Introduction
The global aging population has precipitated a steady rise in age-related neurodegenerative conditions, with dementia and cognitive impairment representing particularly pressing public health challenges. China currently bears the world’s largest dementia burden and, given demographic projections, may account for nearly 50% of global cases by 2050.1,2 Within this context, motoric cognitive risk (MCR) syndrome has emerged as a critical predementia marker, operationally defined by coexisting subjective cognitive complaints and objectively measured gait slowing in older adults without dementia.3 MCR demonstrates significant predictive validity for multiple adverse outcomes including incident falls,4 functional disability,5 dementia,5 cardiovascular events,6 and premature mortality.7 Identifying modifiable risk factors for MCR could inform targeted prevention strategies, potentially mitigating both the personal suffering and substantial socioeconomic costs associated with dementia progression.
Pain represents a complex multidimensional experience encompassing both sensory and affective components, classically defined as an unpleasant sensation associated with actual or potential tissue damage.8 This prevalent health concern affects a substantial proportion of adults globally, with many experiencing persistent or chronic manifestations.9 In the Chinese context, pain-related conditions generate annual healthcare expenditures exceeding ¥1 trillion,10 while simultaneously contributing to substantial disease burden through increased disability-adjusted life years (DALYs). Beyond its economic impact, chronic pain frequently precipitates numerous adverse health outcomes including mobility limitations, anxiety disorders, and depressive symptoms.11 Studies have demonstrated that chronic pain may intensify cognitive decline, potentially contributing to the development of conditions such as MCR. However, the pathophysiological mechanisms linking pain to the progression of MCR are not well understood, necessitating further mechanistic research.
Handgrip strength (HGS) serves as a robust objective measure of overall physical capability and a validated predictor of current and future health status.12 Emerging evidence indicates that chronic pain may contribute to accelerated biological aging processes, potentially compromising musculoskeletal integrity and reducing muscular strength.13 Mechanistic evidence suggests that chronic pain may precede declines in physical capacity. Specifically, persistent nociceptive signaling can induce neuromuscular inhibition and disuse atrophy, leading to reduced muscle strength including HGS.13,14 Concurrently, pro-inflammatory states associated with chronic pain may accelerate sarcopenia, further compromising HGS.15 Epidemiological studies consistently demonstrate an inverse relationship between pain prevalence and HGS in middle-aged and older populations.16 Notably, diminished HGS shows significant associations with both functional disability and cognitive impairment.17,18 This relationship is particularly evident in longitudinal research involving Chinese elderly populations, where greater baseline HGS predicted slower rates of cognitive decline.19 From a neurophysiological perspective, HGS reflects integrated nervous and motor system function,20 and the deterioration of these systems represents a key pathway in age-related cognitive deterioration.21 Cognitive function is vital to MCR, but limited research on HGS and MCR leaves their connection unclear, necessitating further study. Given that HGS is a robust biomarker of neuromuscular integrity and cerebral health,20,22 it may constitute a critical pathway linking pain to cognitive-motor deficits in MCR. Thus, we hypothesize that HGS mediates the association between pain and MCR.
Utilizing data obtained from the 2015 China Health and Retirement Longitudinal Study (CHARLS), this research investigated the relationship between pain, HGS, and MCR in individuals aged 45 years and older. A mediation analysis framework was employed to evaluate the potential mediating role of HGS in the association between pain and MCR.
Study Methodology
Data Collection
The present analysis employed a cross-sectional design, leveraging data sourced from the CHARLS. Conducted as a longitudinal, nationally representative survey targeting Chinese adults ≥ 45 years older, CHARLS was designed to create a comprehensive, publicly accessible micro-level dataset that is both reliable and representative of middle-aged and elderly populations. A multistage probability-proportional-to-size (PPS) sampling strategy was employed across 28 provincial-level regions to ensure demographic diversity. Comprehensive methodological details regarding CHARLS have been extensively documented in previous research.23 The CHARLS study was approved by the Ethics Review Committee of Peking University (approval number: IRB00001052-11015). All methods were carried out in accordance with relevant guidelines and regulations, and all participants signed informed consent forms when participating. The inaugural national data collection occurred in 2015 with an initial cohort of 21,095 respondents. For analytical rigor, exclusion criteria were applied sequentially: individuals below 45 years (n=7,105), those with incomplete MCR assessments (n=8,929), undocumented pain (n=129), unspecified HGS data (n=66), and missing covariate information (n=71) were omitted. This selection process yielded a final analytical cohort of 4,795 middle-aged and elderly participants, with the complete exclusion pathway illustrated in Figure 1.
Figure 1 Flowchart of the participants selection process.
Abbreviations: CHARLS, China health and retirement longitudinal study; MCR, Motoric cognitive risk syndrome.
Assessments
MCR
We employed the established diagnostic framework for MCR, which requires the concurrent presence of: (1) subjective cognitive complaints and (2) objectively measured slow gait speed (GS), in individuals without dementia.24 Dementia identification is detailed in the Supplementary Methods.
Subjective cognitive complaints were evaluated through a standardized single-item question: “How would you describe your current memory function?” Participants responded using a 5-point ordinal scale:
Excellent
Very good
Good
Fair
Poor
Responders selecting “Fair” (4) or “Poor” (5) were operationally classified as exhibiting clinically significant cognitive complaints.
GS was objectively measured using infrared sensor technology during a 2.5-meter walk at self-selected usual pace. Three trials were conducted with adequate rest periods between measurements. Slow GS was defined using population-based normative data, with the following age- and sex-specific thresholds [>1 standard deviation (SD) below mean values]:25
Male participants:
Aged <75 years: ≤0.44 m/s
Aged ≥75 years: ≤0.35 m/s
Female participants:
Aged <75 years: ≤0.41 m/s
Aged ≥75 years: ≤0.33 m/s
Pain
A self-report questionnaire was administered by the CHARLS investigators in each wave of data collection to obtain information regarding body pain. The questionnaire commenced with the inquiry, “Are you often troubled by any pain in your body?” Based on the participants’ responses, pain was categorized as a binary variable (yes/no). Pain locations were identified across 15 specific sites and subsequently grouped into three primary regions: head and neck; trunk (encompassing the chest, stomach, back, waist, or buttocks); and limbs (including shoulders, arms, wrists, fingers, legs, knees, ankles, or toes). Pain data for this study were obtained from CHARLS 2015.
HGS
HGS was assessed during baseline evaluations utilizing a hydraulic grip strength dynamometer. Each participant underwent two consecutive measurements for both the left and right hands. The highest value recorded across all four trials was selected as the continuous variable for subsequent analysis.26,27
Control Variables
The baseline investigation examined a range of demographic and health-related variables. These encompassed age, sex, geographic residency (urban or rural), and marital status—classified as married and living with a spouse or married but living without a spouse or single, divorced, and windowed. Additional covariates included the count of chronic conditions (none, single, or multiple), smoking status (smoker or non-smoker), drinking status (non-drinker, drink but less than once a month, or drink more than once a month), highest educational attainment (elementary school or below or middle school or above), body mass index (BMI), and sleep quality (good, fair or poor). Chronic disease prevalence was ascertained through self-reported diagnoses of fourteen non-communicable conditions: hypertension, diabetes, dyslipidemia, chronic pulmonary disease, hepatic disorders, renal disease, cardiovascular events (myocardial infarction, stroke), malignancies, arthritis, asthma, gastrointestinal ailments, cognitive impairment, mental health conditions, and musculoskeletal disorders.
Statistical Analysis
Sample characteristics were summarized with means and SD for continuous variables and frequencies with percentages for categorical variables. Secondly, we used Spearman correlation to evaluate the relationships between the main variables. The study used the Baron and Kenny mediation model.28 The analytical framework evaluated the intermediary effect of HGS measured at baseline in the association between pain and MCR. Linear regression analyses were conducted to: (1) explore the association between pain and HGS, (2) examine the link between pain and MCR, and (3) investigate the pain-MCR relationship with HGS as a mediator. The odds ratio (OR) quantified the association’s magnitude between variables. We utilized a nonparametric bootstrap approach with 1000 resamples to assess both the total and indirect effects.29 Stratified subgroup analyses explored effect heterogeneity across predefined demographic and health-related categories. Covariates accounted for in the analyses encompassed age, sex, educational attainment, marital status, geographic residence, smoking status, drinking status, sleep quality, BMI, and the number of diagnosed chronic diseases. Statistical significance for mediation effects was determined using bias-corrected accelerated bootstrap 95% CI, with effects deemed significant if the interval excluded zero. All statistical analyses were performed with R (v4.3.2), using a significance level of p < 0.05.
Result
Study Participants’ Baseline Traits
The study cohort was divided into non-MCR and MCR subgroups, with their demographic and clinical characteristics detailed in Table 1. The analysis included 4,795 participants, with an average age of 67.3 ± 6.5 years. Of these, 2,416 were female (50.4%) and 2,379 were male (49.6%). There were 4,648 in the non-MCR group and 147 in the MCR group. The mean age of non-MCR group was 67.2 ± 6.4 years, and 2,322 (50%) were women and 2,326 (50%) were men. The mean age of MCR group was 71.5 ± 8.1 years. There were 94 females (63.9%) and 53 males (36.1%). A significant proportion of participants lived in rural areas (62.8%) and were married and cohabiting with their spouses (78.9%). Significant variations emerged across demographic and health-related variables—including age, gender distribution, educational attainment, marital status, alcohol intake, and chronic disease prevalence—with statistical significance maintained across all measures (p < 0.01). Notably, in the non-MCR group, 32.67 participants (70.3%) had no pain, 221 (4.8%) had one pain, and 1215 (25.3%) had two or more pain. In the MCR group, 75 participants (51%) had no pain, 15 (10.2%) had one pain, and 57 (38.8%) had two or more pain. (p < 0.001). Similarly, the mean HGS in the non-MCR group was 29.1kg ± 9.0 kg, which significantly differed from the mean HGS of 21.7 kg ± 8.4 kg in the MCR group (p < 0.001).
Table 1 Baseline Characteristics of the Study Participants with and Without Motoric Cognitive Risk Syndrome
Associations of Pain and HGS with MCR
Table 2 displays associations between pain, HGS, and MCR. The analysis examined the association of pain with MCR across 4,795 participants. Unadjusted analyses showed a statistically significant positive association between pain at one site and MCR, with an OR of 2.96 (95% CI: 1.67–5.24; P < 0.001). After adjusting for covariates in Model 1, the effect size attenuated slightly (OR = 2.71, 95% CI: 1.51–4.84; p = 0.001). Further adjustments in Model 2 (OR = 2.81, 95% CI: 1.56–5.06; p = 0.001) and Model 3 (OR = 2.76, 95% CI: 1.53–4.99; p = 0.001). Unadjusted analyses showed a significant link between pain at multiple sites and MCR (OR = 2.15; 95% CI: 1.51–3.05; P < 0.001). Adjustments in Models 1, 2, and 3 slightly reduced the effect size but maintained significance: Model 1 (OR = 2.02; 95% CI: 1.40–2.91; P < 0.001), Model 2 (OR = 2.08; 95% CI: 1.42–3.04; P < 0.001), and Model 3 (OR = 1.90; 95% CI: 1.29–2.80; P = 0.001). This consistency indicates that, despite the adjustment for covariates, pain remains positively associated with MCR.
Table 2 Associations of Pain and Handgrip Strength with Motoric Cognitive Risk Syndrome
HGS demonstrated a significant inverse association with MCR in the analysis (OR = 0.90, 95% CI: 0.88–0.92; p < 0.001). This relationship retained statistical significance across sequential adjustments: Model 1 (OR = 0.90, 95% CI: 0.88–0.92; p < 0.001), Model 2 (OR = 0.90, 95% CI: 0.88–0.93; p < 0.001), and Model 3 (OR = 0.91, 95% CI: 0.88–0.93; p < 0.004). After adjusting for covariates, the robustness of the negative association between HGS and MCR persisted.
Table S1 presents the results of a subgroup analysis examining the relationship between pain and MCR, demonstrating consistent associations across all subgroups (interaction P>0.05). Table 3 highlights that sleep quality significantly affects the link between HGS and MCR (interaction P=0.028).
Table 3 Subgroup Analysis of the Association Between Handgrip Strength and Motoric Cognitive Risk Syndrome
HGS Mediated the Association Between Pain and MCR
Table 4 illustrates the associations among baseline pain, HGS, and MCR. Pain showed a positive association with MCR (r = 0.07, p < 0.001) and a negative association with HGS (r = −0.21, p < 0.001). Notably, HGS and MCR were inversely associated (r = −0.14, p < 0.001).
Table 4 Association Among Pain and Handgrip Strength with Motoric Cognitive Risk Syndrome
Bootstrap analysis revealed the total effect of baseline pain on MCR (β₀ =1.04 × 10−2, p= 0.002). HGS was found to significantly mediate the relationship between pain and MCR, with a mediation effect size of 2.58 × 10−3 (p < 0.001). This mediating pathway accounted for 24.87% of the total effect variation. A visual representation of this mediation pathway is provided in Figure 2.
Figure 2 The conceptional framework of the mediation models. β0 was the total effect of number of pain on MCR; β1 represents the effect of number of pain on handgrip strength; β2 represents the effect of handgrip strength on MCR. The mediation effect was computed as the product of “β1” and “β2”(β1 × β2), and the mediation proportion was calculated as the ratio of the mediation effect product to total effects [(β1 × β2)/β0].
Abbreviation: MCR, Motoric cognitive risk syndrome.
Discussion
This study is the first to examine the link between pain, HGS, and MCR in middle-aged and older Chinese using 2015 CHARLS data. The study revealed a positive association between pain and MCR, while HGS exhibited a negative association with MCR. Furthermore, HGS was found to mediate the relationship between pain and MCR. Sleep quality also significantly influenced the HGS-MCR relationship, supporting our hypothesis.
MCR is a pre-dementia syndrome marked by subjective cognitive complaints and slow GS, suggesting a higher risk of cognitive decline and dementia. Pain is intricately associated with MCR, and the nature of this association is direct, indirect, or mediated by other risk factors. Controlling for confounders, each unit increase in pain raised the MCR by 1.9 times (OR = 1.90, 95% CI: 1.29–2.80; p = 0.001). Chronic pain, marked by ongoing discomfort, can impair attention, memory, and executive function, leading to cognitive dysfunction.30 It also slows cognitive processing in middle-aged and older adults, highlighting its impact on cognition.31 Moreover, the presence of pain can exacerbate other risk factors for MCR, such as depression and sleep disturbances. Depression is a well-documented risk factor for cognitive decline, and its prevalence is higher among individuals experiencing chronic pain. This comorbidity can lead to a more pronounced decline in cognitive functions, as both conditions independently contribute to cognitive impairment.32,33 Pain can reduce physical activity, essential for cognitive health. Chronic pain often leads to sedentary behavior, a changeable risk factor for cognitive decline and MCR. Promoting exercise and overcoming pain-related obstacles may help lower this risk.34,35 A prior meta-analysis found that pain was linked to decreased GS in older adults.36 Pain causes compensatory changes in walking patterns, such as shorter strides and increased support time for both limbs, which can lead to slower GS.
The relationship between pain and HGS in middle-aged and older individuals is a multifaceted issue that can be influenced by various physiological and psychological factors. Studies have shown that chronic pain in the older people can lead to muscle disuse and atrophy, which in turn reduces muscle strength, including HGS.14 Inflammation is another potential mechanism linking pain and HGS, which negatively affects muscle function. Inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6) are associated with muscle weakness and reduced HGS in the older population.14,15 Furthermore, chronic pain can induce changes in neuromuscular function, resulting in diminished motor control and coordination, which subsequently reduces muscle efficiency and HGS.37 Psychological factors like depression and anxiety, often seen in chronic pain patients, can impact HGS. Depression is linked to decreased physical activity and muscle strength, including HGS, in older adults. This connection might be two-way, as lower HGS can also increase the risk of depression.38,39
Mediation analyses revealed that HGS partially mediated the association between pain and MCR. Following comprehensive adjustment for covariates, each 1-unit elevation in HGS corresponded to a 9% decline in MCR (OR=0.91, 95% CI: 0.88–0.93; p < 0.001), underscoring an inverse relationship between HGS and MCR. Extensive empirical evidence suggests that HGS may serve as a robust predictor of MCR. Muscle weakness as indicated by reduced HGS may lead to decreased physical activity and exacerbate cognitive decline through decreased cerebral blood flow and neuroplasticity.40 In addition, lower HGS is linked to higher cardiovascular risk markers like hypertension and inflammation, which can reduce cerebral perfusion and increase the risk of cerebrovascular events, potentially leading to cognitive decline and MCR.41 Furthermore, HGS is linked to brain structure and function, with stronger HGS correlating to more grey matter in cognitive areas like the temporal cortices. This may indicate better neural health and cognitive reserve, possibly guarding against MCR.22
Our study has several strengths. Firstly, the analysis leverages a prospective, nationally representative cohort dataset from China, strengthening the validity and broader applicability of the findings. Secondly, this investigation represents the inaugural effort to systematically examine the relationships between pain, HGS, and MCR within middle-aged and older populations. Finally, the potential mediator HGS was evaluated, which further supports the mechanistic framework and provides a strong rationale for preventing and improving MCR. Concurrently, this study acknowledges several limitations. Firstly, the study’s focus on middle-aged and older adults within the cultural and demographic setting of China constrains the extrapolation of results to younger demographics or other ethnicities/cultural contexts. Secondly, the cross-sectional survey design inherently restricts causal inference, as temporal sequencing between exposures (eg, pain, HGS) and MCR outcomes cannot be established, leaving mechanistic pathways unresolved. The association between pain, HGS, and MCR remains contentious, and it is not feasible to ascertain whether a bidirectional relationship exists among these variables. Thirdly, unmeasured confounders (eg, dementia, chronic inflammation, and physical activity) may have biased the association and influenced the magnitude of the mediating proportion. Lastly, the dependence on self-reported questionnaires for MCR (cognitive complaints) introduces the potential for information bias in our results. The operationalization of subjective cognitive complaints was based on a single-item question, which cannot distinguish between stable low memory perception and genuine subjective cognitive decline reflecting progressive deterioration. It may misclassify individuals with lifelong lower memory confidence as having memory impairment.
Conclusion
The study revealed a significant positive association between pain and MCR and a significant negative association between HGS and MCR. Mediation analysis identified HGS as a partial mediator in the pathway linking pain and MCR. These findings hold critical implications for healthcare policy and public health interventions targeting the enhancement of well-being among aging populations vulnerable to MCR and dementia.
Data Sharing Statement
This study analyzed publicly available datasets found at http://charls.pku.edu.cn/en.
Ethics Approval and Consent to Participate
The Ethics committee of Bengbu Medical University follows the Declaration of Helsinki and the international ethical standards for human health research, and performs the responsibility of independent ethical assessment. This study used publicly accessible data that were legally obtained and met the criteria for exemption from review outlined in the Ethical Review Protocol for Life Science and Medical Research Involving Human Subjects.
Acknowledgments
This study uses CHARLS data, with gratitude to the CHARLS team and participants for their contributions.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
This study received joint funding from the Philosophy and Social Sciences Foundation of Anhui Higher Education Institutions, China (Grant Nos. 2024AH052821 and 2024AH052823).
Disclosure
The authors have declared that they have no conflicts of interest.
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