Relationship between musculoskeletal pain and depressive symptoms over 45 years people: the evidence from CHARLS

DOI:https://doi-xx.org/1050/17690660433339

Yanhao Ma¹, Chao Wang¹,Yanxing Li ¹,Guofeng Cui¹ ,Yingjie Liu¹,Li Zhang³,Wei Wang^1、2*

1Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang  471000,Henan, China

2Department of Comprehensive Orthopedics, Orthopedic Center ,the Second Affiliated Hospital of  Xi’ an Jiaotong University, Xi’ an  710004,Shaanxi,China

3Department of Physiology and Pathophysiology, School of Medicine, Xi’an Jiaotong University, Xi’an 710061,Shaanxi, China

* Corresponding author: Wei Wang. dr.wangwei@xjtu.edu.cn

Abstract

Background:  Depression is a prevalent societal issue that incurs significant annual social costs. Chronic pain, a known contributor to the development of depression, encompasses conditions like musculoskeletal pain. Despite this, research on the correlation between depression and musculoskeletal pain is sparse.

Methods:  The data for this study was derived from a representative samples the Chinese population from the China Health and Retirement Longitudinal Study (CHARLS). We collected data on sociodemographic factors, health-related factors, four satisfaction scales, depression scales and musculoskeletal pain-related information. Univariate and multivariate logistic analyses were employed to evaluate the relationship between musculoskeletal pain and depression.

Results:  10,278 individuals were experiencing musculoskeletal pain, and 3,847 also suffered from depression. A correlation was found between experiencing pain in multiple body parts and an increased likelihood of developing depression. Number of pains over 5 are linked to sleep difficulties, drink and chronic conditions . Depression is significantly different with rural , educational attainment and female.

Conclusion:  The incidence of depression in the Chinese population is correlated with musculoskeletal pain, demographic traits, and comorbidvariables.

Keywords:  Musculoskeletal pain, depressive symptoms, over 45 years  people, CHARLS

Introduction

Chronic pain is widespread in global cultures, and it is estimated that up to 70% of older persons are affected[1]. It is a huge and constantly increasing economic and medical burden. Musculoskeletal pain can lead to limited physical function and, in extreme cases, loss of ability [2] . A study conducted in Japan involving 12,883 participants showed a prevalence of persistent musculoskeletal pain of up to 39%[3]. Musculoskeletal pain, as defined in the 10th edition of the International Classification of Diseases (ICD-10), specifically refers to persistent pain resulting from musculoskeletal structures such as bones and joints, taking into account the associated pathology and tissue damage[4]. According to the ICD-10, musculoskeletalpain is classified into primary and secondary levels, including biological, psychological, and social factors. Primary musculoskeletalpain is commonand requires long-term treatment[2, 5]. The occurrence of musculoskeletal pain may be influenced by various factors, including contextual factors such asthe presence of comorbiditiesand the quality of care[6]. Chronic pain is the primary symptom of musculoskeletal problems, usually occurring in various areas of the body. Previous studies have shown that the incidenceof chronic pain in the elderly may be as high as 40% or even higher[3, 6-8]. Compared to pain at a single site, pain at multiple sites is linked to more severe health consequences, including worse sleep quality and heightened depressive symptoms[9]. The management and perception of depression have a significant impact on the sensation of pain in individuals with depression. Similarly, pain can also have an impact on the treatment and prospects of patients suffering from depression[10]. More than half of patients with long-lasting musculoskeletal pain also exhibit symptoms associated with depression[7, 11]. Therefore, it is necessary to comprehensively understanding of the correlation between musculoskeletal discomfort and depressive symptoms.

Recent studies have investigated the correlation between pain and depression symptoms. Some of these reports have simply presented a single-factor effect or variations in certain patients[12, 13], while others have investigated the correlation between chronic pain and depression[11, 14]. Most research papers mainly focus on chronic pain or fibromyalgia in adolescents[12, 15, 16]. However, musculoskeletal pain is a persistent pain commonly present in middle-aged and elderly individuals. Surprisingly, there is a lack of studies on the specific elements that contribute to the relationship between musculoskeletal pain and depression in large Chinese populations. This study used the China Health and Retirement Longitudinal Study (CHARLS) to investigate (1) the correlation between musculoskeletal pain and depression in a representative sample of the Chinese population and (2) whether the number of pain sites influences depression.

MATERIALS AND METHODS

Participants

  This study examined data from the fourth wave of the CHARLS, which was conducted in 2018 totaling 19,816 participants in the study. The database was approved by the ethical review committee of Peking University. It includes data from 450 village-level units in 150 counties (districts) across 28 provinces (autonomous regions and municipalities directly under the central government) in China[17]. The database was created using a multistage stratified random sample, with the aim of studying the problems faced by residents aged 45 and above in China and promoting related research[18]. All participants have signed informed consent forms. The criteria for inclusion in our study were participants who were at least 45 years old and had available data on musculoskeletal pain. The exclusion criteria were as follows: (1) under the age of 45 years; (2) incomplete demographic information; (3) incomplete depression scale information, with at least half of the responses being 8 (unknown) or 9 (rejected); and (4) missing data on musculoskeletal pain or the absence of musculoskeletal pain. In the end, a total of 10278 met the specified criteria and were selected for this study.

Assessment

Assessment of pain

CHARLS evaluated 15 frequent pain sites, which included the head, shoulders, arms, wrists, fingers, chest, stomach, back, waist, buttocks, legs, knees, ankles, toes, and neck[19]. This study overlooked pain in only one part of the head, chest, and stomach, particularly as they are associated with visceral disease[20], these pain sites were noted in their self-reports.

Assessment of depressive symptoms

The ICD-10, a shortened form of the Center for Epidemiological Studies Depression Scale, was used to assess the presence of depression symptoms in the CHARLS. The scoring system assigns a range of 0-3 points to each item, with a total score between 0 to 30. Based on past literature, comorbid depressive symptoms are defined as appearing when the score is 12 or above[21, 22].

Covariates

All variables were extracted from the CHARLS database, which counted them using a questionnaire. The manifestation of depressive symptoms in patients experiencing pain is determined by the complex interaction of various factors. This study considered many sociodemographic characteristics, including gender, age, current residency status (urban, rural), faith, race, marital status(separated, divorced, widowed, unmarried, married, or cohabiting), health insurance, and education level (lack of formal education, middle school or junior high school education high or vocational education, college or higher education). We categorized the over 45 people into three groups (45–59, 60–74, ≥ 75), while race was categorized into Han Chinese as well as other ethnic minorities according to the Charls database. Studies indicate that health-related factors are highly indicative of depression symptoms[23, 24]. The variables utilized for evaluating health status included sleep, drink, presence of multisite pain and chronic illnesses. Sleep is classified into two categories: napping and nighttime sleep. Based on previous research, nighttime sleep may be divided into four groups[22, 25]: normal sleep (7-9 hours per night), extremely short sleep (less than 5 hours per night), short sleep (5-7 hours per night), and excessive sleep (9 or more hours per night). Meanwhile, we gathered data on life satisfaction, health satisfaction, marriage content, and child satisfaction to evaluate their impact on depression. For a satisfactory response, the answer should be 1 (completely satisfied), 2 (very satisfied), or 3 (somewhat satisfied). Conversely, a response of 4 (not very satisfied) or 6 (not at all satisfied) was considered unsatisfactory.

Statistical analysis

Descriptive statistics are used to examine the distribution of the data by representing the categorical variables as relative percentiles. The chi-square test and logistic regression were used to evaluate the statistical significance of the differences between the subgroups with and without depressive symptoms. The adjusted odds ratio (OR) and its accompanying 95% confidence intervals (CIs) were provided. Use StataMP17.0 for data extraction and cleansing, and use IBM SPSS Statistics (version 26.0) for statistical analysis. A P value <0.05 was considered to indicate statistical significance.

Results

Demographic data

Figure 1 shows 10,278 respondents with pertinent data in this survey. The mean age was 61.9 years old, with the majority being females (59.39%), of which approximately half were illiterate or had not attained primary education. There are 97.32% of insured individuals, with the majority residing in rural regions (77.04%). The majority of individuals (80.14%) expressed satisfaction with several aspects of their lives, including their overall well-being, marital status, and having children. Overall, 80.83% of the patients experienced pain in multiple parts of their body.

Fig.1 Flowchart for screening study subjects

Relationship between musculoskeletal pain and depressive symptoms

The findings of the correlation study examining the baseline of characteristics, and the relationship between musculoskeletal pain and depression by univariate analysis symptoms are displayed in Table 1. Gender, present residency status, health insurance status, and education factors had a significant impact (P<0.05). However, no significant difference was observed between gender (P=0.33) and marital status (P=0.15) in the univariate analysis. Illiterate women living in rural areas had a significantly greater susceptibility to depression (P<0.01). Regarding health-related factors, all of the covariates exhibited significant differences between groups in both the univariate analysis and the multivariate analysis (P<0.05) in Table 2. Sleep issues, multisite pain, and chronic conditions were significant contributors to depression (all with a significance level of P<0.01). Naps (P=0.01), alcohol intake (P=0.04), and satisfaction (all with a significance level of P<0.01) are factors that protect against depression in patients with musculoskeletal pain.

TABLE 1 Baseline of characteristics, and relationship between musculoskeletal pain and depression symptoms by univariate analysis

 TABLE 1 (continued)

Fig.2 Histogram of number of people in musculoskeletal pain

TABLE 2 Multivariate analysis of factors associated with depressive symptoms in musculoskeletal pain

TABLE 2 (continued)

Relationship between the number of pain sites and depressive symptoms

According to the data presented in Table 3, a strong correlation exists between having more than 2 anatomical sites and a higher probability of experiencing depressionsymptoms, as determined using univariate analysis(P<0.01). TheNumber of individuals in each group is shown in Figure 2. However, as shown in Figure 3, when the number of pain sites exceeded 5, multivariate analysis combined with the above variables revealed a significant correlation with the prevalence of depression (P<0.05). The risk of depression is greatest when pain is present in 13 areas (OR 3.41, CI 2.27-5.11, P<0.01).

TABLE 3. Correlation between anatomical sites and depressionsymptoms

Fig.3 Forest plot of the relationship between pain site and depression symptoms after analysis by multivariate analysis.

Discussion

This utilizedcross-sectional data of study was from a sample of 10,278were over 45 yearsor elderlyold individuals. These data were collected in a follow-up survey of the 2018 CHARLS database. was CHARLS is a comprehensive database that contains precise fundamental information. This study disclosed the correlation between depression symptoms and musculoskeletal pain in individuals. Moreover, the results of this study may strengthen the existing knowledge base and serve as a benchmark for future epidemiological research.

The findings of this study revealed that the incidence of depressive symptoms in individuals with musculoskeletal pain was 37.4%, which is higher than the incidence of chronic disease patients and painless patients in the United States[26, 27]. Importantly, we specifically excluded basic physical pain and focused on a narrower range. However, the occurrence of depression in this study was greater than that reported by Qiu[11] for patients with bodily pain. These findings highlight the significant psychological health issues in this population. The present study did not find any statistically significant differences in terms of prevalence between Han Chinese individuals and ethnic minorities. However, our prevalence rate is similar to that reported in a 1,495-person cohort study in South Africa and Uganda (37.2%)[28]. This finding suggests a potential absence of racial disparities, which warrants further investigation.

Research on depression has mainly focused on the relationships between gender, pain, and depression [29]. As our report indicates, the incidence rate in females is higher than that in males, and Laia has similar reports as well[30]. There is evidence to suggest that women’s functional status is lower, leading to increased levels of psychological stress[31] and impaired self-perception of pain among women, especially those residing in economically disadvantaged rural regions[32], all of which may contribute to increased susceptibility to depression.

Older women are more susceptible to chronic diseases, which further increase the likelihood of depression and create a vicious cycle [26, 33]. Research from a significant Indian study indicated that additional factors, such as disability caused by coexisting conditions, contribute to depression [34]. Older women with lower education levels and living in rural areas typically have lower socioeconomic status and limited social support, which further increases their risk of developing depression[8, 33, 35, 36]. These findings are consistent with the results of the present study.

The relationship between pain and depression is complex, and inflammatory mediators play an important role in this connection[37]. First, the generation of inflammatory mediators results in pain. Additionally, it can also impair the blood-brain barrier and cause cellular and structural alterations in the central nervous system. These changes ultimately reduce reward pathways and lead to an increase in depression[33, 38].

Previous research has confirmed that age plays an important role in the onset of depression, and older age is associated with increased susceptibility to depression [12, 22, 39]. However, we did not observed any differences in the incidence of depression among the population aged 45 and above. This may be attributed to decreased hormone levels and reduced pain sensitivity in middle-aged and elderly individuals, which may not lead to age-based depression [40]. The majority of the elderly population no longer work and unemployed due to pain. Additionally, as age increases, the incidence of severe work-related stressors also decreased[41]. Meanwhile elderly patients with chronic pain may have more predictable and consistent experiences, thereby increasing their well-being scores. This may be the reason for the 80% patient satisfaction mentioned in the paper[39].

Previous research has established that multisite pain is a predictive factor for depression[42], and there are numerous similarities between multisite pain and geriatric disorders. Further research is needed to to examine multisite pain as a distinct geriatric syndrome[43]. Our study is consistent with the Ellen study [44], which revealed that insomnia and insufficient sleep time are risk factors for the onset of chronic pain. In addition, our findings indicate that excessive sleep can also lead to the development of depression.

Our study also has several drawbacks. It is worth noting that the present study was conducted in a cross-sectional manner. However, in order to validate our findings, more prospective investigations must be conducted. Furthermore, depression status survey and chronic illness rely on self-reports from participants rather than diagnoses from physicians, which may lead to varying degrees of bias in evaluating the relationship between chronic illness and depression. Thirdly, despite our efforts to account for various potential covariates, some variables have been overlooked.

For instance, previous studies have investigated the association between smoking and depression [45, 46], but due to the lack of number of observations on smoking, we did not analyze this relationship. This deficiency may have affected our research results.

Conclusion

Compared to other studies, we utilized a substantial sample from the CHARLS database to investigate the correlations between musculoskeletal pain and depression. Special attention should be paid to vulnerable populations, such as those who quit drinking, have difficulty sleeping and have experience pain in several areas, especially rural women with limited education. This study provides health screening instruments for healthcare professionals and can provide additional guidance for public health.

Abbreviations

ICD-10:10th edition of the International Classification of Diseases; CHARLS : China Health and Retirement Longitudinal Study; OR: odds ratio; CIs: confidence intervals;

Acknowledgments

The authors thank the China Health and Retirement Longitudinal Study

(CHARLS) team for providing data.

Authors’ contributions

YM Designed the study and conducted data analysis and manuscript writing. CW Interpret data . YL critically revised important knowledge content. GC and YL proceeded with data .LZ wrote comments. WW reviewed the final manuscript and provided suggestions for key changes.

Funding

This study was supported by Luoyang Key Laboratory of Digital Clinical Application Development and Research for Intelligent Orthopedics,Luoyang City.

Availability of data and materials

The original dataset provided in the study is available from the China Health and Retirement Longitudinal Study (CHARLS) website: http://charls.pku.edu.cn/en/

Declarations

Ethics approval and consent to participate

CHARLS reviewed and approved the study involving human participants and obtained ethical approval from the Ethics Review Board of Peking University (approval No. IRB0000105211015), and each subject signed an informed consent form. Written informed consent was not required for this study following national legislation and institutional requirements. Written informed consent was obtained from individuals for the release of any potentially recognizable images or data contained in this article.

Consent for publication

Not applicable.

Competing interests

Not applicable.

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