Abstract
Sarcopenia has been reported to be associated with cognitive decline and the risk of dementia. However, few studies have addressed the association between sarcopenia and brain morphological changes in the general population. A total of 1373 community-dwelling participants aged ≥ 65 years underwent brain MRI. Sarcopenia was defined based on the Asian Working Group for Sarcopenia's criteria. The pattern of regional gray matter volume loss associated with sarcopenia were assessed using a voxel-based morphometry (VBM) analysis. Regional brain volumes, intracranial volumes (ICV), and white matter lesions volumes (WMLV) were also measured using FreeSurfer. An analysis of covariance was used to examine the associations of sarcopenia with regional brain volumes in proportion to ICV. Of the participants, 112 had sarcopenia. The participants with sarcopenia had significantly lower total brain volume/ICV and total gray matter volume/ICV and higher WMLV/ICV than those without sarcopenia after adjusting for confounders. In VBM, sarcopenia was associated with lower gray matter volume in the frontal lobe, insula, cingulate gyrus, hippocampus, amygdala, and basal ganglia. Using FreeSurfer, we confirmed that the participants with sarcopenia had significantly lower frontal, insular, cingulate, and hippocampal volumes than those without sarcopenia. The current study showed that participants with sarcopenia had significantly lower volume in the frontal lobe, insula, cingulate, and hippocampus and higher WMLV than participants without sarcopenia. As these brain regions are likely to play an important role in cognitive function, these changes may suggest a shared underlying mechanism for the progression of sarcopenia and cognitive decline.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11357-024-01289-8.
Keywords: Sarcopenia, Brain MRI, Gray matter, White matter lesion, General population
Introduction
Sarcopenia, a multifaceted geriatric syndrome characterized by age-related loss of skeletal muscle mass associated with reduced muscle strength and/or impaired physical performance [1–3], is emerging as a major health concern for older adults. In recent years, there have been several reports on the relationship between sarcopenia and cognitive decline [4, 5] and incidence of dementia [6], as well as the relationship between reduced gait speed or handgrip strength, which are components of sarcopenia, and the development of dementia [7–9]. It is also well known that patients with dementia show progressive brain atrophy and the development of cerebral white matter lesions as morphological changes [10–12]. Therefore, it is anticipated that individuals with sarcopenia are more likely to have gray matter atrophy of dementia-related brain regions, such as the medial temporal lobe and hippocampus, as well as a higher white matter lesions volume (WMLV). Some population-based studies have examined the association between sarcopenia and brain morphological changes, but the findings on the atrophic regions were not consistent across these previous studies, probably due to the small sample sizes and differences in populations and in methods for quantifying regional brain volumes [13–15].
The aim of the current study was to examine the patterns of gray matter volume loss associated with sarcopenia and each of its components (low gait speed, low handgrip strength, and low muscle mass) using voxel-based morphometry (VBM) without defining regions of interest. In addition, we confirmed the findings regarding sarcopenia and the gray matter volume in the brain regions identified in the VBM analysis and investigated the association between sarcopenia and WMLV, which could not be assessed in the VBM analysis by using another quantitative brain volume measurement tool for regions of interest, the FreeSurfer software.
Methods
Study population
The Hisayama Study is an ongoing population-based observational study for non-communicable diseases such as cerebrocardiovascular diseases, lifestyle-related diseases and geriatric conditions in the town of Hisayama, a suburban community of the Fukuoka metropolitan area of Kyushu Island in Japan, where annual health examinations have been conducted since 1961. In addition, comprehensive screening surveys for dementia have been conducted every 5 to 7 years since 1985 among residents aged 65 years or older. Detailed information regarding the study design has been previously published [16, 17].
In 2017 and 2018, a total of 2202 residents aged ≥ 65 years (94.1% of the town’s total population in this age group) participated in the screening survey of dementia. Of these participants, 1577 individuals (71.6%) underwent brain MRI scanning. Moreover, the following participants were excluded from the study: 7 individuals who did not consent to the use of their MRI data, 119 participants whose brain MRI data were considered not applicable for the VBM or FreeSurfer analysis after visual inspection by the study team, and 78 participants with insufficient information to diagnose sarcopenia. The remaining 1373 participants (585 men and 788 women) were included in the present study (Fig. 1).
Fig. 1.
Flow chart of the study sample selection. Abbreviations: MRI, magnetic resonance imaging; VBM, voxel-based morphometry
Standard protocol approvals, registrations, and participant consent
This study protocol for the present analysis was approved by the Kyushu University Institutional Review Board for Clinical Research (Approval No. 686–10), and written informed consent was obtained from all participants.
Definition of sarcopenia
Sarcopenia in this study was defined according to the criteria of the Asian Working Group for Sarcopenia (AWGS) [2]. A diagnosis required the presence of both low muscle mass and low muscle function, which could be indicated by either low handgrip strength or low gait speed. In cases where data were missing for either muscle mass or muscle function, an attempt was made to determine the presence of sarcopenia using the limited data available. The detailed diagnostic procedure for sarcopenia was described in our previous study [18]. Muscle mass was measured by bioelectrical impedance analysis using an MC-190 body composition analyzer (Tanita, Tokyo). Low muscle mass was defined as a skeletal muscle mass index < 7.0 kg/m2 for men and < 5.7 kg/m2 for women. Handgrip strength was assessed twice alternately for each hand, and the maximum value from the four measurements was used for the analysis. Low handgrip strength was characterized as < 28 kg for men and < 18 kg for women. Usual gait speed was measured in the middle 5 m of the course and the faster of the two trials was selected for analysis. Slow gait speed was identified as < 1.0 m/s.
MRI analysis
A detailed description of the brain MRI data acquisition and processing was published previously [19]. Magnetic resonance imaging examinations included T1-weighted, three-dimensional, magnetization-prepared, rapid gradient echo images, conventional T1- and T2-weighted images, fluid-attenuated inversion recovery (FLAIR) images, T2*-weighted images, and magnetic resonance angiography of the brain. They were performed using a 1.5-Tesla MRI scanner (Intera Pulsar; Philips Medical Systems, Best, the Netherlands) with a multichannel head coil. T1-weighted three-dimensional images acquired in the sagittal plane were used to determine regional brain atrophy with the following parameters: repetition time 8.5 ms, echo time 4.0 ms, inversion time 1000 ms, flip angle 8 °, field of view 240 mm, acquisition matrix 192 × 192, slice thickness 1.2 mm, number of excitations 1.
Voxel-based morphometry preprocessing
T1-weighted three-dimensional images were processed and examined using VBM8 Toolbox version 435 (University of Jena, Germany) in SPM8 (University College London, UK) running in MATLAB (The Mathworks, Inc.). The default parameters were used, except that affine regularization was performed using the International Consortium for Brain Mapping template for East Asian brains. T1-weighted images were segmented into gray matter, white matter, and cerebrospinal fluid. To prevent misclassification of white matter lesions as gray matter, we created individualized binarized white matter hyperintensity masks and corrected for white matter and gray matter. The segmented gray matter images underwent normalization and modulation to compensate for volumetric alterations during spatial normalization. Finally, we applied an isotropic Gaussian kernel with a full-width half-maximum of 8 mm for image smoothing. Intracranial volume (ICV) was determined by summing the volumes of gray matter, white matter, and cerebrospinal fluid.
FreeSurfer preprocessing
Segmentation and volume measurements of cortical and subcortical brain structures, including total brain volume (TBV), total gray matter volume (GMV), white matter lesions, and ICV, were performed automatically using FreeSurfer software, version 6.0.0 (Harvard University). To ensure accuracy, all automated segmentation results from FreeSurfer were visually inspected by the study team. A group of six study members, including an expert stroke neurologist and an expert psychiatrist, double-checked the accuracy of the cortical segmentation performed by FreeSurfer according to the Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) Cortical Quality Control Protocol 2.0. If an inaccurate segmentation was identified by at least one team member, all misclassifications related to the skull and dura were corrected using appropriate commands and the images were reanalyzed. The reanalyzed images were then reviewed again by the study members to ensure their suitability for the present analysis. Brain volumes for each region were summed for the left and right sides. To account for variations in head size, TBV, total GMV, regional brain volumes, and WMLV divided by ICV were computed for the analysis.
Other risk factors
Participants answered questionnaires about their medical history, treatment for hypertension and diabetes, educational level, alcohol consumption, smoking habits, regular exercise habits, and depressive symptoms. Low education was defined as 9 years or less of formal education. Alcohol consumption and smoking habits were classified as current or not current. Regular exercise habits were defined as exercising three or more times per week during leisure time. Depressive symptoms were assessed using the 15-item Geriatric Depression Scale (GDS), and a participant with a GDS score of ≥ 6 was defined as having depressive symptoms. Resting blood pressure, analyzed as the mean of three measurements, was measured in the sitting position, and hypertension was defined as blood pressure ≥ 140/90 mmHg and/or current antihypertensive treatment. Plasma glucose levels were determined by the hexokinase method, and diabetes mellitus was defined as either a fasting plasma glucose level ≥ 126 mg/dl postprandial or a 2-h postload or casual glucose level ≥ 200 mg/dl or current treatment with hypoglycemic agents. Serum total cholesterol was measured enzymatically. Height and weight were measured in light clothing without shoes, and body mass index (BMI) was calculated (in kilograms per square meter). Dementia was assessed using the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised criteria by screening with the Mini-Mental State Examination and comprehensive psychiatric examinations as previously described17. All dementia cases were adjudicated by expert psychiatrists and stroke physicians on the study team. Total energy intake was determined by a semiquantitative food frequency questionnaire. Cerebrovascular lesions observed on MRI were defined as the presence of cerebral infarction or hemorrhage, regardless of the presence or absence of neurological symptoms. The definitions of cerebral infarction and brain hemorrhage were described in our previous study [20].
Statistical analysis
The WMLV/ICV was natural logarithmically transformed because of its skewed distribution. The age- and sex-adjusted mean values of continuous variables and frequencies of categorical variables for risk factors were calculated and compared between participants with and without sarcopenia using a multiple linear regression analysis and a logistic regression analysis including age and sex in the relevant model.
For voxel-based analysis, statistical parametric mapping was performed using an ANCOVA between participants with and without sarcopenia or its components (low gait speed, low handgrip strength, low muscle mass) after adjustment for age, age-squared, sex, low education, hypertension, diabetes, serum total cholesterol, body mass index, total energy intake, current smoking habits, current alcohol intake, regular exercise, depressive symptoms, cerebrovascular lesions on MRI, and ICV calculated with voxel-based analysis. We applied both explicit masking using Template_6_IXI550_MNI152 with GM values greater than 0.05 and absolute masking with a threshold of 0.01 [20]. For all statistical maps, thresholds were set by combining an uncorrected P < 0.001 at the peak level and a familywise error-corrected P < 0.05 at the cluster level. We performed a sensitivity analysis excluding individuals with dementia. Supplementary Table 1 provides the Montreal Neurological Institute (MNI) coordinates for this analysis.
For the analyses using the brain volume data estimated by the FreeSurfer software, the age- and sex-adjusted or multivariable-adjusted means of TBV, total GMV/ICV, and the regional brain volume/ICV, and geometric means of WMLV/ICV were compared between participants with and without sarcopenia by using an analysis of covariance (ANCOVA). In this analysis, the following brain regions were selected as regions of interest in order to confirm the results of the VBM analysis: the (1) frontal lobe, (2) cingulate, (3) insula, (4) hippocampus, (5) amygdala, and (6) basal ganglia. In the multivariable analysis, model 1 included age, age-squared, and sex; and model 2 included age, age-squared, sex, low education, hypertension, diabetes, serum total cholesterol, BMI, total energy intake, current smoking habits, current alcohol intake, regular exercise, depressive symptoms, and cerebrovascular lesions on MRI. Another sensitivity analysis was performed in a sex- and age-matched population to consider the large age difference between individuals with and without sarcopenia. The participants with sarcopenia and those without sarcopenia were matched with the use of a 1:2 matching protocol. In this analysis, we used the paired t-test and ANCOVA to compare the crude and multivariable-adjusted means of regional brain volume/ICV and geometric means of WMLV/ICV between the participants with and without sarcopenia. The statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC). We considered P values < 0.05 statistically significant.
Results
Of the 1373 participants, 112 participants had sarcopenia at baseline. Table 1 shows the age- and sex-adjusted characteristics of the study participants according to the status of sarcopenia. The mean values of age and serum total cholesterol and the proportion of participants with depressive symptoms were significantly higher in participants with sarcopenia than in those without sarcopenia, whereas the mean values of BMI and total energy intake and the proportions of participants with hypertension and antihypertensive medication were significantly lower in the sarcopenia group.
Table 1.
Age- and sex-adjusted baseline characteristics of the study participants with and without sarcopenia in 2017–2018
| No sarcopenia | Sarcopenia | ||
|---|---|---|---|
| (n = 1261) | (n = 112) | P value | |
| Age, yearsa | 73.3 (0.2) | 80.3 (0.6) | < 0.001 |
| Men, %b | 42.5 | 44.0 | 0.77 |
| Education ≤ 9 years, % | 44.9 | 55.0 | 0.06 |
| Systolic blood pressure, mmHg | 130.3 (0.5) | 131.7 (1.8) | 0.46 |
| Diastolic blood pressure, mmHg | 70.6 (0.3) | 70.9 (1.1) | 0.83 |
| Hypertension, % | 67.4 | 55.3 | 0.02 |
| Antihypertensive agents, % | 55.2 | 40.7 | 0.01 |
| Diabetes mellitus, % | 24.5 | 26.7 | 0.62 |
| Serum total cholesterol, mg/dL | 205.7 (1.0) | 216.9 (3.4) | 0.002 |
| Body mass index, kg/m2 | 23.6 (0.1) | 20.2 (0.3) | < 0.001 |
| Current alcohol intake, % | 43.3 | 35.4 | 0.17 |
| Current smoking, % | 4.5 | 8.9 | 0.09 |
| Regular exercise, % | 21.8 | 18.6 | 0.48 |
| Cerebrovascular lesions on MRI, % | 29.7 | 36.3 | 0.16 |
| Depressive symptoms, GDS score ≥ 6, % | 13.4 | 21.4 | 0.02 |
| Total energy intake, kcal/day | 1854.3 (11.0) | 1740.2 (37.9) | 0.004 |
| Dementia, % | 2.9 | 4.8 | 0.10 |
Abbreviations: MRI magnetic resonance imaging; ADL activities of daily living; GDS Geriatric Depression Scale
Values are shown as mean (standard error) or percentage
aAge is adjusted for sex
bSex is adjusted for age
Figure 2 shows the violin plots of TBV/ICV, total GMV/ICV, WMLV/ICV according to the status of sarcopenia. The participants with sarcopenia had significantly lower mean values of TBV/ICV and total GMV/ICV and higher geometric mean value of WMLV/ICV than those without sarcopenia after adjusting for age, age-squared and sex (model 1). These significant associations were unchanged after adjustment for other potential confounders (model 2).
Fig. 2.
Violin plots of adjusted values of TBV/ICV (A), total GMV/ICV (B), and WMLV/ICV (C) among the participants with and without sarcopenia. Abbreviations: TBV, total brain volume; ICV, intracranial volume; GMV, gray matter volume; WMLV, white matter lesions volume. The box represents the interquartile range (IQR). The bottom of the box is the first quartile, and the top of the box is the third quartile. The thick horizontal line in the middle of the box represents the median. The lines extending from the top and bottom of the box represent the data within 1.5 * IQR. Means are indicated by a white dot. Model 1: Adjusted for age, age-squared and sex. Model 2: Adjusted for age, age-squared, sex, low education, hypertension, diabetes, serum total cholesterol, body mass index, total energy intake, current smoking habits, current alcohol intake, regular exercise, depressive symptoms, and cerebrovascular lesions on magnetic resonance imaging. *P value < 0.05
Figure 3 shows the results of the multivariable-adjusted VBM analysis. The participants with sarcopenia had smaller significant gray matter volume than those without sarcopenia in the following brain regions: the frontal lobe, insula, cingulate, hippocampus, amygdala and basal ganglia. In addition, we compared the brain volumes of these 6 brain regions, which were estimated using the FreeSurfer software, according to the status of sarcopenia (Table 2). After adjustment for age, age-squared, and sex, the participants with sarcopenia had significantly lower frontal, insular, cingulate, hippocampal and amygdalar volumes than participants without sarcopenia (model 1). These results were robust after adjusting for the other confounding factors (model 2), except in the case of the amygdala, which failed to reach a level of statistical significance. In the sensitivity analysis, similar associations were observed for total GMV, the volumes of the 6 brain regions, and WMLV in participants excluding the 82 participants with dementia (Supplementary Table 2) and in participants matched for age and sex in a 1:2 ratio (109 participants with sarcopenia and 209 without sarcopenia) (Supplementary Table 3).
Fig. 3.
Gray matter regions that were inversely correlated with the presence of sarcopenia. The regions of gray matter atrophy mainly involved the frontal lobe, insula, cingulum, hippocampus, amygdala and basal ganglia. Analyses was performed with adjustment for age, age-squared, sex, low education, hypertension, diabetes, serum total cholesterol, body mass index, total energy intake, current smoking habits, current alcohol intake, regular exercise, depressive symptoms, cerebrovascular lesions on magnetic resonance imaging, and intracranial volume. The color intensity represents T-statistic values, and there was a strong positive correlation between these values and the intensity of the yellow color
Table 2.
Adjusted mean values (95% CIs) of the regional brain volumes among the participants with and without sarcopenia (no sarcopenia n = 1261, sarcopenia n = 112)
| Model 1 | Model 2 | |
|---|---|---|
| Frontal cortex volume/ICV | ||
| No sarcopenia | 9.71 (9.67–9.75) | 9.71 (9.67–9.75) |
| Sarcopenia | 9.54 (9.41–9.67) | 9.55 (9.41–9.68) |
| P value | 0.01 | 0.02 |
| Insular cortex volume/ICV | ||
| No sarcopenia | 0.881 (0.876–0.885) | 0.876 (0.885–0.881) |
| Sarcopenia | 0.861 (0.846–0.875) | 0.846 (0.875–0.859) |
| P value | 0.01 | 0.01 |
| Cingulate gyrus volume/ICV | ||
| No sarcopenia | 1.118 (1.101–1.134) | 1.101 (1.134–1.122) |
| Sarcopenia | 1.084 (1.062–1.107) | 1.062 (1.107–1.075) |
| P value | 0.01 | 0.01 |
| Hippocampal volume/ICV | ||
| No sarcopenia | 0.485 (0.482–0.488) | 0.482 (0.488–0.485) |
| Sarcopenia | 0.465 (0.456–0.475) | 0.456 (0.475–0.468) |
| P value | < 0.001 | 0.002 |
| Amygdalar volume/ICV | ||
| No sarcopenia | 0.179 (0.177–0.180) | 0.177 (0.180–0.179) |
| Sarcopenia | 0.171 (0.167–0.176) | 0.167 (0.176–0.174) |
| P value | 0.002 | 0.07 |
| Basal ganglia volume/ICV | ||
| No sarcopenia | 1.260 (1.240–1.281) | 1.240 (1.281–1.260) |
| Sarcopenia | 1.250 (1.222–1.278) | 1.222 (1.278–1.239) |
| P value | 0.38 | 0.41 |
Abbreviations: CI, confidence interval; ICV, intracranial volume
Values are percentages unless otherwise indicated and shown as adjusted mean values (CIs)
Model 1: Adjusted for age, age-squared and sex. Model 2: Adjusted for age, age-squared, sex, low education, hypertension, diabetes, serum total cholesterol, body mass index, total energy intake, current smoking habits, current alcohol intake, regular exercise, depressive symptoms, and cerebrovascular lesions on magnetic resonance imaging
Furthermore, we examined the association between each of the three components of sarcopenia and patterns of brain atrophy. Gray matter atrophy regions significantly associated with low gait speed and low handgrip strength overlapped with those for sarcopenia (Fig. 4A and B), but there were no significantly reduced gray matter areas correlating with lower muscle mass (Fig. 4C).
Fig. 4.
Gray matter regions that were inversely correlated with the presence of low gait speed (A), low handgrip strength (B), and low muscle mass (C). The regions of gray matter atrophy associated with low gait speed mainly involved the frontal lobe, temporal lobe, insula, cingulum, hippocampus, amygdala, and entorhinal cortex (A). Those associated with low handgrip strength included the frontal lobe, insula, hippocampus, amygdala, caudate, putamen, accumbens, thalamus and entorhinal cortex (B). There were no significantly decreased gray matter areas that were correlated with low muscle mass (C). Values were adjusted for age, age-squared, sex, low education, hypertension, diabetes, serum total cholesterol, body mass index, total energy intake, current smoking habits, current alcohol intake, regular exercise, depression, cerebrovascular lesions on magnetic resonance imaging, and intracranial volume. The color intensity represents T-statistic values, and there was a strong positive correlation between these values and the intensity of the yellow color
Discussion
The present study demonstrated that participants with sarcopenia had significantly lower total brain volume, total gray matter volume, and higher white matter lesions volume than participants without sarcopenia after adjustment for potential confounding factors in a general older Japanese population. Furthermore, the VBM analysis revealed that sarcopenia was associated with lower gray matter volume in the frontal lobe, insula, cingulate gyrus, hippocampus, amygdala, and basal ganglia. Significant associations were also observed between sarcopenia and the gray matter volumes of the frontal lobe, insula, cingulate gyrus, and hippocampus in the automated segmentation analysis using FreeSurfer software. These findings suggest that sarcopenia is associated with decreased gray matter volume of certain brain regions that are known to be associated with cognitive function.
There have been two population-based cross-sectional studies investigating the association between sarcopenia and brain volume, but the results are inconsistent. In the Women’s Healthy Ageing Project Study including 150 Australian women (median age of 70 years), there was no difference in brain volumes between participants with and without sarcopenia [13]. Another study, which included 102 retirement-community residents aged 65 years and older in Taiwan, showed that the sarcopenia group had smaller parietal volume than the non-sarcopenia group [14]. Furthermore, one longitudinal research from the Ansan cohort of the Korean Genome Epidemiology Study (1284 participants, mean age of 57 years) reported that participants with sarcopenia had greater reductions in the parietal lobe volume than participants without sarcopenia over a 4-year follow-up periods [15]. In contrast, the present study showed that sarcopenia was associated with reduced brain volume in a wider range of brain regions. The discrepancy may be due to differences in the sample size, the age distribution, the method used to diagnose sarcopenia, or the approach used to analyze brain imaging data. In the previous studies, brain volume was assessed using methods such as a visual rating scale [14] or a single brain imaging analysis tool [13, 15]. In contrast, we confirmed our results using two different brain imaging analysis tools, namely VBM and FreeSurfer. On the other hand, in regard to the associations between components of sarcopenia, such as gait speed and grip strength, and morphological changes in the brain, several previous studies found that gait speed or grip strength were associated with lower brain volume in the frontal lobe, cingulate gyrus, insula, hippocampus, basal ganglia, and cerebellum, and higher white matter lesions volume, some of which have been reported to be associated with dementia [21–23]. These findings were consistent with ours. Taken together, the present findings suggest that sarcopenia is associated with reduced brain volumes of specific regions related to cognitive function.
Several biological mechanisms can explain the association between sarcopenia and brain atrophy and increased white matter lesions volume. First, diabetes and unhealthy lifestyles, including smoking and physical inactivity, are common risk factors for both sarcopenia and morphological changes in the brain [24]. These cardiovascular risk factors can lead to oxidative stress, chronic inflammation, and insulin resistance. These biological changes can induce muscle wasting through protein degradation and anabolic resistance [24, 25], while also triggering neurodegeneration and neuronal cell death [20, 26]. Second, decreased skeletal muscle mass and function due to sarcopenia may cause brain atrophy. Recently, it has been clarified that some myokines, substances secreted by skeletal muscle, play a role in regulating immune cell functions [27, 28]. Among these myokines, irisin and cathepsin B have been found to induce the expression of brain-derived neurotrophic factor in the hippocampus, enhancing synaptic plasticity of neurons and improving cognitive function [29, 30]. In individuals with sarcopenia, decreased skeletal muscle mass and function may lead to reduced levels of these myokines, potentially contributing to brain atrophy. Third, brain atrophy and white matter lesions may also contribute to the development of sarcopenia. The frontal lobe affects spontaneity and executive function [31]. The hippocampus is known to be involved not only in memory but also in movement [32]. The cingulate gyrus is related to cognition and emotion [33]. The insula is reported to be involved in emotion, somatosensation, and taste [34]. White matter lesions disrupt the microenvironment in the brain, leading to impairment of the intercortical networks, which may impair motor function [35]. These factors can lead to reduced physical activity, decreased motivation, and poor nutrition, which may be additional factors contributing to the development of sarcopenia. These mechanisms are not unidirectional but form a complex interrelationship between sarcopenia and brain morphological changes, potentially giving rise to a vicious cycle in which one pathology exacerbates the other [36].
The present study found no association between reduced skeletal muscle mass and morphological changes in the brain. A previous study similarly found that skeletal muscle mass was not associated with gray matter volume [37]. Recent studies have shown that muscle weakness and physical function decline are more strongly associated with outcomes such as ADL decline, falls, hospitalization, and death than skeletal muscle mass decline [38–40]. The qualitative changes in skeletal muscle may explain the difference in the association among the sarcopenia components. Muscle fibers are largely divided into type I fibers and type II fibers, and it is known that older adults have a myofiber distribution in which type I fibers predominate and type II fibers are atrophic. Moreover, muscle biopsy images have revealed an increase in cell gaps with aging, and such gaps are believed to be infiltrated by fibrotic tissue and intramuscular fat [41]. These changes in muscle composition may lead to differences in muscle output, even when skeletal muscle mass is equivalent. Consequently, assessing skeletal muscle mass loss alone may not fully capture the complex relationship between sarcopenia and morphological changes in the brain.
The strengths of the current study included the population-based design, the comprehensive assessment of potential confounders, and the use of two different analysis techniques for the evaluation of brain volume (i.e., VBM and FreeSurfer). However, several limitations should be noted. First, the cross-sectional design of the study makes it difficult to establish a causal relationship between sarcopenia and brain morphological changes. Longitudinal studies would be needed to gain more insight into the temporal relationship between these variables. Furthermore, although the participation rate in the screening survey was relatively high, approximately one-third of the residents were not included in the analysis. The characteristics of these non-participants differed from those who participated, as non-participants were more likely to be older, to have lower activity of daily living, and to have a higher prevalence of dementia (data not shown). This introduces the possibility of selection bias. Moreover, the substantial disparity in group sizes (with the sarcopenic group being ten times smaller than the non-sarcopenic group) presents a significant limitation, as the smaller sample size in the sarcopenic group could result in less reliable estimates and increased variability, thus affecting the robustness of our findings. However, we believe that the results have a certain degree of robustness, as similar findings were obtained using both VBM and FreeSurfer analyses. Finally, the study participants were limited to older individuals from a single suburban region in Japan. As a result, the generalizability of the study results to broader populations, such as those in urban or rural areas or from different ethnic backgrounds, may be limited.
Conclusion
In community-dwelling older adults, sarcopenia was associated with reduced brain volume in the frontal lobes, cingulate gyrus, insula, and hippocampus, as well as increased volume of white matter lesions. These regions are known to be associated with cognitive function, suggesting a common neural basis for the development of sarcopenia and cognitive decline. Understanding the common neural basis between these conditions could lead to the development of targeted interventions and strategies aimed at preserving both muscle health and cognitive function in aging populations.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the staff members of the Division of Health of Hisayama for their cooperation in this study. The statistical analyses using the SAS software were performed using the computers offered under the category of General Projects by the Research Institute for Information Technology, Kyushu University.
Author contributions
T Tajimi and T Ninomiya contributed to the study conception and study design; T tajimi, N Hirabayashi, Y Furuta, T Nakazawa and T Ninomiya contributed to the data analysis; T Tajimi, N Hirabayashi, Y Furuta, T Nakazawa, T Honda, J Hata, T Ohara, M Shibata and T Ninomiya contributed the data collection. T Tajimi wrote the first draft of the manuscript and all authors contributed to data interpretation and critical revision of the manuscript.
Funding
This study was supported in part by the Ministry of Education, Culture, Sports, Science and Technology of Japan (JSPS KAKENHI Grant Number: JP21H03200, JP21K07522, JP21K11725, JP21K10448, JP22K07421, JP22K17396, JP23K09692, JP23K09717, JP23K16330, JP23K06787, and JP23K09060); by the Health and Labour Sciences Research Grants of the Ministry of Health, Labour and Welfare of Japan (Grant Number: JPMH23FA1006 and JPMH23FA1022); by the Japan Agency for Medical Research and Development (Grant Number: JP23dk0207053 and JP23km0405209); and by Japan Science and Technology Agency (Grant Number: JPMJPF2210).
Data availability
The dataset used in the current study is not publicly available as it contains confidential clinical data on the study individuals. However, the data are available upon reasonable request and with the permission of the Principal Investigator of the Hisayama Study, Professor Toshiharu Ninomiya.
Declarations
Conflict of interest
None declared.
Footnotes
Publisher's Note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The dataset used in the current study is not publicly available as it contains confidential clinical data on the study individuals. However, the data are available upon reasonable request and with the permission of the Principal Investigator of the Hisayama Study, Professor Toshiharu Ninomiya.