INTRODUCTION
The increasing proportion of older adult individuals underscores the urgent need to understand age-related changes in muscle and cognitive functions. Sarcopenia and cognitive decline, which are significant issues affecting older adults, have profound implications on the health and quality of life of this aging population.1) Sarcopenia, characterized by a progressive loss of skeletal muscle mass and function, leads to physical frailty, difficulties in daily activities, increased risk of falls, depression, frequent hospitalizations, and higher mortality rates.2,3) This decline in physical functional capacity substantially diminishes the quality of life in older adults.4) Concurrently, cognitive decline involves deterioration in cognitive functions, such as language, memory, reasoning, social cognition, and planning, which can range from mild forgetfulness to severe impairments that disrupt daily living and independence.5,6)
Recent research has indicated a correlation between sarcopenia and cognitive function. Skeletal muscles play a crucial role in brain health by producing neurotrophic factors, such as brain-derived neurotrophic factor (BDNF), which are essential for maintaining brain synapses and are influenced by muscle activity.7) Low skeletal muscle mass, which can lead to inflammation and altered myokine secretion, is a key contributor to cognitive impairment.8) Therefore, preserving muscle health is vital for maintaining cognitive function.
Muscle health also affects brain function. Low muscle mass and cognitive impairment are risk factors of mortality in older adults.9) The relationship between muscle mass and cognitive function appears to differ by sex, as evidenced by a study showing that men with lower muscle mass had a higher risk of cognitive decline, but no such correlation was found in women.10) Moreover, recent findings suggest that muscle function, rather than muscle mass alone, may have a closer link to cognitive health.11,12) In particular, grip strength, an indicator of muscle strength, has been shown to improve muscle function and, consequently, cognitive health.13) Previous large cohort studies have suggested that grip strength is associated with measures of neurocognitive brain health in both men and women, indicating that grip strength is a critical indicator of muscle function and aging.14,15)
Despite these insights, the precise nature of the relationship among muscle mass, muscle strength, and cognitive function remains unclear. Understanding these connections is crucial for developing effective interventions to enhance the physical and cognitive health of older adults. Thus, this study aimed to elucidate these bidirectional relationships by investigating how cognitive function is related to skeletal muscle mass and grip strength in older adults.
MATERIALS AND METHODS
Participants
This study was conducted from September 2023 to November 2023 through the 2023 BUs-based Screening and Assessment Network (BUSAN) study of Busan National University Hospital. The study population comprised 400 individuals aged ≥65 years who visited welfare centers and senior community centers in Busan and agreed to participate in the study. After selection, a comprehensive health assessment, including body composition, Korean version of Mini-Mental State Examination (K-MMSE), grip strength, tablet cognitive evaluation, and the Symbol Digit Substitution Test (SDST), was conducted by the medical staff. After excluding 65 participants who dropped out or provided insufficient responses, 335 were included in the final analysis. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Busan National University Hospital (IRB No. 2305-030-127). The participants provided informed consent.
Characteristic of Participants
Body composition was assessed using bioelectrical impedance analysis (BWA 2.0; InBody Co., Seoul, South Korea), with measurements including height, weight, body mass index (BMI), body fat percentage, and skeletal muscle mass. Cognitive function was evaluated using the K-MMSE, tablet cognitive evaluation, and SDST, whereas muscle strength was measured using grip strength. Sociodemographic variables included age, sex, employment status, presence of a spouse, educational level, smoking status, and history of diabetes, hypertension, and dyslipidemia. Participants’ K-MMSE scores and grip strength were categorized to identify those with cognitive impairment and grip strength below the threshold.
Measurements
Grip strength
Grip strength, a pivotal marker of global physical functionality, was quantified using a digital dynamometer (Grip-D TKK5101; Takey, Tokyo, Japan) to ensure reliable measurements. The procedure required the participants to align the dynamometer with the specific positioning of the hand, ensuring that the second knuckle of the index finger was perpendicular to the handle. Grip strength was measured twice in the dominant hand, and the average of these two measurements was reported as the final value.
SMMI/LMI
Skeletal muscle measurements were assessed using BWA 2.0 (InBody Co.). Skeletal muscle mass index (SMMI) was calculated by dividing skeletal muscle mass (kg) by height squared (m2). Low SMMI was defined as ≤10.75 kg/m2 for men and ≤6.75 kg/m2 for women.16) Lean mass index (LMI) was calculated by dividing lean muscle mass (kg) by height squared (m2), with low LMI defined as <14.6 kg/m2.17)
Cognitive function tests
Cognitive function was assessed using the K-MMSE, a widely used tool in both clinical and research settings.18) The K-MMSE comprises items that assess orientation, memory, attention, calculation, language function, comprehension, and judgment abilities, with total scores ranging from 0 to 30. Scores ≥24 indicate normal cognition, scores between 18 and 23 indicate mild cognitive impairment, and scores from 0 to 17 indicate severe cognitive impairment.19)
Tablet-based cognitive assessment
The tablet-based cognitive function assessment consisted of three cognitive tests: the Sternberg Memory Scanning (SMS) task, the Paired Associates Learning (PAL) task, and the SDST. The tablet-based SDST was developed by modifying the original SDST20,21) to evaluate information processing speed. Information processing speed was measured as participants matched specific symbols to corresponding numbers, with the number of correct matches recorded within a 120-second time limit. The tablet-based SMS task was adapted from the original SMS task to assess working memory. In this task, participants were shown random sequences of 3, 5, or 7 digits and were asked to remember them. Working memory was assessed by giving participants 30 trials, with each trial requiring them to select the correct answer within 2 seconds. The tablet-based PAL test was adapted from the original PAL22) to evaluate visual memory. This test involved identifying the positions of squares to measure how quickly and effectively participants acquired and retained new information. Visual memory was assessed as participants were given 15 problems and had 5 seconds to provide an answer for each. The total number of correct answers and the time taken were recorded.
Statistical Analyses
Participants' characteristics were reported using numbers (percentages) for categorical variables and mean±standard deviation for continuous variables. Independent t-tests were used to compare continuous variables between men and women, while chi-square tests were used for categorical variables. Due to non-normal distribution of the variables (as verified by Shapiro-Wilk test), Spearman correlation matrix was used to examine the correlations among muscle mass indices, grip strength, and cognitive function measures.
Binary logistic regression analysis was used to investigate the bidirectional relationship between muscle mass indices, grip strength, and cognitive function performance. Binary logistic regression analysis was used to investigate the bidirectional relationship between muscle mass indices, grip strength, and cognitive function performance. Low grip strength was classified using cutoff points of 28 kg for men and 16 kg for women,23) whereas cognitive decline was defined as a K-MMSE score <24. Poor tablet cognitive performance is defined as a score below the median. In addition to analyzing the main effects of exposures (e.g., grip strength, muscle mass indices) on cognitive outcomes, we tested for potential interaction effects between sex and all exposures. Interaction terms (e.g., exposure × sex) were included in logistic regression models to evaluate whether sex acts as an effect modifier. Odds ratios (ORs), 95% confidence intervals (CIs), and p-values for interaction terms were estimated. No significant interaction effects were identified, supporting our treatment of sex as a confounding factor in the primary analyses. However, interaction results are presented in Supplementary Tables S1–S3 for completeness.
All regression models were adjusted for potential confounders including age, sex, BMI, body fat percentage, employment status, spousal presence, educational status (high school graduation), and history of diabetes, hypertension, dyslipidemia, and smoking. Analyses were conducted using IBM SPSS Statistics version 23.0 (IBM Corp., Armonk, NY, USA). A p-value <0.05 was considered statistically significant.
RESULTS
The study included 335 eligible participants (91 males and 244 females), with a mean age of 77.5±6.1 years. Significant sex differences were observed in several physical characteristics: body fat percentage (male 28.3%±7.5% vs. female 34.4%±6.8%; p<0.001), height (male 165.7±5.8 cm vs. female 152.2±5.6 cm; p<0.001), weight (male 68.1±9.7 kg vs. female 56.4±8.6 kg; p<0.001), grip strength (male 30.5±6.5 kg vs. female 17.6±4.9 kg; p<0.001), LMI (male 14.8±1.4 kg/m² vs. female 16.60±1.6 kg/m2; p<0.001), and SMMI (male 9.5±1.2 kg/m2 vs. female 8.3±1.0 kg/m2; p<0.001) (Table 1).
Regarding categorical variables, significant sex differences were found in spouse presence (male 76.9% vs. female 36.1%; p<0.001), educational status (p=0.002), diabetes history (male 41.8% vs. female 24.2%; p=0.003), dyslipidemia history (male 28.6% vs. female 42.6%; p=0.026), and smoking status (male 16.5% vs. female 0.4%; p<0.001). Both groups showed similar rates of hypertension history (male 58.2% vs. female 57.8%; p=1.000) and grip strength below cut-points (male 39.6% vs. female 38.9%; p=1.000). Most participants in both groups were unemployed (male 76.9% vs. female 85.7%; p=0.082) and did not show cognitive decline (male 86.8% vs. female 78.3%; p=0.109). (Table 1).
Table 2 presents the Spearman correlation matrix among muscle indices, grip strength, and cognitive functions. Grip strength showed significant moderate correlations with muscle indices (LMI and SMMI) and weak to moderate correlations with all cognitive measures. Both muscle indices were very strongly correlated with each other and showed weak but significant correlations with most cognitive measures. Among cognitive measures, strong correlations were observed between information processing speed, working memory, visual memory, and K-MMSE score.
Binary logistic regression analyses were conducted to examine the bidirectional relationship between grip strength, muscle mass indices, and cognitive function. Tables 3 and 4 demonstrate the bidirectional relationship between grip strength and cognitive function. After adjusting for potential confounders, participants with grip strength below recommended cut-offs showed significantly higher odds of cognitive decline (OR=2.70, 95% CI 1.38–5.28) and poor performance across all tablet-based cognitive tests (ORs ranging from 2.44 to 2.80; all p<0.05). Similarly, when cognitive function was treated as the predictor, poor cognitive performance was significantly associated with increased odds of low grip strength (ORs ranging from 2.48 to 2.77, all p<0.05). Tables 5 and 6 present the correlations between muscle mass indices (LMI and SMMI) and cognitive function. After adjusting for the same confounders, neither low LMI nor low SMMI showed significant correlations with cognitive decline or tablet-based cognitive performance measures. Similarly, when cognitive function was treated as the predictor, no significant correlations were found with either muscle mass index. All models were adjusted for age, sex, BMI, body fat percentage, employment status, spouse presence, educational status, and history of diabetes, hypertension, dyslipidemia, and smoking.
DISCUSSION
This study examined the relationship between muscle strength and cognitive function in older adult individuals and highlighted the significant impact of physical functionality on cognitive health. Our findings confirm that grip strength and muscle mass are crucial indicators of cognitive function. Specifically, older adults with lower grip strength had lower K-MMSE scores, indicating a higher risk of cognitive decline. This finding suggests that grip strength is closely associated with overall physical function and plays a vital role in maintaining cognitive health.24)
Grip strength is well-established as a strong predictor of overall health, particularly in the context of aging, where it serves as an indicator of neural function and brain health.25) Our study revealed significant bidirectional correlations between grip strength and cognitive function, as measured by both K-MMSE and tablet-based cognitive assessments. These findings are supported by research showing that muscle activity can influence neuroprotective factors such as BDNF,26) and that muscle-derived myokines (including cathepsin B, FNDC5/irisin, and interleukin-6) play crucial roles in various brain functions such as learning, memory, and mood regulation.27) While some studies have suggested that low muscle mass might contribute to cognitive impairment through inflammatory responses,8) our findings indicate that grip strength—rather than muscle mass alone—may be a more reliable indicator of cognitive health. This suggests that grip strength measurement could serve as a simple yet effective screening tool for early assessment of cognitive health in older adult populations, potentially contributing to earlier intervention in cognitive decline.
Our findings align with previous studies11,28) showing significant correlations between grip strength and cognitive function, but no significant relationships between muscle mass indices and cognitive function. While previous studies primarily examined raw muscle mass values, our study advanced the analysis by incorporating height-adjusted indices (LMI and SMMI) with established cut-points, providing a more standardized assessment of muscle status. Even with these refined measures that account for body size differences, we still found no significant correlations with cognitive function, strengthening the consistency of previous findings.
The consistency across studies, despite our more sophisticated muscle mass measurements, suggests that grip strength may be a more robust indicator of cognitive function than muscle mass indices in older adults, possibly because grip strength reflects not only muscle mass but also muscle function and overall neuromuscular integrity.
The absence of significant relationships between muscle mass indices and cognitive function in both K-MMSE and tablet-based assessments might reflect the complex nature of muscle-brain interactions. While grip strength provides a dynamic measure of neuromuscular function, muscle mass alone—even when standardized for height—may not fully capture the quality and functionality of muscle tissue that could be relevant to cognitive health. Additionally, the difference in assessment methods—K-MMSE involving direct examiner interaction versus tablet-based assessment requiring independent device interaction—provides complementary perspectives on cognitive function, strengthening the reliability of our findings.29)
These results underscore the importance of incorporating grip strength measurements in clinical assessments of older adults, as they may serve as valuable indicators of both physical and cognitive health. Future longitudinal studies are needed to further explore the temporal relationship between these parameters and to determine whether interventions targeting grip strength could effectively contribute to maintaining cognitive function in the aging population.
This study has several limitations. The cross-sectional design inherently limits our ability to establish causal relationships among muscle mass, grip strength, and cognitive function. While we explored the correlations between these variables using logistic regression models with reversed dependent and independent variables, it's crucial to understand that this approach, while demonstrating correlations in both directions, does not provide evidence of causal bidirectionality. As discussed earlier, these observed correlations could be due to confounding factors, reverse causality (where the presumed “outcome” may actually influence the “predictor”), or simply reflect statistical correlations without a direct causal link. Therefore, the observed reciprocal correlations should be interpreted with caution. Future longitudinal studies are needed to determine the true directionality of these relationships and explore the underlying biological mechanisms in greater detail. Such studies, employing methodologies like cross-lagged panel models or intervention designs, would be better suited to disentangle the complex interplay between muscle mass, grip strength, and cognitive function. Additionally, as our sample was representative of an older population, the findings may not be generalizable to younger populations or those with different health conditions. Further research is needed to investigate these relationships in diverse populations.
This study provides further evidence of a strong relationship between muscle health and cognitive function in older individuals. These findings suggest that maintaining muscle mass and strength is crucial for preserving cognitive ability in aging populations. Further research is warranted to explore effective interventions that could mitigate the impact of muscle loss on cognitive decline and to better understand the interconnected pathways linking these two critical aspects of aging.
In conclusion, this study revealed that muscle strength play crucial roles in preserving cognitive function in older adults. Specifically, grip strength were identified as strong predictors of cognitive decline, suggesting that grip strength measurement could serve as a simple yet important tool for assessing cognitive health. Furthermore, maintaining muscle strength and mass through physical activity may prevent or delay cognitive decline. Future research should explore the relationship between muscle strength and cognitive function in greater detail, considering factors such as sex, socioeconomic status, and lifestyle. Longitudinal studies assessing the effect of muscle maintenance programs on cognitive function over time are required.
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