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The effect of long-term strength training on brain metabolism in the elderly: a 7T 1H MRS study
Xinyu Liu1,2,3, Selin Scherrer4, Sven Egger4, Song-I Lim2, Benedikt Lauber4, Wolfgang Taube4, and Lijing Xin2,3
1Laboratory for functional and metabolic imaging (LIFMET), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, 3Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 4Department of Neurosciences and Movement Science, University of Fribourg, Fribourg, Switzerland

Synopsis

Keywords: Aging, Nervous system, Cortical inhibition

Motivation: The effect of short-term physical activity on brain metabolism has been widely studied in young population, but less is known about metabolic plasticity induced by strength training over a long range among elderly adults.

Goal(s): This study examined the modulatory potential of long-term strength training on metabolism in elderly population.

Approach: Using 7 tesla magnetic resonance spectroscopy (MRS), we assessed concentrations of γ-Aminobutyric acid (GABA), glutamate (Glu), and a number of other metabolites in the sensorimotor cortex.

Results: We found that three-months of strength learning significantly reduced lactate levels in the sensorimotor cortex compared to a passive control group.

Impact: This study systematically examined brain metabolic plasticity induced by strength training in the elderly population. The reduction in lactate indicates that strength training may facilitate oxidation of lactate to meet increased energy demands for learning.

Intoduction

Physical exercise is an important measure to counteract age-associated physiological deterioration processes. Acute physical activity has been shown to modulate metabolism in the human brain, such as elevation of GABA, Glu1 and lactate2 in visual cortex. However, most of previous studies focused on young population and it remains elusive whether similar modulatory effects can be observed after a long period of physical training, especially in elderly population who are known to suffer from reduced physical abilities.
In this study, we seek to evaluate the effects of long-term strength training on brain metabolism in elderly adults using 7T magnetic resonance spectroscopy (MRS). The concentration of the main inhibitory neurotransmitter GABA were measured by MEGA-sSPECIAL, a method reported to reliably quantify metabolites GABA levels in sensorimotor cortex with high reproducibility3. Semi-adiabatic SPECIAL (sSPECIAL) sequence was used for neurochemical profiling of other metabolites.

Methods

Thirty-two healthy volunteers (66 – 82 years old, 17 males/15 females) gave informed consent prior to the study. They are randomly assigned to strength learning (n = 16) or a control group (n =16).
We used MP2RAGE T1 weighted images for voxel positioning, MEGA-sSPECIAL for edited GABA measurement, and sSPECIAL for neurochemical profiling. Detailed acquisition parameters can be found in Table 1. After the first MRS session, participants in the strength training group underwent three months of progressive, multifaceted strength training. They were trained around 3 times per week (a total of at least 30 training sessions) for 45 minutes in supervised group sessions. Participants in the control group did not engage in any training. After three months, both groups underwent the second measurement session with identical MR protocol as the first one.
MR spectra were averaged after frequency drift and phase correction using FID-A4 and analyzed by LCModel5 for quantification. Metabolites with Cramer-Rao lower bound (CRLB) lower than 30% were reported. Unsuppressed water signal was used for quantification and water content was corrected for tissue composition. Metabolite concentrations were corrected for CSF contribution. A linear mixed-effect model with group as between-subject factor and time as within-subject factor was used to detect whether there is an interaction effect between time and group. False discovery rate (FDR) correction were performed using Benjamini and Hochberg procedures6 when comparing metabolite concentrations detected using sSPECIAL. When there is a significant interaction effect, post-hoc two-sample t-tests were followed.

Results

Demographic information and spectral quality parameters can be found in Table 2. There is no significant difference observed in signal-to-noise ratio (SNR) and linewidth between the pre- and post-measurements. Figure 1 showed an exemplar voxel placement and spectra for left sensorimotor cortex.
Using linear mixed-effect model, we found that there is no significant interaction effect of time × group in GABA level (p = 0.14, z59 = -1.493, [-0.730, 0.099]) (Figure 2A). Semi-adiabatic SPECIAL detected eleven metabolites of interest. We found that there is a significant interaction effect of time × group in lactate level (corrected p = 0.049, z57 = -2.839, [-0.762, -0.140]). Post-hoc analysis suggests that this is driven by a significant decrease in lactate in strength training group (p = 0.02) but not in the control group (p = 0.33) (Figure 2B). No significant interaction effects were found for other investigated metabolite concentrations (Figure 3).

Discussion

Lactate is an important energy metabolite in the brain which ensures adequate energy supply, modulates neuronal excitability levels and regulates adaptive functions in order to set the 'homeostatic tone' of the nervous system7. Multiple MRS studies have reported increased brain lactate level following acute physical exercise2,8. Contrast to short-term studies, we found that long-term strength learning significantly reduced sensorimotor cortical lactate level in the learning group compared to the control group. Previous animal studies have shown that lactate accumulation is a hallmark of aging and is associated with elevated lactate dehydrogenase (LDH)-A/LDH-B ratio9. Thus, one potential explanation for the observed lactate reduction could be that strength learning can facilitate the oxidation of lactate in the brain into pyruvate which enters in oxidative phosphorylation for energy production in aerobic fashion.

Conclusion

In conclusion, the present study found that long-term strength training significantly reduced lactate level compared to a control group. This may indicate that strength training has the potential to facilitate oxidation of lactate in the brain to meet increased energy demand for learning, and shed new light into the distinct modulatory mechanisms between short-term and long-term physical exercise on brain metabolism.

Acknowledgements

This work was supported by the Swiss National Science Foundation (grants n° 32003B_197687). We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence funded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).

References

  1. Maddock RJ, Casazza GA, Fernandez DH, Maddock MI. Acute modulation of cortical glutamate and GABA content by physical activity. J Neurosci. 2016;36(8):2449-2457. doi:10.1523/JNEUROSCI.3455-15.2016
  2. Maddock RJ, Casazza GA, Buonocore MH, Tanase C. Vigorous exercise increases brain lactate and Glx (glutamate+glutamine): A dynamic 1H-MRS study. Neuroimage. 2011;57(4):1324-1330. doi:10.1016/j.neuroimage.2011.05.048
  3. Lim SI, Xin L. γ-aminobutyric acid measurement in the human brain at 7 T: Short echo-time or Mescher–Garwood editing. NMR Biomed. 2022;(June 2021):1-17. doi:10.1002/nbm.4706
  4. Simpson R, Devenyi GA, Jezzard P, Hennessy TJ, Near J. Advanced processing and simulation of MRS data using the FID appliance (FID-A)—An open source, MATLAB-based toolkit. Magn Reson Med. 2017;77(1):23-33. doi:10.1002/mrm.26091
  5. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed. 2001;14(4):260-264. doi:10.1002/nbm.698
  6. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc. 1995;57(1):289-300. doi:10.2307/2346101
  7. Magistretti PJ, Allaman I. Lactate in the brain: From metabolic end-product to signalling molecule. Nat Rev Neurosci. 2018;19(4):235-249. doi:10.1038/nrn.2018.19
  8. Dennis A, Thomas AG, Rawlings NB, et al. An ultra-high field magnetic resonance spectroscopy study of post exercise lactate, glutamate and glutamine change in the human brain. Front Physiol. 2015;6(DEC). doi:10.3389/fphys.2015.00351
  9. Ross JM, Öberg J, Brené S, et al. High brain lactate is a hallmark of aging and caused by a shift in the lactate dehydrogenase A/B ratio. Proc Natl Acad Sci U S A. 2010;107(46):20087-20092. doi:10.1073/pnas.1008189107

Figures

Table 1. Acquisition parameters of the MR sequences used in the current study.

Table 2. Demographic information and spectral quality parameters of the two groups. There are no significant differences between pre- and post-measurements in both groups (p > 0.1). Values are reported as mean and standard deviation.

Figure 1. Exemplar MRS voxel placement (A) as well as edited and short-TE MR spectra (B) of the left sensorimotor cortex.

Figure 2. Comparison of GABA (A) and lactate (B) levels between pre and post measurements in both groups. A significant interaction (p = 0.005) was found for lactate level in training group compared to control group.

Figure 3. Comparison of short TE sSPECIAL detected metabolite concentrations before and after training in the two groups. No significant interaction was found for other metabolites.

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
4209
DOI: https://doi.org/10.58530/2024/4209