| Fasting blood glucose (mmol/l, mean ± SD) | 5.4 ± 1.5 | 5.5 ± 1.6 | 5.4 ± 1.5 |
| Family history of diabetes (%) | 138 (5.6) | 120 (4.7) | 258 (5.2) |
| SDS score (mean ± SD) | 35 ± 9 | 38 ± 9** | 37 ± 9 |
| SAS score (mean ± SD) | 37 ± 8 | 40 ± 9** | 38 ± 9 |
| All | 2476 (49.5) | 2529 (50.5) | 5005 (100.0) |
* p < 0.05, ** p < 0.01, comparison of gender distribution across groups with different characteristics
BMI = body mass index
SD = standard deviation
Table 2 indicates the prevalence of depression and anxiety symptoms by socioeconomic status among the study population. The prevalence of depression symptoms, anxiety symptoms, and comorbid depression and anxiety symptoms was 5.4% (3.4% for males and 7.3% for females, P < 0.01), 12.8% (8.1% for males and 17.4% for females, P < 0.01), and 4.0% (2.3% for males and 5.8% for females, P < 0.01), respectively. The prevalence of depression and anxiety symptoms increased with age (P < 0.01), and was higher in the Han majority compared to the ethnic minority group (P < 0.01). Individuals with lower education levels and lower SEP were more likely to experience depression, anxiety, and comorbid depression and anxiety symptoms (P < 0.01). Individuals with good access to medical services were more likely to exhibit depression symptoms and comorbid depression and anxiety symptoms (P < 0.05). There were no significant differences in the prevalence of depression and anxiety symptoms among individuals with different annual household incomes (P > 0.05).
Table 2.
Distribution of prevalence of depression and anxiety symptoms by socioeconomic status in rural Yunnan Province, China
| Variable | Depression symptoms n (%) |
Anxiety symptoms n (%) |
Comorbid depression and anxiety symptoms n (%) |
|---|---|---|---|
| Sex | |||
| Male | 85 (3.4) | 220 (8.1) | 56 (2.3) |
| Female | 185 (7.3)** | 439 (17.4)** | 146 (5.8)** |
| Age | |||
| 35–44 years | 9 (2.3) | 21 (5.4) | 6 (1.6) |
| 45–54 years | 33 (3.2) | 87 (8.6) | 22 (2.2) |
| 55–64 years | 61 (4.5) | 146 (10.9) | 43 (3.2) |
| 65–74 years | 84 (6.2) | 219 (16.3) | 66 (4.9) |
| ≥ 75 years | 83 (9.1)** | 166 (18.2)** | 65 (7.1)** |
| Ethnicity | |||
| Han | 251 (6.3)** | 549 (13.8)** | 186 (4.7)** |
| Minority | 19 (1.9) | 90 (8.9) | 16 (1.6) |
| Education level | |||
| Illiterate | 87 (9.1)** | 182 (19.1)** | 69 (7.2)** |
| Primary (grade 1–6) or higher | 183 (4.5) | 457 (11.3) | 133 (3.3) |
| Annual household income | |||
| Low | 183 (5.8) | 419 (13.2) | 136 (4.3) |
| High | 87 (4.7) | 220 (12.0) | 66 (3.6) |
| Access to medical services | |||
| Poor | 68 (4.0) | 222 (13.0) | 50 (2.9) |
| Good | 202 (6.1)* | 417 (12.6) | 152 (4.6)** |
| SEP | |||
| Low | 214 (6.2)** | 483 (14.1)** | 163 (4.7)** |
| High | 56 (3.6) | 156 (9.9) | 39 (2.5) |
| All | 270 (5.4) | 639 (12.8) | 202 (4.0) |
* p < 0.05, ** p < 0.01, comparison of prevalence of depression and anxiety symptoms across groups with different characteristics
Table 3 displays the prevalence of diabetes by sex, age, and depression and anxiety symptoms. The overall prevalence of diabetes in the surveyed population was 9.6% (9.5% for males and 9.8% for females). Prevalence of diabetes increased with age (P < 0.01). Individuals with depression, anxiety, and comorbid depression and anxiety symptoms had a higher prevalence of diabetes than their counterparts (P < 0.05).
Table 3.
Prevalence of diabetes by sex, age, and depression and anxiety symptoms in rural Yunnan Province, China
| Variable | Diabetes n (%) |
Non-diabetes n (%) |
|---|---|---|
| Sex | ||
| Male | 234 (9.5) | 2242 (90.5) |
| Female | 247 (9.8) | 2282 (90.2) |
| Age | ||
| 35–44 years | 17 (4.4) | 370 (95.6) |
| 45–54 years | 75 (7.4) | 942 (92.6) |
| 55–64 years | 124 (9.2) | 1218 (90.8) |
| 65–74 years | 173 (12.8) | 1174 (87.2) |
| ≥ 75 years | 92 (10.1) ** | 820 (89.9) |
| Depression symptoms | ||
| Yes | 43 (15.9) ** | 227 (84.1) |
| No | 438 (9.3) | 4297 (90.7) |
| Anxiety symptoms | ||
| Yes | 90 (14.1) ** | 549 (85.9) |
| No | 391 (9.0) | 3975 (91.0) |
| Comorbid depression and anxiety symptoms | ||
| Yes | 30 (14.9)* | 172 (85.1) |
| No | 451 (9.4) | 4352 (90.6) |
| All | 481 (9.6) | 4524 (90.4) |
* p < 0.05, ** p < 0.01, comparison of prevalence of diabetes across groups with different characteristics
Table 4 presents the results of multi-variable logistic regression for the prevalence of diabetes. After adjusting for age, sex, ethnicity, education level, annual household income level, access to medical services, family history of diabetes, and obesity, individuals with depression symptoms (OR = 1.78, 95% CI: 1.25–2.53), individuals with anxiety symptoms (OR = 1.66, 95% CI: 1.30–2.16), and individuals with comorbid depression and anxiety symptoms (OR = 1.61, 95% CI: 1.07–2.44) all had a greater probability of having diabetes (P < 0.01). Additionally, depression symptoms had the strongest association with prevalence of diabetes.
Table 4.
Odds ratios (OR) and 95% confidence intervals (CI) for multi-variable logistic regression for prevalence of diabetes
| Variable | Diabetes (reference: no) | |
|---|---|---|
| Adjusted odds ratio† | 95% CI | |
| Depression symptoms (reference: No) | 1.78** | 1.25 ~ 2.53 |
| Anxiety symptoms (reference: No) | 1.66** | 1.30 ~ 2.16 |
| Comorbid depression and anxiety symptoms (reference: No) | 1.61* | 1.07 ~ 2.44 |
* p < 0.05, ** p < 0.01, † adjusted for age, sex, ethnicity, education level, annual household income level, access to medical services, family history of diabetes, and obesity
Discussion
The findings uncovered that both prevalence of depression and anxiety symptoms varied by socioeconomic factors in rural southwest China. They also demonstrated that depression, anxiety, and comorbid depression and anxiety symptoms were independently correlated with a higher risk of having diabetes.
We additionally found that the prevalence of anxiety symptoms (12.8%) was more widespread than that of depression symptoms (5.4%) in rural residents of southwest China. This is consistent with previous findings on the epidemiological characteristics of mental disorders in China, as well as studies from both developed and developing countries [4, 32]. Furthermore, the prevalence of depression, anxiety, and comorbid depression and anxiety symptoms among the studied population was higher in women than in men, and increased with age. These gender and age findings are in line with previous research conducted in both high-income and low- and middle-income countries [4, 8, 33, 34]. They may result from the fact that women and the elderly may be more likely to be situated in vulnerable and underdeveloped rural areas, thereby facing more sources of stress, including an increased likelihood to play multiple family roles, face personal health issues, have financial difficulties, and have a poor quality of life [33, 35]. The results highlight an urgent need for improving community-based depression and anxiety prevention strategies focused particularly on women and older adults.
Similar to findings from other studies [8, 34, 36], we also found differences in mental health status among different ethnic groups. Ethnic minorities reported lower rates of depression, anxiety, and comorbidity of both symptoms compared to the Han population. This result could be due to the differing lifestyles, traditional beliefs, and cultural values of ethnic minorities in southwestern China [34]. Further research is needed to explore these differences in greater detail.
Consistent with previous studies [9, 37], the findings of this research indicated that individuals with lower education levels were at greater risk of experiencing depression and anxiety symptoms than their counterparts. Education allows for the availability of increased knowledge and skills, and people with higher education levels tend to be more financially successful. In contrast, many with lower education levels may be burdened with multiple predicaments, such as stressful economic pressures, a lack of health awareness and knowledge, and poor working and living environments, all of which are likely to contribute to the development of mental illness [38]. Furthermore, individuals with better access to medical services reported a higher prevalence of depression and comorbid depression and anxiety symptoms. This result may stem from the fact that higher accessibility of healthcare services allows this population to receive more mental health education, thereby enhancing their awareness and understanding of mental health issues. In turn, they may experience less stigma and shame associated with mental health problems and thus be more inclined to express their psychological issues openly during surveys [10, 39]. These factors collectively may increase the likelihood of detecting depression and anxiety symptoms in individuals with better access to medical services.
A previous systematic review found varying patterns in the relationship between common mental disorders and income across different social contexts, where higher income levels in low- and middle-income countries are not always linked to a lower prevalence of mental disorders and may even be associated with higher rates [9]. Our study did not find statistically significant differences in the prevalence of depression and anxiety symptoms across different income groups, which may be related to the specificity of the investigated population and social context of rural Southwest China, where higher education does not necessarily translate to better income opportunities due to regional economic constraints, potentially creating distinct stressors that impact mental health. Larger-scale surveys or cohort studies would be necessary to ascertain the specific reasons behind this discrepancy.
The results of the study additionally revealed that SEP plays an important role in influencing the prevalence of depression and anxiety symptoms in rural areas, with depression and anxiety symptoms more prevalent among those with low SEP. This association aligns with findings from studies conducted in other areas of China, as well as studies from other countries [8, 40, 41]. This study thus suggests that there is a need to develop strategies to address socioeconomic inequalities in mental health in rural southwest China and enhance comprehensive mental healthcare services for those with low SEP.
In this study, the overall prevalence rate of diabetes (9.6%), lower than the national average of Chinese adults (12.4%) [22] as well as lower than rates in high-income areas [42], but higher than levels in low- and middle-income areas [43]. In line with previous research [22], age was identified as a significant risk factor for diabetes, highlighting the importance of early screening, diagnosis, and standardized management of diabetes in high-risk elderly rural populations.
Our data also showed that the prevalence of diabetes was higher among groups with depression and anxiety symptoms (including comorbidity of both symptoms). The association between mental health symptoms and diabetes remained significant even after adjusting for other potential confounding factors. Numerous previous studies [12, 16] have provided evidence supporting our findings. In this study, individuals with depression symptoms had higher odds of diabetes (OR = 1.78, 95% CI: 1.25–2.53), exceeding the estimate reported in a prior systematic review (OR = 1.38, 95% CI: 1.23–1.55) [44]. Similarly, individuals with anxiety symptoms showed increased odds of diabetes (OR = 1.66, 95% CI: 1.30–2.16), higher than findings from a previous systematic review (OR = 1.19, 95% CI: 1.13–1.26) [15]. Those with comorbid depression and anxiety also demonstrated elevated odds of diabetes (OR = 1.61, 95% CI: 1.07–2.44), slightly lower than the results from a prospective study in the Netherlands (OR = 2.05, 95% CI: 1.40–3.00) [45]. Adverse health behaviors associated with depression or anxiety symptoms (for example, sleep disturbances, inactive lifestyles, and poor dietary habits), as well as biological mechanisms leading to hormonal imbalances and decreased immunity [16, 19] may increase the risk of diabetes. Moreover, the relationship between diabetes and mental health is likely bidirectional. Previous studies have shown that mental health disorders, such as depression and anxiety, may increase the risk of diabetes, while the stress of managing diabetes, poor glycemic control, and fear of complications can heighten the risk of mental health issues [12, 15, 17]. This interplay may intensify disease burden and mortality risk, potentially creating a vicious cycle if unaddressed. In this study, we did not conduct an in-depth analysis of the impact of diabetes on mental health, and future research is needed to further explore and analyze this relationship.
Additionally, we found that depression symptoms had the highest association with the risk of diabetes. One possible explanation is that the combined effects of depression-related physiological inflammation (such as increased cortisol), antidepressant-induced weight gain and insulin resistance, and sedentary lifestyles increase the risk of diabetes [19, 20], while patients with both anxiety and depression symptoms exhibit more complex behavioral and physiological responses that may make their association with diabetes less significant than that of depression alone. Our analysis did not uncover the underlying factors mediating the complex links between different psychiatric symptoms. The specific underlying mechanisms of those associations are not yet fully understood, further research is needed.
In short, although this study does not establish a causal relationship, the findings indicate a significant association between mental health and diabetes. Addressing both psychological and physical health could contribute to the comprehensive prevention and management of diabetes. It is recommended that healthcare providers in rural southwest China strengthen mental health services and consider incorporating mental health monitoring and management into routine diabetes care.
The findings are limited in several ways. First, this study utilized a cross-sectional design, which limited the ability to infer causality. Second, diagnoses of diabetes were based on FBG, which may underestimate diabetes prevalence. Third, although self-reported depression and anxiety symptoms were measured with a credible, the present study lacked an objective evaluation of depression and anxiety symptoms, which may affect the accuracy of the symptoms recorded. Fourth, we did not measure a wider range of factors such as dietary patterns, substance use and other chronic conditions that could potentially contribute to diabetes prevalence, further research is needed to examine the association between mental health and diabetes. Finally, the present findings were based on a random sampling of three counties in the Yunnan region, limiting their generalizability.
Conclusion
In conclusion, there are significant socioeconomic differences in the prevalence of depression and anxiety symptoms in rural southwest China, and both depression and anxiety symptoms have strong impacts on diabetes. Our findings highlight that future prevention and control strategies of depression and anxiety symptoms must be tailored to address socioeconomic factors, and future diabetes interventions should account for depression, anxiety, and comorbid depression and anxiety symptoms.
Acknowledgements
None.
Author contributions
DLL carried out the study and drafted the manuscript. LC and GYW conceptualized the research idea and revised the manuscript. ZZY, LL, GHL, XML, and CYR collected the data. ARG provided comments on the paper during the writing process. All authors have read and approved the manuscript.
Funding
The data collection and analysis of this study were supported by grants from the Major Union Specific Project Foundation of Yunnan Provincial Science and Technology Department and Kunming Medical University (202401AY070001-027), the Program for Innovative Research Team of Yunnan Province (202005AE160002), and the First-Class Discipline Team of Kunming Medical University (2024XKTDTS16). The funders had no role in the study design, decision to publish, or preparation of the manuscript.
Data availability
The datasets used and/or analyzed in this study are available from the corresponding author upon reasonable request.
Declarations
Ethical approval and consent to participate
This study was approved by the Ethics Committee of Kunming Medical University prior to the commencement of research. Written informed consent was obtained from all individuals participating in the study, and the Ethics Committee of Kunming Medical University approved this consent procedure. This study was performed in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed in this study are available from the corresponding author upon reasonable request.