Abstract
Background
Shoulder pain is highly prevalent among diabetic and menopausal women, yet evidence on contributing factors in these high-risk groups is limited. In this study, the prevalence and associated clinical and lifestyle factors in both populations were analysed using large, nationally representative datasets.
Methods
The data for this study were obtained from the China Health and Retirement Longitudinal Study, which used a multistage sampling design. A total of 21,095 participants were initially recruited, after which strict inclusion and exclusion criteria were applied to ensure data quality. Statistical significance was defined as P < 0.05. Both univariate and multivariate logistic regression analyses were conducted to identify factors associated with shoulder pain among diabetic patients and menopausal women.
Results
The diabetes analysis included 12,214 individuals: 10,009 without diabetes and 2205 with diabetes. The menopausal analysis included 5535 women: 864 non-menopausal women and 4671 menopausal women. The prevalence of shoulder pain was 17.4% among diabetic patients and 14.1% among non-diabetic participants (P < 0.001), and 20.4% among menopausal women and 15.5% among non-menopausal women (P = 0.001). Multivariate logistic regression analysis revealed that shoulder pain in diabetic patients was significantly associated with female sex (OR 2.00, 95% CI 1.53–2.61), residency in a village (OR 1.46, 95% CI 1.13–1.89), sleep time (OR 0.83, 95% CI 0.79–0.88), and white blood cell count (OR 1.08, 95% CI 1.02–1.14). In menopausal women, significant factors included residency in a village (OR 1.21, 95% CI 1.02–1.43), sleep time (OR 0.81, 95% CI 0.78–0.84), white blood cell count (OR 1.05, 95% CI 1.01–1.10), and C-reactive protein level (OR 1.02, 95% CI 1.01–1.03).
Conclusions
This study revealed that shoulder pain is more prevalent among diabetic patients than among non-diabetic participants, and is more common among menopausal women than among non-menopausal women. Shoulder pain in diabetic patients and menopausal women is influenced by factors such as the living environment, sleep duration, and inflammatory markers. Notably, longer sleep duration appears to have a protective effect, and lifestyle factors and inflammatory status play important roles in the development of shoulder pain in these high-risk populations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13018-025-06546-w.
Keywords: Diabetic patients, Menopausal women, Shoulder pain, Risk factors, CHARLS
Background
Shoulder pain is among the most common musculoskeletal disorders in middle-aged and elderly people, and manifests primarily as limited shoulder joint movement and persistent or intermittent pain, which significantly affects quality of life and daily activities [1]. The incidence of shoulder pain increases with age and is influenced by various metabolic, endocrine, and lifestyle factors. Shoulder pain is commonly associated with various conditions, including frozen shoulder (FS) and cervical spondylosis with radiculopathy. Frozen shoulder, also known as adhesive capsulitis of the shoulder joint, is a fibrotic inflammatory condition of unknown etiology, that is characterized primarily by shoulder pain, joint stiffness, and limited range of motion [2]. The pathological basis of this condition is often chronic inflammation and fibrosis of the shoulder joint capsule and surrounding soft tissues. Radicular cervical spondylosis is caused primarily by compression of the nerve roots due to degenerative changes in the cervical intervertebral discs or narrowing of the intervertebral foramen. Among these, involvement of the C5 nerve root is particularly likely to result in significant shoulder pain and a reduced range of motion [3].
Diabetes is a common chronic metabolic disease. Previous studies [4, 5] have shown that diabetes is closely related to shoulder periarthritis, and that patients with diabetes are more likely to develop shoulder periarthritis. Furthermore, numerous studies [6–8] have shown that joint and muscle pain are among the most common symptoms of menopause. Patients with diabetes are more prone to shoulder joint disorders because of metabolic abnormalities and chronic inflammation, whereas menopausal women are more susceptible to joint and muscle pain as a result of hormonal changes. These findings suggest that shoulder pain may be more common among patients with diabetes and menopausal women.
Few studies have separately examined the factors associated with shoulder pain among diabetic patients and menopausal women using large, nationally representative datasets. Diabetes and menopause are common conditions affecting middle-aged and older adults, but shoulder pain in these populations is often overlooked. This study investigated the prevalence and associated factors of shoulder pain in diabetic and menopausal populations. It also provided evidence for early prevention strategies targeting shoulder pain.
Materials and methods
Study participants
The data used in this study were from the China Health and Retirement Longitudinal Study (CHARLS) Project [9], which is a nationally representative survey of Chinese residents aged 45 and above, organized by the National School of Development at Peking University and jointly implemented by the China Social Science Survey Center and the Communist Youth League Committee of Peking University. The project adopted a strict random sampling method. It employed a multistage sampling approach, using probability proportional to size (PPS) sampling at both the county/district and village/community levels, in which 450 primary units were selected from 150 counties or districts on the basis of size. Specifically, at the first stage, excluding Xizang, all other counties and districts nationwide were ranked within the eight regions according to urban/rural status and Gross Domestic Product (GDP) per capita, and 150 counties or districts were selected. At the second stage, three secondary sampling units were randomly selected from each selected county-level unit. Standardized questions and methodologies were used in the survey to collect thorough and high-quality data concerning the health condition of the population 45 years and older.
To maintain the long-term representativeness of adults aged 45 and above in mainland China, CHARLS reserved individuals aged 40–44 as replacement samples at the baseline survey for use in future waves. When respondents were unable to complete the interview due to health or other reasons, CHARLS employed a strictly authorized proxy interview procedure, in which a knowledgeable family member answered on behalf of the respondent to ensure data completeness and accuracy. The CHARLS questionnaire was designed with reference to international survey experiences. It pioneered the use of electronic mapping software to create village-level sampling frames, covered multiple dimensions including health, economy, family, and social aspects, and a pilot survey was conducted. Survey interviewers were recruited and trained through structured courses and field simulations to ensure familiarity with the questionnaire and proficiency in standardized interviewing skills. The survey was supervised, and local dialects or video interviews were used when necessary to ensure smooth fieldwork. CHARLS’s quality control measures included conducting quality control training for interviewers. To ensure data quality, the project team employed data checks and audio reviews, and the quality control team provided detailed feedback to interviewers. CHARLS tracked baseline respondents and their spouses, employing multiple contact methods to maintain follow-up and achieve high response rates.
Furthermore, blood biomarkers [10] can indicate inflammation, metabolic control, and general physiological status, and their inclusion in this study allowed for a more comprehensive assessment of factors related to shoulder pain.
All CHARLS surveys received ethical authorization from the Institutional Review Board (IRB) at Peking University (IRB00001052-11015). Furthermore, the protocol of the blood-based biomarker sample collection study was approved by the ethical review committee of Peking University (IRB00001052-11014). During the field survey, all respondents who agreed to participate were required to sign two copies of the informed consent form: one copy was kept by the respondent, and the other was stored at the CHARLS office. All data collected by the CHARLS project are stored at the China Social Science Survey Center of Peking University. The data and study materials are available on the CHARLS project website (http://charls.pku.edu.cn/).
The high quality of the CHARLS data and the large sample size provide real and effective data support for the analysis in this paper. This study was a secondary analysis of the CHARLS 2015 survey data.
Inclusion and exclusion criteria
For the analysis of the association between diabetes and shoulder pain, all participants from the CHARLS 2015 dataset were included, and those with missing data for any study variables were excluded. To analyse the association between menopausal status and shoulder pain, we first included all participants in the CHARLS 2015 dataset; after excluding male participants and those with missing sex information, only female participants were retained. Participants with missing data on any study variables or missing information on menopausal status were further excluded. The outcome variable was the presence or absence of shoulder pain on the basis of self-reported responses to the CHARLS questionnaire. Data quality was ensured by using the nationally representative CHARLS database, which used a multistage sampling design. The final sample size was determined on the basis of the available data after the inclusion and exclusion criteria were applied.
Definition of diabetes and assessment of menopausal status in this study
To collect comprehensive data related to people with diabetes, we included the health status and functioning data, as well as the blood data of the participants. The diagnostic criteria for diabetes in this study were any of the following: selecting “yes” for “had diabetes or high blood sugar”, or receiving any treatments to treat or control diabetes, or having a fasting blood glucose concentration ≥ 126 mg/dl, or a glycosylated hemoglobin (HbA1c) level ≥ 6.5%.
Menopausal status was determined through self-reports by respondents; on the basis of their response to the questionnaire item “Have you started menopause?”. Respondents who answered “yes” were classified as menopausal women, and those who answered “no” were classified as non-menopausal women.
Investigation content relevant to this study
The information collected from all the study participants included demographic background (age, gender, residence, and marital status), health status and functioning (sleep time, nap time, and shoulder pain), anthropometric indicators (height and weight), and blood data (white blood cell count, hemoglobin level, hematocrit level, mean corpuscular volume, platelets count, and levels of triglycerides, creatinine, blood urea nitrogen, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, glucose, uric acid, cystatin C, C-reactive protein, and glycated hemoglobin). To ensure the accuracy and completeness of the study, sample sizes with missing values for any variables were excluded. In this study, shoulder pain was defined as the dependent variable, whereas all the other variables served as independent variables. Continuous variables included age, sleep time, nap time, height, weight, and blood data, whereas categorical variables included gender, residence, and marital status.
Shoulder pain was defined on the basis of the questionnaire item “On what part of your body do you feel pain?” Respondents who answered “Shoulder” were classified as having shoulder pain.
Age was calculated based on the respondent’s date of birth. Sex was recorded by the interviewers, including male and female. Residence was classified on the basis of the CHARLS survey definitions. Main city zone, combination zone between urban and rural areas, the town center, ZhenXiang area, special area, and township central were categorized as “city or town”, whereas village was categorized as “village”.
Marital status was defined on the basis of the questionnaire item “What is your marital status?” Respondents who answered “married with spouse present” or “married but not living with spouse temporarily for reasons such as work” were classified as “yes”, whereas those who answered “separated”, “divorced”, “widowed”, “never married”, or “cohabitated” were classified as “no”.
Sleep time was recorded based on the questionnaire item, “During the past month, how many hours of actual sleep did you get at night (this may be shorter than the number of hours you spend in bed)?” The unit of sleep time was hours. Nap time was recorded based on the questionnaire item, “During the past month, how long did you take a nap after lunch?” The unit of nap time was minutes. Height and weight were obtained through measurements, and blood data were obtained from laboratory tests.
Statistical analyses
In this study, continuous variables are expressed as medians and interquartile ranges, and rank-sum tests were used to compare groups. Categorical variables are expressed as percentages, and χ2 tests or Fisher’s exact tests were used for group comparisons. Demographic characteristics and covariates were compared for the presence or absence of shoulder pain in diabetic patients and non-diabetic patients, or in menopausal women and non-menopausal women. When α = 0.05, P < 0.05 indicated that the difference is statistically significant. Bold values in the tables indicated statistical significance. Univariate logistic regression analysis was initially used to determine the factors associated with shoulder pain in diabetic patients or menopausal women. To include statistically significant variables and maintain a parsimonious model, variables with P values < 0.05 in the univariate logistic regression analysis were then included in the multivariate logistic regression analysis. The significance level was set at 0.05 to examine the factors associated with shoulder pain in diabetic patients or menopausal women. Continuous variables in the logistic regression model were not assigned a reference category, whereas categorical variables were analysed with clearly defined reference groups. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were calculated. When the OR was > 1.0, the variable was considered a risk factor for shoulder pain; when the OR was < 1.0, the variable was considered to have a protective effect. R software (version 4.2.3) was used for all the analyses in this study.
Results
Participant characteristics
A total of 12,214 individuals were included in the analysis of shoulder pain among diabetic patients, including 10,009 non-diabetic patients and 2205 diabetic patients. The prevalence of shoulder pain was greater among diabetic patients (17.4%) than among non-diabetic participants (14.1%) (P < 0.001), as shown in Table 1. For the analysis of menopausal status, 5535 women were included, with 864 non-menopausal women and 4671 menopausal women. The prevalence of shoulder pain was greater among menopausal women (20.4%) than among non-menopausal women (15.5%) (P = 0.001), as shown in Table 2. The overall study flow is shown in Fig. 1.
Table 1.
The sociodemographic and clinical characteristics of CHARLS 2015 participants with or without diabetes
| Variables | Total (n = 12,214) |
No diabetes (n = 10,009) |
Diabetes (n = 2205) |
P value |
|---|---|---|---|---|
| Sociodemographic characteristics | ||||
| Age (years) | 64.00 [56.00, 70.00] | 63.00 [55.00, 70.00] | 66.00 [59.00, 73.00] | < 0.001 |
| Gender (%) | 0.003 | |||
| Male | 5693 (46.6) | 4729 (47.2) | 964 (43.7) | |
| Female | 6521 (53.4) | 5280 (52.8) | 1241 (56.3) | |
| Residence (%) | < 0.001 | |||
| City or town | 3212 (26.3) | 2500 (25.0) | 712 (32.3) | |
| Village | 9002 (73.7) | 7509 (75.0) | 1493 (67.7) | |
| Marriage (%) | < 0.001 | |||
| Yes | 10,733 (87.9) | 8846 (88.4) | 1887 (85.6) | |
| No | 1481 (12.1) | 1163 (11.6) | 318 (14.4) | |
| Sleep time (hours) | 6.00 [5.00, 8.00] | 6.00 [5.00, 8.00] | 6.00 [5.00, 8.00] | 0.287 |
| Nap time (minutes) | 30.00 [0.00, 60.00] | 30.00 [0.00, 60.00] | 30.00 [0.00, 60.00] | 0.001 |
| Height (cm) | 158.00 [152.30, 164.40] | 158.10 [152.40, 164.50] | 157.50 [152.10, 164.20] | 0.067 |
| Weight (kg) | 59.50 [52.50, 67.47] | 58.90 [52.10, 66.60] | 62.80 [55.10, 71.10] | < 0.001 |
| Clinical characteristics | ||||
| WBC (1000) | 5.72 [4.78, 6.90] | 5.67 [4.70, 6.80] | 6.10 [5.10, 7.34] | < 0.001 |
| Hb (g/dl) | 13.60 [12.50, 14.80] | 13.60 [12.50, 14.80] | 13.70 [12.60, 14.80] | 0.767 |
| Hct (%) | 41.20 [38.10, 44.70] | 41.20 [38.10, 44.70] | 41.20 [38.00, 44.60] | 0.895 |
| MCV (fl) | 91.90 [88.00, 95.70] | 92.10 [88.20, 95.90] | 91.00 [87.00, 94.60] | < 0.001 |
| PLT (109/L) | 202.00 [160.00, 244.00] | 201.00 [160.00, 244.00] | 205.00 [164.00, 248.00] | 0.008 |
| TG (mg/dl) | 115.04 [83.19, 169.91] | 109.73 [80.53, 161.06] | 144.25 [99.12, 213.27] | < 0.001 |
| Cr (mg/dl) | 0.76 [0.66, 0.89] | 0.76 [0.66, 0.89] | 0.74 [0.63, 0.89] | < 0.001 |
| BUN (mg/dl) | 14.85 [12.32, 17.93] | 14.85 [12.32, 17.93] | 14.85 [12.32, 18.21] | 0.098 |
| HDL (mg/dl) | 49.81 [43.24, 57.53] | 50.58 [43.63, 57.92] | 47.10 [40.54, 54.05] | < 0.001 |
| LDL (mg/dl) | 100.39 [82.63, 119.31] | 99.61 [82.24, 118.53] | 103.47 [83.40, 123.17] | < 0.001 |
| TC (mg/dl) | 181.08 [159.07, 205.41] | 179.92 [158.30, 203.47] | 186.87 [162.93, 212.74] | < 0.001 |
| GLU (mg/dl) | 95.50 [88.29, 106.31] | 93.69 [86.49, 100.90] | 122.52 [100.90, 151.35] | < 0.001 |
| UA (mg/dl) | 4.80 [3.90, 5.70] | 4.80 [3.90, 5.70] | 4.90 [4.00, 5.90] | < 0.001 |
| CysC (mg/l) | 0.82 [0.71, 0.94] | 0.81 [0.71, 0.94] | 0.84 [0.73, 0.97] | < 0.001 |
| CRP (mg/l) | 1.40 [0.70, 2.60] | 1.30 [0.70, 2.40] | 2.00 [1.00, 3.60] | < 0.001 |
| HbA1c (%) | 5.80 [5.50, 6.10] | 5.70 [5.50, 6.00] | 6.70 [6.30, 7.70] | < 0.001 |
| Outcome variable | ||||
| Shoulder pain (%) | < 0.001 | |||
| No | 10,416 (85.3) | 8594 (85.9) | 1822 (82.6) | |
| Yes | 1798 (14.7) | 1415 (14.1) | 383 (17.4) | |
WBC white blood cell, Hb hemoglobin, Hct hematocrit, MCV mean corpuscular volume, PLT platelets, TG triglycerides, Cr creatinine, BUN blood urea nitrogen, HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterol, TC total cholesterol, GLU glucose, UA uric acid, CysC cystatin C, CRP C-reactive protein, HbA1c glycated hemoglobin
Table 2.
The sociodemographic and clinical characteristics of CHARLS 2015 female participants with or without menopause
| Variables | Total (n = 5535) |
Non-menopausal (n = 864) |
Menopausal (n = 4671) |
P-value |
|---|---|---|---|---|
| Sociodemographic characteristics | ||||
| Age (years) | 64.00 [56.00, 70.00] | 53.00 [51.00, 55.00] | 66.00 [60.00, 72.00] | < 0.001 |
| Residence (%) | 0.946 | |||
| City or town | 1392 (25.1) | 216 (25.0) | 1176 (25.2) | |
| Village | 4143 (74.9) | 648 (75.0) | 3495 (74.8) | |
| Marriage (%) | < 0.001 | |||
| Yes | 4671 (84.4) | 831 (96.2) | 3840 (82.2) | |
| No | 864 (15.6) | 33 ( 3.8) | 831 (17.8) | |
| Sleep time (hours) | 6.00 [5.00, 8.00] | 7.00 [5.00, 8.00] | 6.00 [5.00, 8.00] | < 0.001 |
| Nap time (minutes) | 10.00 [0.00, 60.00] | 15.00 [0.00, 60.00] | 5.00 [0.00, 60.00] | 0.317 |
| Height (cm) | 153.00 [149.00, 157.00] | 155.15 [151.10, 158.90] | 152.50 [148.60, 156.60] | < 0.001 |
| Weight (kg) | 56.50 [50.20, 63.60] | 59.05 [53.30, 65.90] | 56.00 [49.50, 63.20] | < 0.001 |
| Clinical characteristics | ||||
| WBC (1000) | 5.54 [4.60, 6.61] | 5.61 [4.64, 6.80] | 5.53 [4.60, 6.60] | 0.188 |
| Hb (g/dl) | 12.90 [12.10, 13.80] | 12.80 [11.80, 13.70] | 12.90 [12.10, 13.80] | < 0.001 |
| Hct (%) | 39.40 [36.80, 42.00] | 39.00 [36.00, 41.70] | 39.50 [36.90, 42.00] | < 0.001 |
| MCV (fl) | 91.00 [87.30, 94.64] | 89.80 [84.50, 93.50] | 91.30 [87.70, 94.90] | < 0.001 |
| PLT (109/L) | 211.00 [167.00, 254.00] | 228.50 [184.00, 271.00] | 207.00 [165.00, 251.00] | < 0.001 |
| TG (mg/dl) | 122.12 [89.38, 176.99] | 109.73 [80.53, 166.37] | 124.78 [91.15, 177.88] | < 0.001 |
| Cr (mg/dl) | 0.68 [0.61, 0.76] | 0.65 [0.59, 0.72] | 0.68 [0.61, 0.77] | < 0.001 |
| BUN (mg/dl) | 14.29 [12.04, 17.37] | 12.89 [10.64, 15.41] | 14.57 [12.32, 17.65] | < 0.001 |
| HDL (mg/dl) | 51.35 [44.79, 58.69] | 51.16 [44.79, 58.30] | 51.35 [44.79, 58.69] | 0.396 |
| LDL (mg/dl) | 105.02 [85.71, 123.94] | 98.46 [81.47, 115.44] | 106.18 [87.26, 125.10] | < 0.001 |
| TC (mg/dl) | 188.03 [166.41, 212.74] | 177.99 [157.92, 198.07] | 190.35 [168.34, 214.67] | < 0.001 |
| GLU (mg/dl) | 95.50 [88.29, 106.31] | 91.89 [86.49, 100.90] | 95.50 [88.29, 106.31] | < 0.001 |
| UA (mg/dl) | 4.30 [3.60, 5.10] | 4.00 [3.40, 4.80] | 4.40 [3.70, 5.20] | < 0.001 |
| CysC (mg/l) | 0.80 [0.69, 0.92] | 0.68 [0.61, 0.77] | 0.83 [0.72, 0.94] | < 0.001 |
| CRP (mg/l) | 1.40 [0.80, 2.60] | 1.10 [0.60, 2.20] | 1.50 [0.80, 2.70] | < 0.001 |
| HbA1c (%) | 5.80 [5.60, 6.20] | 5.70 [5.40, 6.00] | 5.90 [5.60, 6.20] | < 0.001 |
| Outcome variable | ||||
| Shoulder pain (%) | 0.001 | |||
| No | 4446 (80.3) | 730 (84.5) | 3716 (79.6) | |
| Yes | 1089 (19.7) | 134 (15.5) | 955 (20.4) | |
WBC white blood cell, Hb hemoglobin, Hct hematocrit, MCV mean corpuscular volume, PLT platelets, TG triglycerides, Cr creatinine, BUN blood urea nitrogen, HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterol, TC total cholesterol, GLU glucose, UA uric acid, CysC cystatin C, CRP C-reactive protein, HbA1c glycated hemoglobin
Fig. 1.
Study flowchart of the participant selection process
Prevalence of shoulder pain among diabetic patients
As shown in Supplementary Fig. S1 and Table S5, the prevalence of shoulder pain among diabetic patients was 17.4% (383/2205). Significant differences were observed in terms of gender, residence, sleep duration, nap time, height, weight, hemoglobin level, hematocrit level, mean corpuscular volume, and levels of creatinine, glucose, and glycated hemoglobin (P < 0.05). For details, see Supplementary Table S1.
Prevalence of shoulder pain among non-diabetic patients
As shown in Supplementary Fig. S1 and Table S5, the prevalence of shoulder pain among non-diabetic patients was 14.1% (1415/10,009). Significant differences were observed in terms of gender, residence, marital status, sleep duration, nap time, height, weight, hemoglobin level, hematocrit level, and levels of creatinine, high-density lipoprotein cholesterol, glucose, uric acid, and C-reactive protein (P < 0.05). For details, see Supplementary Table S2.
Prevalence of shoulder pain among menopausal women
As shown in Supplementary Fig. S1 and Table S5, the prevalence of shoulder pain among menopausal women was 20.4% (955/4671). Significant differences were observed in terms of residence, sleep time, height, weight, white blood cell count, glucose, and C-reactive protein (P < 0.05). For details, see Supplementary Table S3.
Prevalence of shoulder pain among non-menopausal women
As shown in Supplementary Fig. S1 and Table S5, the prevalence of shoulder pain among non-menopausal women was 15.5% (134/864). Significant differences were observed in terms of sleep time, mean corpuscular volume, triglycerides, high-density lipoprotein cholesterol, and cystatin C (P < 0.05). For details, see Supplementary Table S4.
Risk factors for shoulder pain among diabetic patients
The logistic regression results for shoulder pain risk factors among diabetic patients are shown in Table 3. Univariate logistic regression revealed significant factors for shoulder pain in diabetic patients, including: female gender (OR 2.33, 95% CI 1.83–2.96), residency in village (OR 1.50, 95% CI 1.17–1.93), unmarried status (OR 1.36, 95% CI 1.01–1.82), sleep time (OR 0.81, 95% CI 0.77–0.86), nap time (OR 1.00, 95% CI 0.99–1.00), white blood cell count (OR 1.06, 95% CI 1.00–1.11), hemoglobin level (OR 0.88, 95% CI 0.83–0.93), hematocrit level (OR 0.95, 95% CI 0.93–0.97), mean corpuscular volume (OR 0.99, 95% CI 0.97–1.00), and C-reactive protein (OR 1.01, 95% CI 1.00–1.03).
Table 3.
The univariate and multivariate logistic regression analysis of risk factors for shoulder pain in CHARLS 2015 diabetic patients
| Variables | Univariate | Multivariate | ||
|---|---|---|---|---|
| OR (95%CI) | P value | OR (95%CI) | P value | |
| Sociodemographic characteristics | ||||
| Age (years) | 1.00 (0.99–1.02) | 0.516 | ||
| Gender (%) | ||||
| Male | ||||
| Female | 2.33 (1.83–2.96) | < 0.001 | 2.00 (1.53–2.61) | < 0.001 |
| Residence (%) | ||||
| City or town | ||||
| Village | 1.50 (1.17–1.93) | 0.001 | 1.46 (1.13–1.89) | 0.004 |
| Marriage (%) | ||||
| Yes | ||||
| No | 1.36 (1.01–1.82) | 0.042 | 1.12 (0.82–1.52) | 0.476 |
| Sleep time (hours) | 0.81 (0.77–0.86) | < 0.001 | 0.83 (0.79–0.88) | < 0.001 |
| Nap time (minutes) | 1.00 (0.99–1.00) | 0.013 | 1.00 (1.00–1.00) | 0.107 |
| Height (cm) | 0.99 (0.98–1.00) | 0.127 | ||
| Weight (kg) | 1.00 (1.00–1.00) | 0.873 | ||
| Clinical characteristics | ||||
| WBC (1000) | 1.06 (1.00–1.11) | 0.037 | 1.08 (1.02–1.14) | 0.006 |
| Hb (g/dl) | 0.88 (0.83–0.93) | < 0.001 | 1.02 (0.91–1.14) | 0.744 |
| Hct (%) | 0.95 (0.93–0.97) | < 0.001 | 0.97 (0.94–1.01) | 0.190 |
| MCV (fl) | 0.99 (0.97–1.00) | 0.033 | 1.00 (0.98–1.02) | 0.962 |
| PLT (109/L) | 1.00 (1.00–1.00) | 0.664 | ||
| TG (mg/dl) | 1.00 (1.00–1.00) | 0.799 | ||
| Cr (mg/dl) | 0.64 (0.40–1.00) | 0.052 | ||
| BUN (mg/dl) | 1.01 (0.99–1.03) | 0.403 | ||
| HDL (mg/dl) | 1.00 (1.00–1.01) | 0.345 | ||
| LDL (mg/dl) | 1.00 (1.00–1.01) | 0.254 | ||
| TC (mg/dl) | 1.00 (1.00–1.00) | 0.152 | ||
| GLU (mg/dl) | 1.00 (0.99–1.00) | 0.005 | 1.00 (1.00–1.00) | 0.030 |
| UA (mg/dl) | 0.95 (0.88–1.02) | 0.180 | ||
| CysC (mg/l) | 1.15 (0.82–1.60) | 0.413 | ||
| CRP (mg/l) | 1.01 (1.00–1.03) | 0.036 | 1.01 (1.00–1.02) | 0.107 |
| HbA1c (%) | 0.95 (0.89–1.02) | 0.195 | ||
WBC white blood cell, Hb hemoglobin, Hct hematocrit, MCV mean corpuscular volume, PLT platelets, TG triglycerides, Cr creatinine, BUN blood urea nitrogen, HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterol, TC total cholesterol, GLU glucose, UA uric acid, CysC cystatin C, CRP C-reactive protein, HbA1c glycated hemoglobin, CI confidence intervals
Multivariate logistic regression revealed significant factors for shoulder pain in diabetic patients, including: female gender (OR 2.00, 95% CI 1.53–2.61), residency in village (OR 1.46, 95% CI 1.13–1.89), sleep time (OR 0.83, 95% CI 0.79–0.88), and white blood cell count (OR 1.08, 95% CI 1.02–1.14). The factors associated with shoulder pain among diabetic patients are shown in Fig. 2.
Fig. 2.
The univariate and multivariate logistic regression analysis of risk factors for shoulder pain in CHARLS 2015 diabetic patients. WBC white blood cell, Hb hemoglobin, Hct hematocrit, MCV mean corpuscular volume, PLT platelets, TG triglycerides, Cr creatinine, BUN blood urea nitrogen, HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterol, TC total cholesterol, GLU glucose, UA uric acid, CysC cystatin C, CRP C-reactive protein, HbA1c glycated hemoglobin
Risk factors for shoulder pain among menopausal women
The logistic regression results for shoulder pain risk factors among menopausal women are shown in Table 4. Univariate logistic regression revealed significant factors for shoulder pain in menopausal women, including: residency in village (OR 1.23, 95% CI 1.04–1.45), sleep time (OR 0.81, 95% CI 0.78–0.84), nap time (OR 1.00, 95% CI 1.00–1.00), white blood cell count (OR 1.07, 95% CI 1.03–1.11), cystatin C (OR 1.31, 95% CI 1.01–1.70), and C-reactive protein (OR 1.03, 95% CI 1.01–1.04).
Table 4.
The univariate and multivariate logistic regression analysis of risk factors for shoulder pain in CHARLS 2015 menopausal women
| Variables | Univariate | Multivariate | ||
|---|---|---|---|---|
| OR (95%CI) | P value | OR (95%CI) | P value | |
| Sociodemographic characteristics | ||||
| Age (years) | 1.00 (1.00–1.01) | 0.422 | ||
| Residence (%) | ||||
| City or town | ||||
| Village | 1.23 (1.04–1.45) | 0.018 | 1.21 (1.02–1.43) | 0.032 |
| Marriage (%) | ||||
| Yes | ||||
| No | 1.19 (0.99–1.43) | 0.057 | ||
| Sleep time (hours) | 0.81 (0.78–0.84) | < 0.001 | 0.81 (0.78–0.84) | < 0.001 |
| Nap time (minutes) | 1.00 (1.00–1.00) | 0.046 | 1.00 (1.00–1.00) | 0.411 |
| Height (cm) | 1.00 (1.00–1.00) | 0.666 | ||
| Weight (kg) | 1.00 (1.00–1.00) | 0.232 | ||
| Clinical characteristics | ||||
| WBC (1000) | 1.07 (1.03–1.11) | 0.001 | 1.05 (1.01–1.10) | 0.017 |
| Hb (g/dl) | 0.97 (0.93–1.02) | 0.250 | ||
| Hct (%) | 0.99 (0.98–1.01) | 0.202 | ||
| MCV (fl) | 0.99 (0.98–1.00) | 0.216 | ||
| PLT (109/L) | 1.00 (1.00–1.00) | 0.680 | ||
| TG (mg/dl) | 1.00 (1.00–1.00) | 0.707 | ||
| Cr (mg/dl) | 1.05 (0.80–1.37) | 0.731 | ||
| BUN (mg/dl) | 1.01 (0.99–1.02) | 0.453 | ||
| HDL (mg/dl) | 1.00 (1.00–1.01) | 0.531 | ||
| LDL (mg/dl) | 1.00 (1.00–1.00) | 0.238 | ||
| TC (mg/dl) | 1.00 (1.00–1.00) | 0.825 | ||
| GLU (mg/dl) | 1.00 (1.00–1.00) | 0.216 | ||
| UA (mg/dl) | 1.04 (0.98–1.10) | 0.214 | ||
| CysC (mg/l) | 1.31 (1.01–1.70) | 0.044 | 1.17 (0.89–1.55) | 0.252 |
| CRP (mg/l) | 1.03 (1.01–1.04) | < 0.001 | 1.02 (1.01–1.03) | 0.003 |
| HbA1c (%) | 1.01 (0.95–1.08) | 0.654 | ||
WBC white blood cell, Hb hemoglobin, Hct hematocrit, MCV mean corpuscular volume, PLT platelets, TG triglycerides, Cr creatinine, BUN blood urea nitrogen, HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterol, TC total cholesterol, GLU glucose, UA uric acid, CysC cystatin C, CRP C-reactive protein, HbA1c glycated hemoglobin, CI confidence intervals
Multivariate logistic regression revealed significant factors for shoulder pain in menopausal women, including: residency in village (OR 1.21, 95% CI 1.02–1.43), sleep time (OR 0.81, 95% CI 0.78–0.84), white blood cell count (OR 1.05, 95% CI 1.01–1.10), and C-reactive protein (OR 1.02, 95% CI 1.01–1.03). The factors associated with shoulder pain among menopausal women are shown in Fig. 3.
Fig. 3.
The univariate and multivariate logistic regression analysis of risk factors for shoulder pain in CHARLS 2015 menopausal women. WBC white blood cell, Hb hemoglobin, Hct hematocrit, MCV mean corpuscular volume, PLT platelets, TG triglycerides Cr creatinine, BUN blood urea nitrogen, HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterol, TC total cholesterol, GLU glucose, UA uric acid, CysC cystatin C, CRP C-reactive protein, HbA1c glycated hemoglobin
Discussion
This study investigated shoulder pain in two specific populations: diabetic patients and menopausal women. The incidence of shoulder pain has shown considerable variation in previous studies [11–13], which may be attributable to differences in study populations, geographic regions, lifestyle factors, occupational types, cultural backgrounds, access to health care, and study methods or diagnostic criteria. Although the reported prevalence of shoulder pain has been inconsistent, our study clearly revealed that the prevalence of shoulder pain is significantly greater in diabetic patients than in non-diabetic individuals, and significantly greater in menopausal women than in non-menopausal women. Furthermore, multivariable logistic regression analysis revealed significant factors associated with shoulder pain among individuals with diabetes, including female sex, village residence, sleep duration, and white blood cell count. In menopausal women, the significant factors included village residence, sleep duration, white blood cell count, and C-reactive protein level.
Diabetes is a common chronic metabolic disease that causes various microvascular and macrovascular complications. It is associated with musculoskeletal complications, among which frozen shoulder is frequently reported [14]. It is also accompanied by chronic inflammation and local microcirculatory disorders, which increase the risk of shoulder pain. The prevalence of shoulder joint dysfunction among diabetic patients is very high [15]. Diabetic patients may require additional rehabilitation measures and routine blood sugar control to treat adhesive capsulitis [16]. Shoulder periarthritis is an inflammatory condition that causes stiffness and pain in the shoulder joint. Its occurrence is associated with various diseases and factors, including diabetes, cervical spondylosis, thyroid dysfunction, autoimmune rheumatic diseases, and shoulder injuries caused by trauma, fractures, rotator cuff tears, surgery, or immobilization [17]. Additionally, diabetes, cervical spondylosis, and hyperlipidaemia have been confirmed as important risk factors for the development of shoulder periarthritis [18]. Radicular cervical spondylosis can also cause severe shoulder pain. These factors may help explain why we observed a higher incidence of shoulder pain among diabetic patients than among non-diabetic individuals in our study.
Shoulder pain and dysfunction are highly prevalent among patients with diabetes, and have disproportionate effects on women and older adults [19]. Furthermore, shoulder periarthritis usually affects people between the ages of 40 and 60 [20]. Our findings also revealed that shoulder pain is closely associated with female gender among diabetic patients, suggesting that women may be an important population susceptible to shoulder pain. Therefore, women, especially menopausal women, warrant focused attention regarding the prevalence and determinants of shoulder pain. Menopause, as an important physiological characteristic of middle-aged and elderly women, may play a key role in this association; therefore, further exploration of its potential mechanisms is necessary. These results indicate that shoulder pain is highly prevalent among middle-aged women and is associated with a history of shoulder pain or trauma, osteoporosis, trapezius muscle pain, and cervical radiculopathy [21]. Joint pain is a common symptom experienced by many women before and after menopause, and musculoskeletal pain is among the most serious complaints among menopausal women [22, 23]. Menopause decreases oestrogen levels, which affects tendons, bones, muscles, and joints, thereby increasing the risk of shoulder pain. These factors may explain reason why menopausal women in our study had a higher incidence of shoulder pain than non-menopausal women did.
This study revealed that living in village areas is an important factor influencing shoulder pain among diabetic patients and menopausal women. Shoulder pain is more common in village areas than in urban areas [24]. These results may indicate that socioeconomic factors, lifestyle, and access to medical resources may play important roles in the mechanisms underlying urban–rural differences in shoulder pain. Additionally, individuals living in villages may engage in more physically demanding or prolonged labour, placing greater stress on the shoulder joints. Due to lower health awareness and limited early intervention, this may lead to a higher prevalence of shoulder pain. Future studies should explore the impact of urban–rural differences on shoulder pain to develop more targeted prevention and management strategies.
Our research revealed that sleep duration is a significant factor affecting shoulder pain in diabetic patients and menopausal women. Sleep disorders are very common among people with diabetes and have a negative effect on health outcomes [25, 26]. In addition, sleep disorders during menopause are common and significant. Poor sleep quality is a widespread phenomenon among menopausal women, and sleep disorders are among the most common and debilitating symptoms experienced by women during the menopausal transition [27–29]. Women with shoulder pain often have poor sleep quality, and sleep quality may also be associated with activity-related pain and disability in patients with frozen shoulder [30, 31]. Our findings emphasize the important role of sleep in shoulder pain among diabetic patients and menopausal women. Both populations commonly experience sleep disturbances, and insufficient or poor-quality sleep may increase the risk of shoulder pain, whereas adequate sleep duration appears to have a protective effect.
Additionally, this study revealed that in diabetic patients, white blood cell count was a significant factor associated with shoulder pain; in menopausal women, both white blood cell count and C-reactive protein level were significantly associated with shoulder pain. Elevated white blood cell counts often indicate the presence of chronic inflammation or hyperactive immune responses, and chronic inflammation is closely associated with the onset and progression of shoulder periarthritis [32, 33]. Diabetic patients who are chronically exposed to high blood glucose levels may develop low-grade chronic inflammatory responses; in menopausal women, decreased oestrogen levels increase the sensitivity and severity of inflammatory responses, and elevated C-reactive protein levels further support the role of systemic inflammation in the course of shoulder pain. Our findings suggest that inflammation may be an important factor in shoulder pain among diabetic and menopausal women. Clinical care should focus on inflammation monitoring and potential anti-inflammatory treatment.
These findings suggest that diabetic patients and menopausal women represent high-risk populations for shoulder pain. The identification of modifiable and non-modifiable factors such as female sex, village residence, sleep duration, and inflammatory markers highlights potential targets for prevention and intervention. Clinically, these results underscore the importance of tailored strategies to monitor and address these risk factors, including optimizing sleep, managing systemic inflammation, and considering sociodemographic contexts, to reduce the prevalence and impact of shoulder pain in these vulnerable populations.
Strengths and limitations
This study had several significant advantages. First, the CHARLS database was used to analyse the prevalence of shoulder pain and its associated risk factors among diabetic patients and menopausal women. Second, the results provide a foundation for understanding the risk factors for shoulder pain in diabetic patients and menopausal women and for developing preventive strategies. This study also had several limitations. Menopause status was assessed on the basis of self-reports, which may introduce misclassification bias. Pain assessment relies primarily on questionnaires, which may introduce recall bias. Certain potential confounding factors, such as occupational strain or specific exercise habits, were not included, which may have influenced the results. Future prospective cohort studies combining imaging and biomarkers could further clarify the relationships and underlying mechanisms between diabetes and shoulder pain, as well as between menopause and shoulder pain.
Conclusions
This study revealed that shoulder pain is more prevalent among diabetic patients than among non-diabetic individuals, and is more common among menopausal women than among non-menopausal women. In these high-risk populations, shoulder pain is influenced by multiple factors, including the living environment, sleep duration, and inflammatory markers. Notably, a longer sleep duration appears to have a protective effect, and lifestyle factors and inflammatory status play key roles in the development of shoulder pain. These findings suggest that interventions aimed at improving lifestyle, increasing sleep quality, and controlling inflammation may help prevent or alleviate shoulder pain in high-risk groups. These results provide important evidence for early prevention, clinical management, and public health strategies targeting shoulder pain in vulnerable populations.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
This study was conducted using data from the China Longitudinal Study of Health and Retirement (CHARLS). The authors express their gratitude to the CHARLS team for providing the data. We also extend our heartfelt appreciation to the staff of the CHARLS database and the survey respondents.
Abbreviations
- CHARLS
China Health and Retirement Longitudinal Study
- CI
Confidence intervals
- FS
Frozen shoulder
- GDP
Gross domestic product
- IRB
Institutional Review Board
- OR
Odds ratios
- PPS
Probability proportional to size
Author contributions
XW: Software, formal analysis, investigation, writing—original draft. YHZ: Validation, visualization, writing—review and editing. QSZ: Conceptualization, methodology, writing—review and editing, supervision.
Funding
This work was supported by Jilin Province Health Research Talents Special Project (Grant number: 2020SCZ17).
Data availability
The data that support the findings of this study are available from the China Health and Retirement Longitudinal Study (CHARLS) website, subject to the registration and application process. Further details can be found at https://charls.pku.edu.cn/.
Declarations
Ethics approval and consent to participate
All CHARLS surveys received ethical authorization from the Institutional Review Board (IRB) at Peking University (IRB00001052-11015). Furthermore, the protocol of the blood-based biomarker sample collection study was approved by the ethical review committee of Peking University (IRB00001052-11014). All participants completed the informed consent forms.
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.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the China Health and Retirement Longitudinal Study (CHARLS) website, subject to the registration and application process. Further details can be found at https://charls.pku.edu.cn/.