Metabolomics of Aerobic Exercise in Chronic Stroke Survivors: A Pilot Study

Aerobic exercise interventions for promoting cardiovascular health and mobility after stroke: a systematic review with Bayesian network meta-analysis

OBJECTIVE

To determine the superiority of aerobic exercise (AE) interventions on key outcomes of stroke recovery, including cardiorespiratory fitness (V̇O2peak, primary outcome), systolic blood pressure (SBP) and mobility (6 min Walk Test (6MWT) distance and 10 m Usual Gait Speed) after stroke.

DATA SOURCES

MEDLINE, EMBASE, Web of Science, CINAHL, CENTRAL, SPORTDiscus, PsycINFO and AMED Allied and Complementary Medicine were searched from inception to February 2023.

ELIGIBILITY CRITERIA

Randomised controlled trials were included that compared the effects of any AE interventions (low-intensity, moderate-intensity, high-intensity continuous training (HICT), high-intensity interval training (HIIT)) to no exercise, usual care or other AE interventions in individuals poststroke.

ANALYSES

Systematic review with Bayesian network meta-analysis (NMA) methodology was employed. Surface under the cumulative ranking curve (SUCRA) values were used to rank interventions. The Grading of Recommendations, Assessment, Development and Evaluation minimally contextualised framework for NMA was followed.

RESULTS

There were 28 studies (n=1298) included in the NMA for V̇O2peak, 11 (n=648) for SBP, 28 (n=1494) for 6MWT and 18 (n=775) for the 10 m Usual Gait Speed. The greatest effect on V̇O2peak, 6MWT and 10 m Usual Gait Speed was observed after HIIT and HICT. No differences between interventions were found for SBP. SUCRA values identified HIIT as the superior AE intervention for all outcomes of interest. HIIT was the most effective intervention for improving V̇O2peak (2.9 mL/kg/min (95% credible interval 0.8 to 5.0) moderate certainty) compared with usual care.

CONCLUSION

This NMA suggests that higher-intensity AE is superior to traditional low-intensity to moderate-intensity AE for improving outcomes after stroke.

Exercise Metabolome: Insights for Health and Performance

Exercise has many benefits for physical and mental well-being. Metabolomics research has allowed scientists to study the impact of exercise on the body by analyzing metabolites released by tissues such as skeletal muscle, bone, and the liver. Endurance training increases mitochondrial content and oxidative enzymes, while resistance training increases muscle fiber and glycolytic enzymes. Acute endurance exercise affects amino acid metabolism, fat metabolism, cellular energy metabolism, and cofactor and vitamin metabolism. Subacute endurance exercise alters amino acid metabolism, lipid metabolism, and nucleotide metabolism. Chronic endurance exercise improves lipid metabolism and changes amino acid metabolism. Acute resistance exercise changes several metabolic pathways, including anaerobic processes and muscular strength. Chronic resistance exercise affects metabolic pathways, resulting in skeletal muscle adaptations. Combined endurance-resistance exercise alters lipid metabolism, carbohydrate metabolism, and amino acid metabolism, increasing anaerobic metabolic capacity and fatigue resistance. Studying exercise-induced metabolites is a growing field, and further research can uncover the underlying metabolic mechanisms and help tailor exercise programs for optimal health and performance.

Serum Metabolome Adaptations Following 12 Weeks of High-Intensity Interval Training or Moderate-Intensity Continuous Training in Obese Older Adults

Physical activity can be effective in preventing some of the adverse effects of aging on health. High-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) are beneficial interventions for the quality of life of obese older individuals. The understanding of all possible metabolic mechanisms underlying these beneficial changes has not yet been established. The aim of this study was to analyze changes in the serum metabolome after 12 weeks of HIIT and MICT in obese older adults. Thirty-eight participants performed either HIIT (n = 26) or MICT (n = 12) three times per week for 12 weeks. Serum metabolites as well as clinical and biological parameters were assessed before and after the 12-week intervention. Among the 364 metabolites and ratio of metabolites identified, 51 metabolites changed significantly following the 12-week intervention. Out of them, 21 significantly changed following HIIT intervention and 18 significantly changed following MICT. Associations with clinical and biological adaptations revealed that changes in acyl-alkyl-phosphatidylcholine (PCae) (22:1) correlated positively with changes in handgrip strength in the HIIT group (r = 0.52, p < 0.01). A negative correlation was also observed between 2-oxoglutaric acid and HOMA-IR (r = -0.44, p < 0.01) when considering both groups together (HIIT and MICT). This metabolite also correlated positively with quantitative insulin-sensitivity check index (QUICKI) in both groups together (r = 0.46, p < 0.01) and the HIIT group (r = 0.51, p < 0.01). Additionally, in the MICT group, fumaric acid was positively correlated with triglyceride levels (r = 0.73, p < 0.01) and acetylcarnitine correlated positively with low-density lipoprotein (LDL) cholesterol (r = 0.81, p < 0.01). These four metabolites might represent potential metabolites of interest concerning muscle strength, glycemic parameters, as well as lipid profile parameters, and hence, for a potential healthy aging. Future studies are needed to confirm the association between these metabolites and a healthy aging.

Metabolomics Analysis Reveals Alterations in Cochlear Metabolic Profiling in Mice with Noise-Induced Hearing Loss

Noise-induced hearing loss (NIHL) has always been an important occupational hazard, but the exact etiopathogenesis underlying NIHL remains unclear. Herein, we aimed to find metabolic biomarkers involved in the development of NIHL based on a mouse model using a gas chromatography coupled with mass spectrometry (GC-MS) metabolomics technique. We showed that the auditory brainstem response (ABR) thresholds at the frequencies of 4, 8, 12, 16, 24, and 32 kHz were all significantly elevated in the noise-exposed mice. Noise could cause outer hair cell (OHC) loss in the base of the cochlea. A total of 17 differential metabolites and 9 metabolic pathways were significantly affected following noise exposure. Spermidine acting as an autophagy modulator was found to be 2.85-fold higher in the noise-exposed group than in the control group and involved in β-alanine metabolism and arginine and proline metabolism pathways. Additionally, we demonstrated that LC3B and Beclin1 were expressed in the spiral ganglion neurons (SGNs), and their mRNA levels were increased after noise. We showed that SOD activity was significantly decreased in the cochlea of noise-exposed mice. Further experiments suggested that SOD1 and SOD2 proteins in the SGNs were all decreased following noise exposure. The upregulation of spermidine may induce LC3B- and Beclin1-mediated autophagy in the cochlear hair cells (HCs) through β-alanine metabolism and arginine and proline metabolism and be involved in the NIHL. ROS-mediated oxidative damage may be a pivotal molecular mechanism of NIHL. Taken together, spermidine can be regarded as an important metabolic marker for the diagnosis of NIHL.

Associations Between Time After Stroke and Exercise Training Outcomes: A Meta-Regression Analysis

Background Knowledge gaps exist regarding the effect of time elapsed after stroke on the effectiveness of exercise training interventions, offering incomplete guidance to clinicians. Methods and Results To determine the associations between time after stroke and 6-minute walk distance, 10-meter walk time, cardiorespiratory fitness and balance (Berg Balance Scale score [BBS]) in exercise training interventions, relevant studies in post-stroke populations were identified by systematic review. Time after stroke as continuous or dichotomized (≤3 months versus >3 months, and ≤6 months versus >6 months) variables and weighted mean differences in postintervention outcomes were examined in meta-regression analyses adjusted for study baseline mean values (pre-post comparisons) or baseline mean values and baseline control-intervention differences (controlled comparisons). Secondary models were adjusted additionally for mean age, sex, and aerobic exercise intensity, dose, and modality. We included 148 studies. Earlier exercise training initiation was associated with larger pre-post differences in mobility; studies initiated ≤3 months versus >3 months after stroke were associated with larger differences (weighted mean differences [95% confidence interval]) in 6-minute walk distance (36.3 meters; 95% CI, 14.2-58.5), comfortable 10-meter walk time (0.13 m/s; 95% CI, 0.06-0.19) and fast 10-meter walk time (0.16 m/s; 95% CI, 0.03-0.3), in fully adjusted models. Initiation ≤3 months versus >3 months was not associated with cardiorespiratory fitness but was associated with a higher but not clinically important Berg Balance Scale score difference (2.9 points; 95% CI, 0.41-5.5). In exercise training versus control studies, initiation ≤3 months was associated with a greater difference in only postintervention 6-minute walk distance (baseline-adjusted 27.3 meters; 95% CI, 6.1-48.5; fully adjusted, 24.9 meters; 95% CI, 0.82-49.1; a similar association was seen for ≤6 months versus >6 months after stroke (fully adjusted, 26.6 meters; 95% CI, 2.6-50.6). Conclusions There may be a clinically meaningful benefit to mobility outcomes when exercise is initiated within 3 months and up to 6 months after stroke.

Metabolomics in Exercise and Sports: A Systematic Review

BACKGROUND

Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed.

OBJECTIVE

This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics.

METHODS

We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020.

RESULTS

Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies.

CONCLUSION

Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.