Abstract
Background
Muscle strength and brain volume decline with aging; changes in the brain manifested as change in volume may play a role in age-related strength loss, but this hypothesis has never been tested longitudinally. We examined longitudinal associations between brain volume changes and knee extension peak torque change in participants of the Baltimore Longitudinal Study of Aging.
Methods
Brain volumes and isokinetic concentric knee extension peak torque at 30 deg/s were measured in 678 participants (55.2% women; baseline age, 50.1–97.2 years; median follow-up time in those who visited two or more times (n = 375, 4.0 [interquartile range {IQR}, 2.3–5.0] years). Correlations between longitudinal changes in brain volumes and knee extension peak torque were examined using bivariate linear mixed-effects models, adjusted for baseline age, sex, race, education, and intracranial volume.
Results
Greater decline in muscle strength was associated with greater atrophies in global gray matter, temporal lobe, frontal gray matter, temporal gray matter, superior frontal gyrus, inferior frontal gyrus, supramarginal gyrus, middle temporal gyrus, inferior temporal gyrus, and occipital pole (r ranging from .30 to .77, p < .05). After multiple comparison adjustment, only larger decrease in middle temporal gyrus remained significantly related to larger decrease in muscle strength (q = 0.045).
Conclusions
In older adults, declines in knee extension muscle strength co-occurred with atrophies in frontal, temporal, and occipital gray matter. These findings support the idea that age-related knee extension muscle strength is linked with atrophy in some specific brain regions related to motor control.
Keywords: Sarcopenia, Isokinetic dynamometer, Brain aging
Muscle strength declines with age; loss of strength is an important contributor to mobility impairment and disability (1). Optimal muscle strength depends on a harmonized and effective motor control program, conduction through the peripheral nervous system and local muscle structure and composition. All these factors tend to change with age (2). In spite of general consensus that age-related loss of central nervous system function plays an important role in age-related muscle strength loss, very few studies have directly tested this hypothesis (3).
Age-related declines in brain structure and muscle strength well established but there is as yet no evidence that change over time in brain structure are relates to change in muscle strength. A review of the literature shows that studies examining associations between brain volume and strength have largely been cross-sectional. A case–control study demonstrated that top-level young athletes have larger regional gray matter in striatum and thalamus than well-matched less athletic individuals (4). In another study, masters athletes, compared to sedentary individuals showed greater gray and white matter in subgyral, cuneus, and precuneus areas (5). In a multivariate regression model to examine the association between reaction time and corpus callosum size, hand grip strength used as a covariate was positively related to the midbody of the corpus callosum volume in adults aged 60–75 years (6). Other studies have failed to demonstrate any significant associations of gray matter, white matter, or cerebrospinal fluid volume with hand grip strength (7–9). In community-dwelling 70-year-old individuals, higher baseline normal-appearing white matter volume was associated with subsequent less decline in several physical performance measures, including gait speed, hand grip muscle strength, and forced expiratory volume (10).
Thus, there are several important gaps in knowledge about the relationships between brain volume and strength. First, no studies to date have included a broad range of older adults. Muscle strength and brain volume losses become apparent through middle age to the very old age (11,12), and therefore, longitudinal data that cover a broader age range may provide insight on how changes in muscle strength and brain volume are linked to each other. Second, no study has evaluated longitudinal changes of volumes in multiple brain regions and muscle strength.
The Baltimore Longitudinal Study of Aging (BLSA) collected repeated measures of structural magnetic resonance imaging in 47 brain regions and measures of isokinetic muscle strength. The availability of these data offers a unique opportunity to overcome limitations of the previous literature and fully test the hypothesis that age-associated brain changes co-occur with age-related changes in muscle strength. Testing this hypothesis is important because it may imply that interventions to promote healthy brain aging might also help retaining muscle strength with aging. The goal of this study was to pinpoint brain region whose atrophy co-occur with changes in knee extension peak torque in a large sample of middle-aged and older adults.
Methods
Participants
In this longitudinal study, we used data from the BLSA (13). The BLSA is a prospective observational study of mechanisms implicated in the decline of physical and cognitive functions with aging in humans. We selected visits when participants were over 50 years of age and had concurrent data on isokinetic concentric knee extension strength, magnetic resonance imaging structural brain imaging, and a set of baseline characteristics to be used as covariates in the analysis. We excluded visits after the development of central nervous system disease (eg, epilepsy and stroke), psychiatric disorders, cardiac disease (eg, myocardial infarction and coronary artery disease), cancer, and overt cognitive impairment. Determination of cognitive impairment followed the Diagnostic and Statistical Manual of Mental Disorders 3rd Edition, Revised (DSM-III-R) criteria for dementia, and the National Institute of Neurological and Communication Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association criteria (14) for Alzheimer’s disease. Neuropsychological diagnostic tests and clinical data were reviewed at consensus diagnostic conferences. After exclusions, 678 participants (women, n = 376; men, n = 302) were eligible, for a total of 1,355 person-visits. Overall, 303 participants had data for one visit and 375 had data for two or more visits. Median follow-up time for participants who had data for more than two visits was 4.0 (interquartile range [IQR], 2.3–5.0) years. The BLSA protocol was approved by the Institutional Review Board of record at the time of data collection (National Institute of Environmental Health Sciences, NC) and written informed consent was obtained from all participants.
Knee Extension Muscle Strength
Between April 2003 and February 2011, the BLSA measured isokinetic concentric knee extension peak torque by the Kin-Com isokinetic dynamometer (Kin-Com model 125E, version 3.2, Chattanooga Group, Chattanooga, TN). From February 2011 to present (data included through 13 December 2017), BLSA used the Biodex Multi-Joint System-Pro dynamometer (Biodex Medical System, Advantage Software V.4X, Inc., Shirley, NY). For both the Kin-Com and the Biodex isokinetic concentric knee extension testing, the participants performed three trials at 30 deg/s. Knee joint range was set between 100 and 160 deg (full extension, 180 deg). Peak torque was defined as the highest trial value. Reliability of knee extension peak torque examined between the highest and second highest torques among three trials yielded an intraclass correlation coefficient (ICC) of 0.96. We harmonized the data between these two dynamometers based on a conversion equation that was previously developed by having 76 participants assessed with both equipments in the same visit (15). A merged data set was used for further analyses.
Brain Imaging
From February 2009 to present, T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) scans were acquired on a 3T Philips Achieva MR scanner (Philips, Best, The Netherlands) for each participant and visit included in these analyses. As previously described (16), a multi-atlas approach was used for segmentation and volumetric quantification of anatomical brain regions. We assessed 47 brain regions of interest including both global and regional measures that entail whole brain analysis. In addition to total brain volume, gray matter, white matter, and ventricular volumes, we examined frontal, temporal, parietal, and occipital regions, basal ganglia, and limbic system (see Table 1 and Supplementary Table S1; reliability ICC’s range from 0.88 to 0.99) (16). Intracranial volume was defined as the volume within the skull, including the left and right celebral hemispheres, brainstem, celebellum, and cerebrospinal fluid.
Table 1.
Participant Characteristics
| Overall | |
|---|---|
| (n = 678) | |
| Mean ± SD | |
| Demographic characteristics | |
| Age (years) | 71.2 ± 10.2 |
| Sex (women, %) | 55.5 |
| Race (non-white, %) | 33.0 |
| Education (years) | 17.0 ± 2.4 |
| Physical characteristics | |
| Weight (kg) | 76.1 ± 15.3 |
| Height (cm) | 167.5 ± 9.1 |
| Concentric peak torque (Nm) | 110.5 ± 40.9 |
Statistical Analysis
Descriptive data were reported as mean ± standard deviation (SD) or percentages. To estimate the correlation between longitudinal change in peak torque and longitudinal change in brain volume, we used bivariate linear mixed-effects models so that longitudinal trajectories from peak torque and brain volume can be jointly modeled and the correlations between changes can be estimated directly (17).
In these bivariate linear mixed-effects models, there are two sets of fixed effects and random effects separately for each muscle strength and brain volume measure. For muscle strength measures, the fixed effects included sex, race, education, baseline age, baseline age squared, time (follow-up time), and two-way interactions between sex and baseline age with time. For brain volume measures, the intracranial volume was added as an additional covariate. The random effects for each outcome included intercept and time. We specify the 4 × 4 variance–covariance of the random effects to be unstructured. The correlations between longitudinal change in muscle strength and changes brain volume were directly estimated from this variance–covariance matrix after accounting for fixed effects.
Sex was coded 0 for women and 1 for men, race 0 for white, and 1 for nonwhite, baseline age was centered at 70, education was centered at 17 years. Intracranial volume was also mean centered. The bivariate linear mixed-effects models were fit using procedure PROC NLMIXED in SAS 9.4 (SAS Institute, 2015). Significance level was set at 0.05 level. Additionally, we reported the false discovery rate to account for multiple comparisons (18). We used SAS software version 9.4 for Windows (SAS Institute, Inc., Cary, NC) for statistical analyses. We included data from participants with only one visit in the analysis because, although they do not contribute to the longitudinal effects estimates in our model, they provide information on cross-sectional baseline estimates (19). However, we confirmed our findings in a sensitivity analysis that only included participants with two or more visits.
Results
Baseline participant characteristics are presented in Table 1. Participants ranged in age from 50 to 97 years. The education level in BLSA is higher than in the general older adults population. Compared to those with only one visit, participants with two or more visits were significantly older and had larger entorhinal cortex and smaller anterior cingulate gyrus (p < .05; Supplementary Table S1). Spaghetti plots show that brain volumes in superior frontal gyrus and isokinetic peak torque tended to decline over time and were lower at older baseline age (Figure 1).
Figure 1.
Trajectory changes in isokinetic, concentric knee extension peak torque (Nm) and superior frontal gyrus (cm3).
Associations Between Changes in Isokinetic Peak Torque and Regional Brain Volumes
The rate of change in muscle strength was significantly associated with the magnitude of changes in total gray matter (r = .33, p = .031), temporal lobe (r = .31, p = .043), frontal gray matter (r = .30, p = .048), temporal gray matter (r = .35, p = .013), superior frontal gyrus (r = .49, p = .010), inferior frontal gyrus (r = .010), supramarginal gyrus (r = .48, p = .031), middle temporal gyrus (r = .48, p = .002), inferior temporal gyrus (r = .50, p = .011), inferior occipital gyrus (r = .59, p = .033), and occipital pole (r = .77, p = .009; Table 2; Figure 2). However, after multiple comparison adjustment, only larger decrease in middle temporal gyrus remained significantly related to larger decrease in muscle strength (q = 0.045).
Table 2.
Correlation Between Change in Brain Volume and Change in Knee Extension Peak Torque
| Region | Rho | Lower CI | Upper CI | p-value | FDR q-value |
|---|---|---|---|---|---|
| Total brain | 0.30 | −0.07 | 0.67 | 0.118 | 0.137 |
| vCSF | −0.06 | −0.25 | 0.13 | 0.533 | 0.533 |
| Gray matter | 0.33 | 0.03 | 0.63 | 0.031 | 0.083 |
| White matter | −0.12 | −0.61 | 0.36 | 0.614 | 0.614 |
| Frontal | 0.22 | −0.15 | 0.58 | 0.243 | 0.243 |
| Temporal | 0.31 | 0.01 | 0.61 | 0.043 | 0.097 |
| Parietal | 0.22 | −0.16 | 0.60 | 0.246 | 0.246 |
| Occipital | 0.31 | −0.16 | 0.79 | 0.196 | 0.196 |
| Frontal gray matter | 0.30 | 0.00 | 0.60 | 0.048 | 0.097 |
| Temporal gray matter | 0.35 | 0.07 | 0.62 | 0.013 | 0.059 |
| Parietal gray matter | 0.29 | −0.02 | 0.61 | 0.067 | 0.101 |
| Occipital gray matter | 0.64 | −0.01 | 1.00 | 0.052 | 0.097 |
| Frontal white matter | −0.20 | −0.71 | 0.30 | 0.427 | 0.427 |
| Temporal white matter | 0.10 | −0.34 | 0.54 | 0.652 | 0.652 |
| Parietal white matter | −0.16 | −0.58 | 0.27 | 0.470 | 0.470 |
| Occipital white matter | −0.33 | −0.71 | 0.05 | 0.088 | 0.117 |
| Superior frontal gyrus | 0.43 | 0.10 | 0.76 | 0.010 | 0.059 |
| Middle frontal gyrus | 0.18 | −0.14 | 0.51 | 0.260 | 0.260 |
| Inferior frontal gyrus | 0.42 | 0.05 | 0.79 | 0.026 | 0.083 |
| Medial frontal cortex | 0.15 | −0.18 | 0.48 | 0.363 | 0.363 |
| Orbitofrontal | 0.30 | −0.13 | 0.73 | 0.167 | 0.172 |
| Precentral gyrus | 0.29 | −0.13 | 0.72 | 0.175 | 0.175 |
| Postcentral gyrus | 0.52 | −0.23 | 1.00 | 0.173 | 0.173 |
| Superior parietal lobule | 0.24 | −0.20 | 0.69 | 0.287 | 0.287 |
| Supramarginal gyrus | 0.48 | 0.04 | 0.92 | 0.031 | 0.083 |
| Angular gyrus | 0.25 | −0.13 | 0.63 | 0.199 | 0.199 |
| Precuneus | 0.26 | −0.06 | 0.58 | 0.109 | 0.137 |
| Superior temporal gyrus | 0.34 | −0.01 | 0.70 | 0.057 | 0.097 |
| Middle temporal gyrus | 0.48 | 0.17 | 0.78 | 0.002 | 0.045 |
| Inferior temporal gyrus | 0.50 | 0.12 | 0.89 | 0.011 | 0.059 |
| Hippocampus | −0.04 | −0.33 | 0.25 | 0.788 | 0.788 |
| Entorhinal cortex | −0.44 | −1.00 | 0.12 | 0.121 | 0.137 |
| Amygdala | 0.01 | −0.28 | 0.30 | 0.946 | 0.946 |
| Fusiform gyrus | 0.22 | −0.12 | 0.56 | 0.205 | 0.205 |
| Superior occipital gyrus | 0.43 | −0.22 | 1.00 | 0.197 | 0.197 |
| Middle occipital gyrus | 0.39 | −0.02 | 0.80 | 0.060 | 0.097 |
| Inferior occipital gyrus | 0.59 | 0.05 | 1.00 | 0.033 | 0.083 |
| Occipital pole | 0.77 | 0.19 | 1.00 | 0.009 | 0.059 |
| Cuneus | 0.15 | −0.43 | 0.73 | 0.617 | 0.617 |
| Anterior cingulate gyrus | −0.02 | −0.82 | 0.78 | 0.963 | 0.963 |
| Posterior cingulate gyrus | 0.45 | −0.06 | 0.95 | 0.083 | 0.117 |
| Caudate | 0.14 | −0.17 | 0.44 | 0.376 | 0.376 |
| Globus pallidus | 0.03 | −0.32 | 0.39 | 0.848 | 0.848 |
| Putamen | −0.04 | −0.29 | 0.21 | 0.763 | 0.763 |
| Thalamus | −0.01 | −0.46 | 0.43 | 0.950 | 0.950 |
Note: CI = Confidence interval; FDR = False discovery rate. FDR q-values less than 0.05 indicate that 5% of significant test results would result in false positives. The significance level (bold) was set at p-value less than 0.05.
Figure 2.
Brain areas that show volumetric change associated with muscle strength change. Five axial slices (a–e) correspond to slice level are shown in f. Colors represent p-values in the correlations between change in brain volume and change in knee extension peak torque.
Significant positive associations indicated that faster rates of decline in muscle strength were associated with faster rates of decline in brain volumes. Findings limited to participants with two or more visits yielded effects very similar to those reported above (Supplementary Table S2).
Discussion
Among older adults, declines in isokinetic knee extension peak torque co-occurred with atrophy in selected frontal areas related to motor control and the temporal area. This is the first study to examine longitudinally the parallel associations between loss of muscle strength and brain volume changes. We found that brain shrinkage in regions related to motor control versus regions less related to motor control, co-occur with the loss of muscle strength. Notably, we also found that atrophy in supramarginal gyrus, which is not directly connected to motor control, was also associated with strength loss. This association may be due to the central role of supramarginal gyrus in proprioception and preplanning of the motor program and the critical role of precuneus for movement coordination (3,20).
The execution of an isokinetic movement requires the coordinated execution of different processes, including force production from the agonist muscle and synchronous relaxation of the antagonist muscles, and such coordination may require contributions from brain regions other than the primary motor cortex. In this context, it is interesting to speculate that muscle strength loss measured by isokinetic muscle contraction was associated with decline in the temporal lobe and a part of occipital gyrus, because the temporal lobe modulates processing time-related events during contraction and because the occipital gyrus handles visual processing. Although we did not provide visual feedback to participants during the testing, participants might have intentionally or unintentionally paid attention to their knee extension movement.
It is generally assumed that the primary motor cortex (precentral gyrus) plays a major role during the execution of a movement or during isokinetic contraction, and therefore is more activated than other brain regions in stronger and fitter individuals (3,20). A cross-sectional study demonstrated that over the age range from 18 to 93 years, age is inversely related to the thickness of the precentral gyrus (R2 = .34) compared to other brain regions (21). These data suggest that the primary motor and premotor areas are sensitive to the aging process. This hypothesis is consistent with previous research that reported a longitudinal association between brain volume in the primary motor cortex and gait speed (22). Our results that atrophies in superior frontal gyrus and inferior frontal gyrus coexist with loss of muscle strength may provide important information on how brain atrophy and physical performance are linked to each other.
Our study has important strengths. The analytic sample was large and included a broad distribution of sex, age, and race. The availability of longitudinal data allowed evaluation of change over time in both brain and knee extension peak torque. The analysis was adjusted for relevant, potential confounders.
Our study also has limitations. Our intent was to conduct an exploratory analysis in a novel area of investigation. Therefore, we identified brain regions that show volumetric changes associated with strength change over time, with a nominal p-value of less than .05. After adjusting for multiple comparisons, all of the reported associations were no longer significant except for the middle temporal gyrus and muscle strength. Therefore, our findings should be confirmed in future studies conducted in a different population. We included participants with only one visit because analyses including them have more stable baseline estimates. However, in a sensitivity analysis with only participants with at least two visits, findings were consistent with the main results.
Conclusion
In this first longitudinal study of brain volume changes associated with changes in muscle strength in older adults, we found areas of regional atrophy related to knee extension isokinetic strength decline. This work supports a potential role for age-related regional brain atrophy in age-related change in muscle strength. These results also imply that a greater rate of strength decline might indicate accelerated shrinkage in brain regions related to motor control. Understanding the mechanisms underlying these relationships may inform new strategies to counteract potential effects of brain aging on loss of muscle strength.
Supplementary Material
Acknowledgments
Study design: Y.O., S.S., and L.F. Data collection, analysis, and interpretation: Y.O., Q.T., Y.A., S.R., and L.F. Statistical analyses: Y.O. and Y.A. Critical revision of the draft: Q.T., Y.A., S.R., S.S., and L.F. Draft of the report: Y.O. and L.F. All authors approved the final version of the report.
Funding
This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute on Aging.
Conflict of Interest
None reported.
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