Correlation of mild cognitive impairment with locomotive syndrome in geriatric cancer patients: A cross-sectional study

Abstract

BACKGROUND

Locomotive syndrome (LS), a criterion capable of evaluating physical function at an earlier stage, has been less studied in relation to mild cognitive impairment (MCI). Clarifying the correlation between LS status and MCI in geriatric cancer patients may aid in identifying early risks for cognitive and motor impairments, providing new insights into maintaining patient independence.

AIM

To explore risk factors and the correlation between MCI and LS in geriatric cancer patients.

METHODS

A total of 467 geriatric cancer patients admitted to our hospital from July 2024 to June 2025 were enrolled. MCI was assessed using the Mini Mental State Examination, while locomotive function was evaluated using the Geriatric Locomotive Function Scale-25. Univariate analysis was conducted to evaluate differences in MCI and LS among geriatric cancer patients with different clinical characteristics. Logistic regression was performed to identify independent risk factors. Spearman correlation analysis was employed to assess the relationship between MCI and LS.

RESULTS

The prevalence of LS was 58.0%, and that of MCI was 30.5%. Logistic regression analysis indicated that age, number of chronic comorbidities, educational level, and MCI were independent risk factors for LS. Age, number of chronic comorbidities, and LS were risk factors for MCI. Spearman correlation analysis revealed a negative correlation between Geriatric Locomotive Function Scale-25 and Mini Mental State Examination scores (r = -0.436, P < 0.001).

CONCLUSION

A significant correlation exists between MCI and LS in geriatric cancer patients. Clinical management and nursing care should concurrently address cognitive impairment and LS to improve patients’ overall quality of life and prognosis.

Key Words: Geriatric cancer patients; Locomotive syndrome; Cognitive impairment; Musculoskeletal function; Correlation

Core Tip: Locomotive syndrome (LS), a criterion capable of evaluating physical function at an earlier stage, has been less studied in relation to mild cognitive impairment (MCI). Clarifying the correlation between LS status and MCI in geriatric cancer patients may aid in identifying early risks for cognitive and motor impairments, providing new insights into maintaining patient independence. Therefore, this study aimed to explore influencing factors and correlations between MCI and LS in geriatric cancer patients. Identifying these factors may facilitate targeted interventions, thus improving overall health status and prognosis among geriatric cancer patients.

INTRODUCTION

The 2024 National Cancer Report released by the National Cancer Center of China indicated that, in 2022, there were 4.8247 million new cancer cases and 2.5742 million new cancer-related deaths in China[1]. Malignant tumor incidence began to rise significantly in the 35-39 age group, reaching a peak among those aged 80-84 years. Mortality rates increased markedly from the 40-44 age group, peaking in individuals aged 85 years and older[1]. By 2035, it is projected that 60% of newly diagnosed cancer patients worldwide will be individuals aged 65 years or older. Malignant tumors have become a major public health issue, seriously threatening the health and life expectancy of the elderly population[1]. Currently, treatment goals for geriatric cancer patients include not only improving prognosis but also enhancing quality of life (QOL)[2]. A critical aspect of improving QOL involves maintaining and improving musculoskeletal function (MF), enabling patients to perform basic abilities of daily living (ADL)[3].

Locomotive syndrome (LS) is a high-risk condition characterized by difficulty standing, walking, and performing other movements due to weakening or impairment of locomotive organs (bones, joints, muscles, and nerves). LS can lead to restricted mobility, increased need for care, and potentially bedridden status[4]. Research indicates that LS is closely associated with adverse outcomes, including reduced ADL, fractures, and increased mortality[5]. Compared to specific conditions such as sarcopenia and frailty, LS more comprehensively assesses overall MF[6]. In clinical practice and research, LS is commonly assessed using the Geriatric Locomotive Function Scale (GLFS-25), a comprehensive tool that evaluates pain, activities of daily living, social functioning, and mental health[7]. Degeneration of musculoskeletal and other motor organs typically occurs slowly and is difficult to detect early. In geriatric cancer patients, LS may have already advanced before the onset of sarcopenia or frailty[8]. Therefore, some scholars propose that LS may serve as a more sensitive indicator for identifying MF disorders. Early prevention and timely intervention for LS are essential for maintaining ADL and QOL[8]. Additionally, geriatric cancer patients constitute a population at high risk for lymphedema.

Mild cognitive impairment (MCI) is a cognitive state intermediate between normal aging and dementia, representing an early stage of dementia[9]. Although initially presenting only mild memory or cognitive impairment, untreated MCI can progress to irreversible dementia within a few years. Thus, MCI represents a crucial stage for dementia prevention[10]. The Mini-Mental State Examination (MMSE) is one of the most widely used screening tools for detecting MCI, providing a quick assessment of orientation, memory, attention, and language functions, and has demonstrated good reliability among cancer patients[11]. Frailty, an important indicator of physical function, is closely associated with MCI[12]. Frail older adults are 1.6 times to 2.5 times more likely to develop MCI compared to non-frail older adults[13]. Frail older adults with MCI also experience higher rehospitalization rates, disability, and mortality, severely impacting their physical and mental health, reducing QOL, and placing significant caregiving burdens on families and society[14].

However, LS status, a criterion capable of evaluating physical function at an earlier stage, has been less studied in relation to MCI. To address this research gap, we conducted a single-center cross-sectional study as an initial exploration to clarify the potential association between LS status and MCI in geriatric cancer patients. Clarifying the correlation between LS status and MCI in geriatric cancer patients may aid in identifying early risks for cognitive and motor impairments, providing new insights into maintaining patient independence. Specifically, we employed the GLFS-25 and MMSE to quantify LS and MCI, respectively. Identifying these factors may facilitate targeted interventions, thus improving overall health status and prognosis among geriatric cancer patients.

MATERIALS AND METHODS

Subjects and methods

This study is a single-center cross-sectional survey. The study protocol was approved by the Ethics Committee of the Affiliated Hospital of Jiangnan University, No. LS2023101 and registered in the Chinese Clinical Trial Registry, No. ChiCTR2400079958 on January 17, 2024. The study was conducted in accordance with the Declaration of Helsinki. Before participation, all eligible patients were fully informed of the study objectives, procedures, potential benefits, and possible risks. Written informed consent was obtained from each participant prior to enrolment. To ensure ethical compliance and protect participant privacy, all collected data were anonymized by removing personal identifiers. The anonymized datasets were stored in a password-protected database accessible only to the principal investigators and used exclusively for research purposes.

Subjects

In this study, elderly individuals were defined as those aged 60 years and above, in accordance with the official aging standard commonly applied in China by the National Bureau of Statistics and the National Health Commission[15]. Considering the increasingly earlier onset of malignant tumors in recent years[1], the inclusion of individuals aged 60-64 years allows for the identification of early functional and cognitive decline, thereby providing a more comprehensive understanding of locomotive and cognitive changes among older adults with cancer in China. Using convenience sampling, geriatric cancer patients aged ≥ 60 years who were admitted to the Cancer Center of the Affiliated Hospital of Jiangnan University from July 2024 to June 2025 were enrolled.

Inclusion criteria: (1) Age ≥ 60 years; (2) Confirmed diagnosis of malignant tumor according to the 8^th edition of the international tumor-node-metastasis staging system; (3) Able to independently perform ADL, with stable vital signs and clinical condition; (4) No history of musculoskeletal disorders due to primary orthopedic conditions; and (5) Provided informed consent and voluntarily participated.

Exclusion criteria: (1) Recent history of clearly diagnosed stroke affecting cognitive testing; and (2) Inability to provide informed consent or to complete the questionnaires due to compromised cognitive or conscious state.

Sample size

The primary scales used in this study (GLFS-25 and MMSE) contain a total of 37 variables. Accordingly, the sample size calculation was initially calculated on 10 times the number of scale variables, with an additional 10% dropout rate to ensure adequate power, resulting in a minimum required sample size was 407 geriatric cancer patients. A total of 467 eligible participants were ultimately enrolled in the study. After data collection, a post-hoc power analysis was conducted using G*Power 3.1 for the core statistical analyses, including logistic regression (examining the effect of MCI on LS) and Spearman correlation (between GLFS-25 and MMSE scores). The results showed that all analyses achieved statistical power above 0.80, indicating that the sample of 467 participants was sufficient to detect the observed effects.

Research instruments

A questionnaire-based survey was conducted. Relevant literature was retrieved from databases such as Web of Science, EMBASE, CNKI, and Wanfang, and the questionnaire was selected after consultation with clinical experts. A case report form was designed, containing.

Clinical data: Gender, age, height, weight, body mass index, marital status, tumor type, and comorbidities.

LS: The GLFS-25 was used to assess LS occurrence. The GLFS-25, developed by clinical experts at the Japanese Orthopaedic Association, is a screening tool for early detection of LS[7]. It comprises 25 items covering four dimensions: Physical pain, ADL, social activities, and mental health. Items are scored using a 5-point Likert scale (0 = “no difficulty” to 4 = “extreme difficulty”), with total scores ranging from 0 to 100. A total score of ≥ 16 indicates LS. Higher scores reflect worse MF. The GLFS-25 showed a Cronbach’s α coefficient of 0.961 and test-retest reliability coefficients ranging from 0.712 to 0.924. Previous studies have confirmed its appropriateness for measuring the severity of locomotive dysfunction in cancer patients[16].

MCI: The MMSE was used to assess cognitive function. MMSE includes five domains: Orientation, memory, attention and calculation, recall, and language, with a total possible score of 30 points. The diagnosis of MCI was based on educational attainment: (1) Illiterate participants: MMSE ≤ 17 indicates MCI; (2) Primary school education: MMSE ≤ 20 indicates MCI; and (3) Secondary school education or higher: MMSE ≤ 24 indicates MCI. The validated MMSE demonstrated a Cronbach’s α of 0.890 among cancer patients[11], and considering that most elderly cancer patients in China generally have lower educational attainment, this scale is suitable for the population included in the present study.

Statistical analysis

EpiData software was used to establish the study database, ensuring data accuracy and completeness. To minimize errors, two independent researchers performed double data entry and cross-validation. Only questionnaires that were fully completed after quality control were entered into the database; therefore, no missing data were present in the final dataset. Statistical analysis was conducted using SPSS 26.0 software. Qualitative data were presented as frequencies and percentages. The χ² test was applied to assess differences between groups. Binary logistic regression analysis identified risk factors associated with LS and MCI. Spearman correlation analysis assessed the relationship between LS and MCI. Statistical significance was set at P < 0.05.

A two-stage analytical approach was employed to enhance the robustness of the findings. Univariate analyses were conducted without correction for multiple comparisons in order to minimize the risk of type II errors during the variable screening phase. Variables with P < 0.05 in the univariate analyses were subsequently included in the multivariate logistic regression models, which provided adjusted estimates and constituted the primary basis for inferential conclusions.

Quality control

Rigorous quality control measures ensured data reliability: (1) Sampling method: Participants were selected using a strict multi-stage screening process; (2) Standardized training: All survey and quality-control personnel received standardized training on operating procedures and data collection protocols; (3) On-site verification: Investigators performed real-time verification during data collection to ensure accuracy and the completeness of each returned questionnaire; (4) Re-examination: Quality-control staff reviewed questionnaires thoroughly post-collection to reaffirm that no data points were missing; (5) Quality-control sampling: After data collection, questionnaires from each site underwent random re-validation, including participant information and key responses; (6) Data cleaning: Logical checks and data cleaning were conducted using specialized statistical software to remove invalid entries; and (7) Double data entry: Data were independently entered by two staff members for cross-validation to maximize accuracy. As a result of these proactive measures, all questionnaires that were returned and included in the final analysis (n = 467) were fully completed, resulting in a dataset with 100% completeness for all variables. Consequently, no data imputation methods were required. The research flowchart is presented in Figure 1.

Figure 1 Research flowchart for this study.

RESULTS

General characteristics of 467 geriatric cancer patients

A total of 500 questionnaires were distributed, and 467 were returned, yielding a response rate of 93.4%. Participants’ ages ranged from 60 years to 88 years old (mean age: 74.95 ± 6.74); 217 were male, and 250 were female. Distribution of primary tumor types among participants was as follows: Gastrointestinal tumors (122 cases, 26.1%), liver tumors (48 cases, 10.3%), head and neck tumors (57 cases, 12.2%), breast tumors (58 cases, 12.4%), reproductive system tumors (18 cases, 3.9%), and hematological malignancies (22 cases, 4.7%), as shown in Figure 2.

Figure 2 Primary tumor distribution among subjects.

Univariate analysis of LS occurrence and influencing factors

The mean GLFS-25 score among 467 geriatric cancer patients was 6.54 ± 2.23 points, with 271 patients (58.0%) experiencing LS. Univariate analysis showed significant differences in LS occurrence according to gender, age, pre-retirement occupation, smoking status, number of chronic diseases, and presence of MCI (P < 0.05; Table 1).

Table 1 Univariate analysis of factors influencing locomotive syndrome occurrence.

Variables	Classification	n	LS (n)	χ2	P value
Gender	Male	217	73	6.901	0.029
	Female	250	105
Age	60-69	189	32	23.987	< 0.001
	70-79	200	87
	≥ 80	78	59
Marital status	With partner	407	143	2.101	0.432
	Without partner	60	35
Level of education	Illiterate	68	42	2.221	0.484
	Elementary school	89	44
	Junior high school	104	34
	Vocational high school and senior high school	152	37
	University and junior college	54	21
Occupation before retirement	Peasant	135	68	18.819	0.013
	Laborer/clerical worker	263	89
	Personnel of government agencies or public institutions	60	18
	Others	9	3
Monthly income (yuan)	< 2000	152	67	2.623	0.472
	2000-4000	179	71
	> 4000	136	40
Smoking	Yes	76	22	6.434	0.030
	No	391	156
Drinking alcohol	Yes	97	33	1.741	0.813
	No	370	145
Residential situation	Living alone	60	35	1.843	0.721
	Living with family	407	143
Number of chronic diseases combined	0	114	22	28.275	< 0.001
	1	191	71
	2	109	45
	≥ 3	53	40
BMI (kg/m2)	< 18.5	20	18	2.128	0.343
	18.5-23.9	226	162
	≥ 24	221	176
MCI	Yes	143	117	21.321	< 0.001
	No	324	154

LS: Locomotive syndrome; BMI: Body mass index; MCI: Mild cognitive impairment.

Logistic regression analysis of factors influencing LS occurrence

Binary logistic regression analysis, using LS occurrence as the dependent variable and statistically significant factors from the univariate analysis as independent variables, revealed that age, number of chronic diseases, and presence of MCI were independent risk factors for LS in geriatric cancer patients (P < 0.05; Table 2).

Table 2 Logistic regression analysis of factors affecting locomotive syndrome in geriatric cancer patients.

Variables	β	SE	Wald χ2	P value	OR	95%CI
Constant	-1.412	0.287	23.324	< 0.001	-	-
MCI (none as reference)	0.521	0.254	7.542	0.017	1.732	1.154-2.708
Age (60-69 as reference)
70-79	1.421	0.203	47.612	< 0.001	3.732	2.543-5.613
≥ 80	2.621	0.312	69.876	< 0.001	9.984	5.675-16.987
Comorbid chronic diseases (0 as reference)
1	0.412	0.321	4.521	< 0.022	1.325	1.023-2.435
2	1.253	0.237	18.224	< 0.001	3.231	2.050-4.453
≥ 3	2.451	0.501	28.487	< 0.001	6.459	4.254-9.023

CI: Confidence interval; OR: Odds ratio; MCI: Mild cognitive impairment.

Occurrence of MCI and its influencing factors

The present study revealed that 143 participants (30.5%) exhibited MCI. Univariate analysis indicated significant differences in MCI occurrence according to gender, age, marital status, educational attainment, pre-retirement occupation, number of chronic diseases, and presence of LS (P < 0.05; Table 3).

Table 3 Univariate analysis of factors influencing mild cognitive impairment occurrence.

Variables	Classification	n	MCI (n)	χ2	P value
Gender	Male	217	34	9.872	0.019
	Female	250	61
Age	60-69	189	15	23.598	< 0.001
	70-79	200	39
	≥ 80	78	40
Marital status	With partner	407	77	7.098	0.013
	Without partner	60	18
Level of education	Illiterate	68	19	10.346	0.011
	Elementary school	89	23
	Junior high school	104	19
	Vocational high school and senior high school	152	24
	University and junior college	54	10
Occupation before retirement	Peasant	135	26	12.503	0.003
	Laborer/clerical worker	263	59
	Personnel of government agencies or public institutions	60	8
	Others	9	1
Monthly income (yuan)	< 2000	152	30	5.412	0.381
	2000-4000	179	38
	> 4000	136	27
Smoking	Yes	76	16	2.342	0.644
	No	391	89
Drinking alcohol	Yes	97	22	3.189	0.452
	No	370	73
Residential situation	Living alone	60	12	2.137	0.755
	Living with family	407	83
Number of chronic diseases	0	114	17	23.465	< 0.001
	1	191	30
	2	109	25
	≥ 3	53	23
BMI	< 18.5	20	3	3.556	0.382
	18.5-23.9	226	43
	≥ 24	221	49
LS	Yes	271	117	19.989	< 0.001
	No	196	26

LS: Locomotive syndrome; BMI: Body mass index; MCI: Mild cognitive impairment.

Logistic regression analysis of factors influencing MCI occurrence

Binary logistic regression analysis, with MCI occurrence as the dependent variable and significant factors from the univariate analysis as independent variables, showed that age, gender, number of chronic diseases, and presence of LS were risk factors for MCI in geriatric cancer patients (P < 0.05; Table 4).

Table 4 Logistic regression analysis of factors influencing mild cognitive impairment in geriatric cancer patients.

Variables	β	SE	Wald χ2	P value	OR	95%CI
Constant	-2.345	0.349	99.512	< 0.001	0.889	-
LS (none as reference)	0.682	0.212	18.213	< 0.001	2.321	1.489-3.132
Gender (none as reference)	0.4632	0.182	16.678	0.036	1.112	1.028-2.218
Age (60-69 as reference)
70-79	1.122	0.219	18.736	< 0.001	2.879	1.768-4.546
≥ 80	2.512	0.302	54.254	< 0.001	10.865	8.285-15.301
Comorbid chronic diseases (0 as reference)
1	-0.89	0.324	0.189	0.702	1.013	0.672-1.857
2	0.254	0.261	0.817	0.256	1.315	0.662-2.212
≥ 3	0.876	0.342	7.984	0.005	2.876	1.543-4.523

CI: Confidence interval; OR: Odds ratio.

Correlation analysis between GLFS-25 and MMSE total scores

Spearman correlation analysis showed that the total GLFS-25 score was negatively correlated with the total MMSE score and scores for individual cognitive domains (P < 0.05). The strength of these correlations varied, with the overall correlation (r = -0.436) indicating a moderate association, whereas some domain-specific correlations (e.g., memory, r = -0.212) were relatively weak. Results are shown in Table 5.

Table 5 Correlation analysis between mild cognitive impairment and locomotive syndrome.

GLFS-25	Orientation	Memory	Attention and calculation	Recall	Language	MMSE
r	-0.512	-0.212	-0.422	-0.312	-0.359	-0.436
P value	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001

GLFS: Geriatric Locomotive Function Scale; MMSE: Mini Mental State Examination.

DISCUSSION

Occurrence of LS in geriatric cancer patients and associated factors

Given China’s aging population, geriatric cancer patients face challenges in maintaining functional health. The results indicated an LS occurrence rate of 58.0% among geriatric cancer patients, consistent with prior research findings from our group[17]. However, this rate was lower compared to other studies, such as a Japanese study that reported a rate of 96.0% among cancer patients[8]. This discrepancy may be due to the inclusion criteria of this study and excluded those with primary orthopedic diseases, which required patients capable of independent living, resulting in participants with relatively better MF and improved survival outcomes.

LS in geriatric cancer patients is associated with multiple factors. This study revealed that age, chronic diseases, and MCI were significantly associated with LS (P < 0.05). Firstly, age was a major risk factor for LS in this population. Increasing age is linked to declines in cellular activity, tissue regenerative capacity, muscle volume, and muscle mass, which together may contribute to LS[18]. Secondly, geriatric cancer patients with three or more chronic diseases exhibited a 6.459-fold increased risk of LS compared to patients with fewer chronic conditions (P < 0.05). This finding underscores the compounded burden of multimorbidity, which indicates that multiple chronic disease onset is related to limits physical activity and may negatively affect MF[19].

Occurrence of MCI in geriatric cancer patients and associated factors

Cognitive impairment is among the most common health conditions affecting the elderly. As medical treatments advance, geriatric cancer patients experience longer survival, increasing the likelihood of concurrent cognitive impairment and cancer[20]. MCI is significantly associated with poor clinical outcomes in geriatric cancer patients. Assessment and treatment of MCI constitute integral aspects of addressing the psychosocial and psychiatric needs of this patient group[21]. The results of this study indicated a 30.5% MCI prevalence among geriatric cancer patients in China, higher than the prevalence (12.2%) reported in the general elderly population[22]. These findings suggest that cancer may be associated with an increased the risk of developing MCI. Cognitive impairment among geriatric cancer patients significantly exceeds that of the general elderly population, occurring at any stage of the disease course, particularly during or after surgery, chemotherapy, and targeted therapy. It is closely linked to patients’ QOL, independence, and prognosis[23]. Tumors and related treatments impose varying degrees of physical strain, especially in cases involving brain metastases or head and neck tumors, which can cause structural brain lesions[24]. Treatments such as whole-brain radiation therapy may also cause direct damage to brain tissue, further increasing the risk of MCI[25].

This study identified age, gender, chronic diseases, and LS as primary risk factors for MCI among geriatric cancer patients. The occurrence of MCI was significantly associated with age (P < 0.05), which may be related to gradual brain tissue atrophy and physiological decline, potentially impairing cognitive domains such as memory, judgment, language, and attention[26]. Previous studies reported inconsistent results regarding gender differences in MCI prevalence; some indicated higher prevalence among women, whereas others found greater prevalence among men or no gender differences[27]. This study found that female patients exhibited a significantly higher prevalence of MCI compared to male patients (P < 0.05). A potential explanation is the significant decline in estrogen levels after menopause or cancer treatments (chemotherapy or oophorectomy). Estrogen protects the hippocampus and prefrontal cortex, regions critical for memory and executive function. Reduced estrogen has been associated with increased susceptibility to neurodegenerative diseases[28]. Common breast cancer chemotherapy agents (anthracyclines and paclitaxel) can damage neurons by crossing the blood-brain barrier. Women are more susceptible to neurotoxic substance accumulation due to higher body fat percentage and variations in drug metabolism (CYP450 enzyme activity)[29]. This study also demonstrated that geriatric cancer patients with three or more chronic diseases had a 2.876-fold increased risk of MCI compared to patients without chronic conditions (P < 0.05). Older cancer patients are at increased risk for chronic diseases due to the combined effects of cancer and its treatment, and the number of chronic diseases rises significantly with advancing age[27]. Chronic diseases are negatively associated with cognitive function; for instance, hypertension has been linked to alterations in cerebral blood vessel structure and function, which may impair cognitive abilities[30]. Likewise, diabetes is associated with cerebrovascular disease and brain atrophy, thereby potentially affecting cognitive function[31].

LS is closely associated with MCI in geriatric cancer patients

To date, no clinical studies have reported the correlation between LS and MCI. This study analyzed factors influencing both conditions and found age and the number of chronic diseases to be common risk factors for LS and MCI. With advancing age, physical functions gradually decline, and the prevalence of chronic diseases progressively increases, a physiological trend closely linked to aging and disease susceptibility[32,33]. The significant association between LS and MCI identified in our study may be underpinned by several interrelated pathophysiological mechanisms specific to geriatric cancer patients. Firstly, musculoskeletal dysfunction may impair cerebral blood flow and oxygenation, which in turn reduce neural activity and contributes to cognitive decline[34]. Secondly, a shared biological pathway may be chronic, low-grade systemic inflammation, which is commonly observed in both cancer and aging[35]. This inflammatory state can promote muscle catabolism and simultaneously damage neuronal integrity, thereby linking locomotive and cognitive deterioration[36]. Furthermore, cancer-related treatments can have direct toxic effects on both the peripheral nerves/muscles and the central nervous system, creating a dual burden on motor and cognitive function[37]. Beyond these biological mechanisms, social and environmental factors may also play a role, particularly in China’s rapidly aging population. Limited access to rehabilitation resources, reduced family support, and lower community participation among older cancer patients may exacerbate both physical frailty and cognitive vulnerability[38].

The results indicated that geriatric cancer patients with LS had a 2.321-fold increased risk of developing MCI compared to patients without LS (P < 0.05). This may be because LS is associated with difficulties in daily activities and reduces physical exercise, which may lead patients to adopt sedentary lifestyles. Reduced social interaction is linked to fewer environmental stimuli, gradually narrowing cognitive engagement and potentially contributing to cognitive impairment. Conversely, geriatric cancer patients with MCI had a 1.732-fold higher occurrence of LS compared to those without MCI (P < 0.05). Long-term illness predisposes geriatric cancer patients to negative emotions such as depression and anxiety, which may exacerbate cognitive impairment[39]. Additionally, cognitive decline is related to reduced self-care ability and engagement in physical activities, which may further impair their musculoskeletal health and increase the likelihood of LS[40,41].

Correlation analysis further demonstrated that total GLFS-25 scores and MMSE scores were significantly correlated across all dimensions (P < 0.05). Although these correlations were statistically significant, their strength varied across cognitive domains. In particular, some weaker associations (e.g., memory, r = -0.212) suggest that certain cognitive subdomains may be influenced by additional physiological or psychological factors beyond MF. Nonetheless, the moderate correlation between total scores (r = -0.436) indicates a clinically meaningful association between musculoskeletal impairment and overall cognitive decline in geriatric cancer patients. Some studies have suggested that cognitive function in geriatric cancer patients is a critical predictor of their functional status and mobility[42,43].

Taken together, these results emphasize the need for integrated clinical strategies addressing both physical and cognitive health. Healthcare professionals should proactively implement comprehensive, multidimensional interventions tailored to patients’ cognitive impairments and motor disorders. Structured physical exercise, cognitive stimulation, adequate nutritional support, and psychosocial interventions are essential to maintaining both muscle and brain health. Although certain cognitive domains (e.g., memory) demonstrated only weak correlations with locomotive function, these subtle associations may still indicate early functional vulnerability. Therefore, early identification and coordinated management may help improve overall outcomes in this vulnerable population.

Limitations

This study has several limitations: (1) The cross-sectional and single-center design precludes causal inferences and may limit the generalizability of our findings, despite facilitating standardized data collection. The relatively homogeneous sample and the exclusion of patients with primary orthopedic diseases, while intended to reduce confounding, may have introduced selection bias and led to an underestimation of the true prevalence of LS; (2) The use of the MMSE for cognitive screening, though validated, has known limitations in sensitivity for detecting MCI, particularly among individuals with higher educational attainment; future studies would benefit from incorporating more sensitive tools like the Montreal Cognitive Assessment; and (3) Several potential confounders were not captured, including detailed cancer-related characteristics (e.g., specific tumor stage, brain metastases, neurotoxic therapies) and psychological factors (e.g., depression and anxiety). Although the GLFS-25 partially addresses pain, its influence independent of LS remains unclear. Consequently, residual confounding cannot be ruled out. Future research should employ multi-center, prospective cohort designs with larger samples, comprehensive confounder assessments, and refined measurement tools to verify these associations, explore underlying mechanisms, and establish causality.

CONCLUSION

In summary, MCI and LS are significantly associated in geriatric cancer patients. Healthcare providers should prioritize early and concurrent intervention for MCI and LS during patient care, offering targeted guidance. By addressing both cognitive impairment and LS, overall health outcomes can be effectively improved.

ACKNOWLEDGEMENTS

We appreciate all participants for their time and effort.