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Prevalence and risk factors of lower back pain in middle-aged and elderly people with sarcopenia: a nationwide cross-sectional study

BMC Public Health volume 25, Article number: 1517 (2025) Cite this article

Abstract

Background

Although sarcopenia and lower back pain (LBP) are common geriatric conditions for older adults, the relationship between them remains unclear. This study aimed to clarify the prevalence and risk factors of LBP in elderly with sarcopenia using the China Health and Retirement Longitudinal Study (CHARLS).

Methods

The CHARLS is an ongoing, nationally representative survey conducted among residents of China from 2008 to 2020. Sarcopenia status was assessed based on physical examination findings, and participants aged 45 years or older who reported experiencing low back pain (LBP) were identified. This national study analysed data from 8,113 participants collected in 2015. One-way ANOVA and Pearson’s chi-squared test were employed to compare LBP prevalence rates. At the same time, a multiple logistic regression model was used to identify factors associated with LBP in individuals with sarcopenia.

Results

The prevalence of LBP was 24.19% (95% CI 22.94–25.44) among individuals with sarcopenia, significantly higher than 16.40% (15.19–17.61) in those without sarcopenia. LBP was more prevalent in females (28.91% [27.13–30.69]) than in males (18.43% [16.74–20.11], p < 0.001 for gender difference). Additionally, individuals with more than four chronic conditions had a significantly higher prevalence of LBP (44.83% [40.87–48.79]) compared to those without chronic conditions (13.02% [11.02–15.02], p < 0.001). Risk factors for LBP in the sarcopenia population included a history of chronic diseases such as heart attack (odds ratio 1.40 [95% CI 1.11–1.77]), kidney disease (1.80 [1.30–2.49]), stomach disease (1.62 [1.35–1.94]), and arthritis or rheumatism (1.76 [1.48–2.10]). Additional significant risk factors were sleeping time of less than 5 h (2.06 [1.36–3.10]), living in the rural area (1.54 [1.20–1.96]), illiteracy (1.64 [1.21–2.22]) and depression (3.16 [2.56–3.89]).

Conclusions

The current study highlighted the high prevalence of LBP in the sarcopenia population. Chronic diseases, sleep time, residence, educational level, depression, and history of falls are major risk factors for LBP in the sarcopenia population.

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Background

Sarcopenia is a progressive and generalised skeletal muscle disorder characterised by age-related declines in muscle mass, strength, and physical performance, which are associated with adverse outcomes such as pain, frailty, functional impairment, and increased mortality [1, 2]. The global prevalence of sarcopenia is estimated to range from 5 to 22% [3]. For older adults (60–94 years), the prevalence rates of possible sarcopenia, sarcopenia, and severe sarcopenia were 38.5%, 18.6%, and 8.0%, respectively [4]. Aside from commonly affecting older adults, sarcopenia can onset in mid-life [1], with a prevalence range of 8–36% [5].

Lower back pain (LBP) is a high-prevalent chronic disease in the aged population [6, 7]. One review, including 165 studies of 54 countries, estimated the point prevalence of LBP to be 11.9% and to be most common in people aged 40–80 years [8]. A 2022 review suggested a positive correlation between sarcopenia and pain [6]. Another study further confirmed that older adults at risk for sarcopenia often suffer from chronic musculoskeletal pain [9]. However, relatively limited evidence is available concerning the prevalence and risk factors of LBP in the sarcopenia population older than 45 years.

Accurately estimating the prevalence of lower back pain (LBP) in the sarcopenia population will aid in the effective prevention of LBP occurrence and reduce exposure to risk factors [8]. Therefore, the early identification of LBP prevalence and high-risk factors is crucial for reducing the risk of LBP in the sarcopenia population. This study aimed to estimate the prevalence of low back pain (LBP) and examine its associated risk factors among individuals aged 45 years or older with sarcopenia, utilising data from the China Health and Retirement Longitudinal Study (CHARLS).

Methods

Study population

This cross-sectional study is based on CHARLS data, an ongoing nationally representative public survey among residents in China from 2008 to 2020. The CHARLS research team collects high-quality data, including sociodemographic characteristics, lifestyle, and health-related factors, through a structured questionnaire with one-to-one interviews. By multistage stratified probability-proportionate-to-size sampling, the CHARLS research team surveyed more than 17,000 participants in about 10,000 households from 450 villages and 150 districts or counties within 28 provinces in China. The data included individual weighting variables to ensure the survey sample was nationally representative. Data collection tools, including questionnaires and physical performance measurements, have demonstrated robust psychometric properties, such as high reliability and validity, as reported in previous studies [10]. CHARLS released five wave data of the survey in 2011 (wave 1), 2013 (wave 2), 2015 (wave 3), 2018 (wave 4), and 2020 (wave 5). The datasets can be downloaded at http://charls.pku.edu.cn/en/index.htm. Permission to use CHARLS data was obtained by completing the required data usage agreement through its official application process.

This study analysed data from the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), which included 21,095 participants. Participants were excluded based on the following criteria: (1) missing information on gender, age, and physical examination data to estimate sarcopenia status (n = 5321); (2) missing data on LBP status for assessing the outcome variable (n = 233); (3) being under the age of 45 does not classify as middle-aged and older adults (n = 681); (4) presence of abnormal or biologically implausible values in the dataset (n = 38); (5) incomplete data on key covariates required for the analysis, including instrumental activities of daily living (ADL), sleep duration, alcohol consumption, residence type, fall history, physical disabilities, education level, insurance coverage, employment status, and other health-relevant variables (n = 6709). This cross-sectional analysis included 8113 participants. Among them, 4255 participants with sarcopenia were identified. The whole process of selection of participants is depicted in Fig. 1.

Fig. 1
figure 1

Flow chart for subjects enrolled in this study. Note: CHARLS, China Health and Retirement Longitudinal Study; HS, handgrip; CTS, chair stand test; GS, gait speed; ASM/Ht2, height-adjusted muscle mass

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Assessment of sarcopenia status

Sarcopenia was assessed based on the criteria of the Asian Working Group for Sarcopenia 2019, composed of three components: muscle mass, muscle strength, and physical performance [11]. Muscle mass is determined by dividing the Appendicular Skeletal Muscle Mass (ASM) by the square of height. ASM was estimated using the following validated anthropometric equation in Chinese residents, which showed a firm consistency with X-ray absorptiometry [12]:

ASM = 0.193 × weight (kg) + 0.107 × height (cm)– 4.157 × gender − 0.037 × age (years)– 2.631.

The cut-off for defining low muscle mass was based on the sex-specific lowest 20% of the height-adjusted muscle mass (ASM/Ht2) among the study population, with < 5.386 kg/m2 in females and < 7.069 kg/m2 in males for this article.

Muscle strength was indicated through handgrip strength (unit: kg). Participants underwent two grip strength tests for each hand while holding the dynamometer at a right angle (90°). The average of the highest values from both hands was used for analysis. If grip strength in one hand could not be measured due to health-related limitations, the value from the other hand was recorded. According to the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, low handgrip strength was defined as less than 28 kg for men and less than 18 kg for women.

Gait speed and a five-time chair stand test (CST) were used to examine physical performance. For gait speed, each participant walked a 2.5 m distance at a normal pace two times (there and back), and the completed time was recorded to calculate the usual gait speed (m/s). The average of all the values was taken. Five-time CST measures the time (unit: s) needed for participants to rise continuously five times, keeping their arms folded across their chest from the height of a 47 cm chair. According to the AWGS 2019 recommendations, low physical performance was defined as a gait speed of < 1.0 m/s or a 5-time CST duration of ≥ 12 s. Participants who attempted but could not complete the walking test or the CST were classified as having low physical performance for analysis purposes.

Participants who did not exhibit low muscle strength, low muscle mass, or low physical performance were classified as having no sarcopenia. Possible sarcopenia was defined as the presence of either low muscle strength or low physical performance. Confirmed sarcopenia was identified as low skeletal muscle mass combined with either low muscle strength or low physical performance. Severe sarcopenia is considered when low muscle strength, low muscle mass, and low physical performance are detected. In this study population, only 192 (2.3%) participants had severe sarcopenia, and they were merged into the confirmed sarcopenia group. Therefore, all participants were divided into three groups: no-sarcopenia (n = 3591), possible sarcopenia (n = 3227), and confirmed sarcopenia (n = 1295).

Outcome measures

The study outcome was LBP events. In the questionnaire, participants were asked, “What part of your body do you feel pain in?” and instructed to list all parts of the body where they felt pain at present. Pain locations included the head, shoulders, arms, wrists, fingers, chest, abdomen, back, waist, buttocks, legs, knees, ankles, toes, neck, and other areas (Supplemental Method). Participants were classified as having low back pain (LBP) if they reported pain in the waist.

Measures of sociodemographic status

For this research, professionals collected all sociodemographic-related variables using questionnaire instruments. Sociodemographic-related variables included age, sex, height, weight, education (illiterate, primary school and less, or middle school and higher), marital status (married with spouse, divorced, or others), residence, work status (currently not working, never worked, or working), income (≤ 1000, 1000–5000, 5000–10,000, 10,000–20,000, or > 20,000 Chinese Yuan), insurance, and area (Central, East, North, South, Southwest, Northeast, or Northwest China).

Age was divided into three groups every 10 years: 45–55, 55–65, and older than 65. According to the National Bureau of Statistics, the area type of their residence was categorised as urban or rural. An urban area is located within the central city zone, the transitional zone between urban and rural areas, or a town centre. Rural areas included the ZhenXiang area, special area, township central, and village. Income was calculated by adding the value of sold produced products, self-employed business income, received pension, and other compensation. Social activities were coded as “yes” or “no” by asking respondents whether they were involved in certain activities, such as surfing the Internet and engaging in community-related organisations. Physical activities were coded as “yes” or “no” by asking about the amount of time participants spent on physical activities in a usual week. ADL involved clothing, showering, meals, toileting, going to and from bed, and bowel and bladder function. IADL involved housekeeping, preparing meals, shopping, using the phone, administering medication, and finances. In this sample, ADL or IADL disability was counted as reported difficulty with any item.

Measures of health-related factors

For this research, professionals collected health factors variables using questionnaire instruments and venous blood samples assayed. Health factors included body mass index (BMI), chronic diseases, physical disabilities, multisite pain, pain treatment (yes or no), sleeping time (extremely short sleep, ≤ 5 h; normal sleep, 6–9 h; or excessive sleep, ≥ 10 h) [9], smoking and drinking status (yes or no), handgrip, walking speed, stand-up test, waist circumference, ASM/H2, health status, depression (10-item Centre for Epidemiologic Studies Depression Scale), ADL, instrumental ADL (IADL), history of fall, physical activity, social activity, child health condition, health satisfaction, life satisfaction, healthcare satisfaction, C-reactive protein (mg/l), and haemoglobin (g/dl).

BMI was divided into three groups: underweight (< 18.5 kg/ m2), normal weight (18.5–24 kg/m2), and overweight or obesity (≥ 24 kg/m2) [4]. If subjects were diagnosed with chronic diseases, such as hypertension, dyslipidaemia, diabetes or hyperglycaemia, cancer, lung diseases, liver diseases, heart attack, stroke, kidney diseases, gastrointestinal diseases, psychiatric disorders, memory-related diseases, arthritis or rheumatism, and asthma, they would report these diagnoses through a questionnaire. Information on falls, fractures, smoking, drinking, physical activity, social activity, insurance, and physical disabilities were all based on self-reports. Each subject used a scale of 1–5 to report their child’s health condition, life satisfaction, and satisfaction with the healthcare situation, with 1 and 5 denoting excellent and inferior, respectively. The current overall health state ratings were then re-stratified into three groups (good, fair, and poor).

Statistical analysis

Descriptive statistics were used to summarise demographic data, presenting categorical variables as frequencies (n) and proportions (%), while continuous variables were described using means with standard deviations. One-way ANOVA and Pearson’s chi-squared test were conducted to compare the prevalence rates of LBP between populations with and without sarcopenia. P for the difference was used to test linear change trends. Univariate and multivariate logistic regression models estimated odds ratios (ORs) and 95% CIs. Univariate analysis was carried out for the initial selection of risk factors at the significance level of p < 0.20 [13]. The variables that showed a significant association between sarcopenia and LBP in the univariate regression were included in the multivariate logistic regression model. P-value < 0.05 was deemed statistically significant. Calculations of prevalence and risk factors were weighted to represent the general adult population aged 45 years or older in China. Data were analysed using R software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria) and Stata software (version 16.0; StataCorp LLC, College Station, TX, USA).

Results

Participants’ demographic characteristics

Table 1 presents the baseline characteristics of the study population in 2015. Among the 8113 participants, the majority were female (4385, 46.0%), more than 65 years old (2805, 34.6%; median 61.3 ± 8.9), and living in rural areas (6252, 77.1%). Based on the AWGS criteria, 3227 (39.8%) and 1295 (16%) participants were diagnosed as possible sarcopenia and confirmed sarcopenia, respectively. Subjects with confirmed sarcopenia were older than those without sarcopenia (69.4 vs. 55.3 years). The majority of individuals with confirmed sarcopenia were unmarried (21.5%) and resided in rural areas (86.4%). Compared to those without sarcopenia, they exhibited significantly poorer physical performance (mean hand grip strength: 23.7 vs. 33.4 kg; walking speed: 0.8 vs. 5.8 s; stand-up test: 10.4 vs. 7.8 s), a higher prevalence of chronic diseases (12.0%), elevated C-reactive protein levels (3.1 vs. 2.3 mg/l), and lower haemoglobin levels (12.7 vs. 13.7 g/dl), all with p < 0.001. The prevalence of LBP was significantly higher than that of other physical pain. Statistical differences among non-sarcopenia, possible sarcopenia, and confirmed sarcopenia groups were found (p < 0.05) in all covariates except sex, cancer, liver disease, treatment for pain, and life satisfaction.

Table 1 Baseline characteristics of participants by sarcopenia status in CHARLS 2015
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Prevalence of LBP

The prevalence of LBP was 16.40% (n = 589, 95% CI [15.19–17.61]) among the no-sarcopenia population, 23.46% (757, [22.00–24.92]) in the possible sarcopenia population, and 26.02% (337, [23.63–28.42]) in the confirmed sarcopenia population. In the sarcopenia population (Table 2), women had a higher prevalence than men (28.91%, 95% CI [27.13–30.69] vs. 18.43 [16.74–20.11]; p < 0.001). The prevalence of LBP increased steadily with the number of chronic diseases from 13.02% (95% CI 11.02–15.02) at no chronic disease to 44.83% (40.87–48.79) at ≥ 4 chronic diseases (p < 0.001). The prevalence of LBP was significantly higher in individuals with sarcopenia accompanied by hypertension, lung disease, liver disease, heart attack, kidney disease, gastrointestinal disorders, psychiatric conditions, and arthritis or rheumatism compared to those without these chronic comorbidities (all p < 0.001). The prevalence of LBP in the sarcopenia group with depression was as high as 53.84%, with 53.66% in the possible sarcopenia group and 54.20% in the confirmed sarcopenia group (p < 0.001). Regarding educational level, LBP was most prevalent in the illiterate population (29.32% [27.01–31.63, p < 0.001). Individuals with a history of falls were more likely to have LBP than those without a fall history (38.32% [35.16–41.47] vs. 20.60 [19.28–21.93], p < 0.001), and people with poor health in childhood had a higher prevalence of LBP (32.29% [27.39–37.20]). Moreover, the prevalence presented a significant decrease with increases in sleeping time, ranging from 34.76% (32.34–37.18, for ≤ 5 h) to 16.86% (12.24–21.49, for ≥ 10 h; p < 0.001).

Table 2 Prevalence of low back pain in the sarcopenia population aged 45 years or older
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Some different patterns were noted in the prevalence between possible and confirmed sarcopenia populations. For chronic diseases, the highest prevalence of LBP for confirmed and possible sarcopenia was found in people with stroke and kidney disease, respectively. In terms of working status, for confirmed sarcopenia, the prevalence of LBP in working people was higher than that in others. In the possible sarcopenia population, those who were currently unemployed had a higher prevalence.

The subgroup analysis (Table S1) found a higher prevalence and percentage distribution in the female population. In possible sarcopenia, LBP was more common in women with memory-related disease (66.67% [47.66–85.67]), who are unmarried (29.38% [24.36–34.39]), illiterate (31.13% [27.89–34.37]), with insurance (29.22% [26.99–31.46]), currently unemployed (29.93% [26.49–33.36]), and living in Southwest China (32.95% [27.21–38.69]). For sarcopenia, the prevalence was common among women with psychiatric problems (57.14% [27.49–86.79]), who are unmarried (37.75% [31.04–44.45]), had higher educational level (30.83% [25.24–36.41]), and living in Southwest China (40.25% [32.55–48.96]). The prevalence of LBP also showed significant differences in the sarcopenia population according to subgroup analyses based on residence and the number of chronic diseases (Table S2–S3).

After weighing and adjusting the data, the weighted prevalence of LBP still showed the same trend as crude prevalence (Table S4). The weighted prevalence by gender, residence, and chronic disease are listed in the Supplemental Material (Table S5–S7).

Related risk factors of LBP

Multivariable logistic regression showed that in the population with sarcopenia, LBP was significantly associated with a diagnosed heart attack (OR 1.40, 95% CI [1.11–1.77], p = 0.005), kidney disease (1.80 [1.30–2.48], p < 0.001), stomach disease (1.62 [1.35–1.94], p < 0.001), arthritis or rheumatism (1.76 [1.48–2.10], p < 0.001), sleeping time of less than 5 h (2.06 [1.36–3.10], p = 0.001), rural residence (1.54 [1.20–1.96], p = 0.001), lower or higher educational level (illiterate: 1.64 [1.21–2.22], p = 0.001; middle school and higher: 1.70 [1.30–2.23], p < 0.001), depression (3.16 [2.56–3.89], p < 0.001), IADL disability (1.69 [1.37–2.09], p < 0.001), and history of fall (1.64 [1.35–2.01], p < 0.001, Fig. 2). For the possible sarcopenia population, LBP was associated with sleeping time of 6–9 h (1.75 [1.06–2.87], p = 0.028) and child health condition (0.58 [0.40–0.84], p = 0.004). Moreover, for the population with confirmed sarcopenia, LBP was associated with ADL disability (1.87 [1.04–3.35], p = 0.036, Table 3). The results of univariate analyses and multivariate logistic regression for the subgroups of the sarcopenia population by gender, residence, and chronic disease are listed in the Appendix (Table S9–S15, Figure S2–S4).

Fig. 2
figure 2

Risk factors associated with low back pain in sarcopenia population. Note: IADL, instrumental activity of daily living; OR, odds ratio; CI, confidence interval

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Table 3 Multivariate analyses for low back pain associated with risk factors in the sarcopenia population aged 45 years or older
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Discussion

Previous research mainly focused on the prevalence of sarcopenia among persons with chronic diseases such as cancer, kidney dysfunction, and metabolic disorders [3]. In this cross-sectional study, the first comprehensive assessment was performed to address the prevalence of chronic pain (LBP) among possible and confirmed sarcopenia groups and explore the associated factors among the older population using the national survey data of CHARLS. The findings showed significant disparities in the prevalence of LBP between the sarcopenia and general populations and indicated that LBP is more common in older adults diagnosed with sarcopenia (24.2%). Regardless of the type of sarcopenia, the prevalence was higher in women, those living in rural areas, and those with chronic diseases. After adjusting for factors, the prevalence of confirmed sarcopenia remained higher than that of possible sarcopenia. This research also identified a range of lifestyle and health-related factors associated with LBP prevalence, such as history of falls, comorbidities, IADL disability, sleeping time, educational level, and residence, for possible and confirmed sarcopenia populations.

A previous study reported that the proportion of individuals with coexisting sarcopenia and pain (6.9%) who exhibited successful ageing was lower than that of people with sarcopenia alone (8.5%) [14]. Underlying physiological mechanisms may explain the higher prevalence and associated risk in the sarcopenia population. As defined by AWGS 2019, sarcopenic muscle undergoes significant cellular and molecular changes that may contribute to the development of LBP. At the cellular level, sarcopenia is characterised by a reduction in the size and number of myofibers, particularly type II fibres, and increased fat infiltration within and between muscles [1]. These changes are accompanied by dysfunction in deep intrinsic muscles, such as the multifidus and rotators, and erector spinal muscles, which can impair spinal mechanics and potentially lead to LBP. Chronic LBP is further associated with muscle atrophy, as evidenced by a reduced cross-sectional area and increased fat infiltration in lumbar paraspinal muscles, particularly the multifidus [8]. On a molecular level, sarcopenic muscle alters signalling pathways, including insulin-like growth factors, endocrine factors induced by muscle contraction, inflammatory cytokines, and oxidative stress-related chronic low-grade inflammation [1, 15,16,17]. These pathways may contribute to pathological inflammatory responses linked to LBP [7, 8]. While these findings highlight a potential connection between sarcopenic muscle degeneration and LBP, the precise mechanisms remain poorly understood, necessitating further research to clarify their interplay.

This study showed that the LBP prevalence varied across the four major regions in China, with the highest prevalence of 29.2% in the Northwest areas such as Xinjiang, Qinghai, and Gansu. However, two relatively large-sample studies in 2016 showed that the highest age-standardized point prevalence of LBP was in North China (Beijing) at 6.2% [18]. The present study found that the prevalence of LBP in rural areas (26.02%) was significantly higher than that in urban areas (17.54%, p < 0.001). However, previous data for the global population showed no difference in the prevalence of LBP between the two regions [7]. An earlier study reported that age between 40 and 80 contributes the most to the prevalence of LBP [19], which increases with age [8]. In the present study, the age-related mechanisms with LBP in sarcopenia were complex and challenging to discern fully. The prevalence was highest among 45–55 years of age, showing the characteristics of first decreasing and then increasing with the increase in age. First, their social expectations to continue working at 45–55 years of age could increase LBP’s burden [20]. Second, the morbidity and mortality rates of sarcopenia increase with age, so the survival rate of persons with LBP in the older population is an essential factor affecting the morbidity results. However, considering that no weighted database on the demographics of sarcopenia in China is available, more research in this area is needed. The sex-specific analysis found that the prevalence in females was higher than in males in this study, similar to the results of other previously published studies, which showed age-standardized point prevalence to be most common among middle-aged to older women [8].

The results showed some modifiable risk factors for LBP in sarcopenia, including a history of falls, having comorbidities and depression, ADL and IADL disability, less sleeping time, low educational level, and living in rural areas, which is similar to other previously published studies [21]. For middle-aged and older adults, a study has suggested that multimorbidity, such as rheumatoid arthritis and osteoporosis, is correlated with the presence of sarcopenia [22]. In the present study, chronic diseases, such as kidney, gastrointestinal, and arthritis or rheumatism, demonstrated an increased association with LBP. These comorbidities were closely related to changes in abdominal pressure and the release of joint inflammatory factors, which may increase the risk of LBP. At a psychological level, numerous studies have found that sarcopenia increases the risk of depression [23], which is also significantly associated with the occurrence of LBP in people with sarcopenia. LBP may result from depression-mediated susceptibility to dysphoric physical and psychological symptoms, which are related features of a vulnerability to heightened response to physical and/or psychological stressors [24]. The present research found that sleep is a significant risk factor. People who slept less than five hours had almost two times the prevalence of LBP as those with 10 h of sleeping time in the possible sarcopenia group. Previous research has reported a significant association between short sleep duration and high sarcopenia prevalence in older adults [25]. Longer sleep may provide more spinal support and muscle relaxation for people with sarcopenia, and the prevalence of LBP may have resulted from overuse and frailty in sarcopenia. Similar to previous studies [26], the present study found that rural regions had a higher prevalence rate of possible sarcopenia than urban regions. This finding may be explained by the fact that socioeconomic development in urban areas may contribute to better public health with more timely and comprehensive intervention care. However, many rural older adults may suffer from heavy physical labour due to lower educational levels, which, in turn, may increase the risk of injury. Besides, higher academic qualification is considered an independent risk factor for possible sarcopenia due to sedentary and unhealthy lifestyles. A protective factor of fair child health condition was found to be related to possible sarcopenia, which may be due to increased concern and health protection since childhood, indicating that earlier intervention makes their health in old age more secure. A comparison of the risk factors of LBP in confirmed sarcopenia and possible sarcopenia groups showed that LBP is mainly related to diseases and lifestyle. Early prevention of the above modifiable risk factors may effectively reduce the occurrence of LBP in individuals with sarcopenia.

This study has strengths and significant implications for public health and health policies. First, this survey used a large sample size and a nationally representative database (CHARLS) under stringent quality control to investigate LBP’s prevalence and risk factors in Chinese older adults with sarcopenia. Second, the results were stratified by sex, residence, and number of comorbidities. These adjustments are essential for developing LBP prevention in the sarcopenia population and control strategies across China.

This research has some limitations. First, the study participants were limited to individuals who underwent health check-ups in CHARLS 2015. Therefore, the prevalence of LBP in this study may be underestimated in older adults because of subject bias. Second, individuals with severe health disorders may have been less likely to participate in the CHARLS study, thus potentially limiting the generalizability of the findings. However, because of the large sample size and diverse population characteristics, the effects on the generalizability of the findings could be minimal. Third, considering that that is a cross-sectional study, a causal relationship of the identified associated factors could not be established, and future studies are needed to evaluate their predictive ability related to the outcomes. Fourth, the previous history of LBP was unknown, so all cases were determined as the first occurrence after referencing the 2013 data. Finally, due to the limitations in collecting sociodemographic and economic indicators within the database, only income was retained as the covariate for wealth assessment. While it aligns with the focus of our analysis, future studies could focus on incorporating more comprehensive individual wealth information for analysis.

Conclusions

This cross-sectional survey showed that LBP was higher in the sarcopenia population than in the general population, especially females, rural residents, and those with chronic disease. For the possible sarcopenia and confirmed sarcopenia groups, the significantly related risk factors included kidney disease, gastrointestinal disease, arthritis or rheumatism, depression, IADL disability, and a history of falls.

Data availability

Data will be made available on request. The datasets can be downloaded at http://charls.pku.edu.cn/en/index.htm.

Abbreviations

CHARLS:

The China health and retirement longitudinal study

SD:

Standard deviation

CI:

Confidence interval

OR:

Odds ratio

ASM:

Appendicular skeletal muscle

H:

Height

BMI:

Body mass index

ADL:

Activities of daily living

IADL:

Instrumental activity of daily living

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Acknowledgements

This study is conducted under the China Health and Retirement Longitudinal Study (CHARLS) Resource. We want to express our sincere gratitude to all associated researchers of CHARLS. We thank all the participants and researchers involved in the publications cited in this manuscript and peer reviewers who contributed to the continuous improvement of this study.

Funding

This work was supported by the National Natural Science Foundation of China (grant number 82372578).

Author information

Author notes

  1. Wen-Yuan Xing and Le Tang contributed equally to this work.

Authors and Affiliations

  1. Rehabilitation Medicine Center, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China

    Wen-Yuan Xing, Le Tang, Ya-Nan Zheng, Yi-Wen Bai, Xue Jiang & Xue-Qiang Wang

  2. School of Rehabilitation Medicine, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China

    Wen-Yuan Xing, Le Tang, Ya-Nan Zheng, Yi-Wen Bai, Xue Jiang & Xue-Qiang Wang

  3. Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, 200438, China

    Wen-Yuan Xing, Le Tang, Ya-Nan Zheng, Yi-Wen Bai & Xue Jiang

  4. Rehabilitation Medicine Center, Shanghai Pudong New District Zhoupu Hospital, Shanghai, 201318, China

    Xia Bi

  5. College of Rehabilitation Sciences, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China

    Xia Bi

Authors
  1. Wen-Yuan Xing
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  2. Le Tang
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Contributions

Conception, X.-Q.W.; interpretation of data and drafted the work, W.-Y.X. and L.T.; interpretation of data, W.-Y.X., L.T. and Y.-W.B.; writing—review and editing, X.J. and Y.-N.Z.; supervision, X.B and X.-Q.W. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Xue-Qiang Wang.

Ethics declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki, and the CHARLS project obtained ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). All participants provided informed consent before participating in the survey.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Supplementary Material 1

: eTable 1: Prevalence of low back pain in the sarcopenia population aged 45 years or older by gender. eTable 2: Prevalence of low back pain in the sarcopenia population aged 45 years or older by residence. eTable 3: Prevalence of low back pain in the sarcopenia population aged 45 years or older by the number of chronic diseases. eTable 4: Weighted prevalence of low back pain in the sarcopenia population aged 45 years or older. eTable 5: Weighted prevalence of low back pain in the sarcopenia population aged 45 years or older by gender. eTable 6: Weighted prevalence of low back pain in the sarcopenia population aged 45 years or older by residence. eTable 7: Weighted prevalence of low back pain in the sarcopenia population aged 45 years or older by the number of chronic diseases. eTable 8: Univariate and multivariate analyses for low back pain associated with risk factors in the sarcopenia population aged 45 years or older. eTable 9: Univariate analyses for low back pain associated with risk factors in the sarcopenia population aged 45 years or older. eTable 10: Multivariate analyses for low back pain associated with risk factors in the possible sarcopenia population aged 45 years or older by gender. eTable 11: Multivariate analyses for low back pain associated with risk factors in the confirmed sarcopenia population aged 45 years or older by gender. eTable 12: Multivariate analyses for low back pain associated with risk factors in the possible sarcopenia population aged 45 years or older by residence. eTable 13: Multivariate analyses for low back pain associated with risk factors in the confirmed sarcopenia population aged 45 years or older by residence. eTable 14: Multivariate analyses for low back pain associated with risk factors in the possible sarcopenia population aged 45 years or older by the number of chronic diseases. eTable 15: Multivariate analyses for low back pain associated with risk factors in the confirmed sarcopenia population aged 45 years or older by the number of chronic diseases. eFigure 1: Risk factors associated with low back pain in the sarcopenia population by gender.eFigure 2: Risk factors associated with low back pain in the sarcopenia population by residence. eFigure 3: Risk factors associated with low back pain in the sarcopenia population by the number of chronic diseases

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Xing, WY., Tang, L., Zheng, YN. et al. Prevalence and risk factors of lower back pain in middle-aged and elderly people with sarcopenia: a nationwide cross-sectional study. BMC Public Health 25, 1517 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22723-2

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BMC Public Health

ISSN: 1471-2458

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