γ-Aminobutyric acids (GABA) and serum GABA/AABA (G/A) ratio as potential biomarkers of physical performance and aging

doi:10.21203/rs.3.rs-2492780/v1

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γ-Aminobutyric acids (GABA) and serum GABA/AABA (G/A) ratio as potential biomarkers of physical performance and aging

https://doi.org/10.21203/rs.3.rs-2492780/v1

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Background: Physical activity maintains musculoskeletal and general overall health. Declining physical performance is the most obvious indicator of declining health with aging. Early identification of declining physical performance using biomarkers would be useful in predicting treatment outcomes and identifying potential therapeutics.

Method: As γ-aminobutyric acid (GABA), a muscle autocrine factor, can function as muscle relaxant and L-α-aminobutyric acid (L-AABA), is a biomarker for malnutrition, and depression, we sought to determine if they may also be useful for monitoring physical performance during aging. Serum levels were quantified in 120 individuals divided by age, gender, and physical capacity into low (LP), average (AP), and high (HP) performers and correlated with physical parameters and performance.

Results: Both GABA and the ratio of GABA/AABA (G/A), but not AABA, were highly positively associated with age (Pearson correlations r=0.35, p=0.0001 for GABA, and r=0.31, p=0.0007 for G/A, n=120). GABA showed negative associations in the whole cohort with physical performance (fast gait speed, 6 minute walking test (6MWT), PROMIS score, and SF36 PFS raw score) and with subtotal and femoral neck bone mineral density (BMD). L-AABA was positively associated with usual gait speed, 6MWT, total SPPB score, and SF36 PFS raw score in total 120 human subjects, also with 6MWT and SF36 PFS raw score in the 60 male subjects, but no associations were observed in the 60 females. As both GABA and L-AABA appear to be indicative of physical performance, but in opposite directions, we examined the G/A ratio. Unlike GABA, the G/A ratio showed a more distinct association with mobility tests such as total SPPB score, usual and fast gait speed, 6MWT, and SF36 PFS raw score in the males, regardless of age and metabolic status. Serum G/A ratio could be potentially linked to physical performance in the male population.

Conclusions: All these findings strongly suggest that GABA, L-AABA, and the G/A ratio in human serum may be useful markers to link with age and physical function. Taking these aminobutyric acids into consideration may significantly enhance the goal of identifying universal biomarkers to accurately predict physical performance and the beneficial effects of exercise on aging.

muscle

physical performance

biomarkers

In the United States with the increase of life expectancy, the population with an age above 65 will be 20 – 25 % of the total population by 2050 (1). Aging is believed to be a combination of body deterioration and disease (2). The gradual process of bodily deterioration, inevitable changes in cellular structure and function, that takes place throughout the life is known as primary aging. Secondary aging is the process resulting from disease, lifestyle and environmental influences, and is often preventable through lifestyle choice or modern medicine (2). Aging has deleterious effects on muscle and bone, thus reducing physical performance (3) and increasing frailty, risk of fall and fractures, leading to chronic disability to the elderly population (4). The human body consists of more than 500 muscles, regulated by the nervous systems to interact with the skeleton (5). Their synergistic actions allow standing and moving – physical activity, and leads energy consumption (6), and this physical activity, exercise, is well-established as a countermeasure against secondary aging (7). Exercise has many beneficial effects on the cardiorespiratory system and muscle mass/function, preventing age-associated insulin resistance and diminished mitochondrial capacity in skeletal muscle (8). But the mechanisms behind this beneficial effect on skeletomuscular health are still unclear. Therefore, there is an unmet clinical need for biomarkers to detect the effects of physical activity on muscle performance in order to identify new strategies to achieve healthy aging.

Biomarkers have been postulated as essential variables to measure the effects of physical activity on the human body. Generally, these biomarkers are related with performance (muscle status, endocrine response, and oxygen transport), health (nutritional and hydration status, allergies), and recovery (inflammation, injury status and risk, muscle damage) (9). Factors with endocrine, autocrine, and paracrine action secreted during physical activity, including myokines and metabolites, can serve as potential biomarkers for muscle physical performance. Although some published studies have found associations between physical activity, performance, and biomarkers in cohorts with different age, race, and physiological or pathological conditions (10-14), there is still a need for additional universal biomarkers, useful for generalized populations. For example, a published study performed in a cross-sectional population-based sample reported that physical activity and performance are associated with lower levels of inflammatory biomarkers (C-reactive protein, interleukin-6, and fibrinogen) in plasma from the elderly population (aged 65 years or older) (14). However, only the 400-meter walking test was used in this study as a performance-based measurement, and the levels of physical activity were classified based on self-reported information which limits the validity and strength of this study.

Aminobutyric acids are nonproteinogenic amino acids which may be released during physical activity and impact physical performance. They comprise three types of isomers: α-aminobutyric acid (2-aminobutyric acid, AABA), β-aminobutyric acid (3-aminobutanoic acid, BABA), β-aminoisobutyric acid (3-aminoisobutyric acid, BAIBA), and γ-aminobutyric acid (4-aminobutyric acid, GABA). Except for GABA, each isomeric aminobutyric acid has two mirror-image enantiomers with the same molecular structures but significantly different biological functions. Our research laboratory has developed a fast and sensitive method for complete separation and accurate measurement of the aminobutyric acids in biological samples using liquid chromatography with tandem mass spectrometry (LC–MS/MS) (15).

AABA has been postulated to have antioxidant activity and found to be a biomarker for alcohol liver injury, sepsis, malnutrition, and depression (16-19). L-AABA was further reported as the only identified enantiomer in human and mouse biological fluid samples including serum, plasma, and cerebrospinal fluid, and showed significant association with expression levels of UPB1, a gene encoding β-ureidopropionase belonging to the CN hydrolase family and significant association with BMD (15). GABA is present in bacteria where it is functionally involved in spore germination, and it also confers resistance to acidic pH to several bacteria (20) in fungi where it works as a source of carbon and nitrogen (21) and in plants where it plays hey roles in growth and development (22). In humans, it is a major inhibitory neurotransmitter in the central nervous system that regulates internal neuronal communication but also acts as a muscle relaxant during sleep and in setting overall muscle tone (23). It is also an antioxidant and anti-inflammatory amino acid (24). Our previous studies have shown that GABA has potential for early prevention of post-menopausal osteoporosis (15).

In this study, serum concentrations of GABA, L-AABA, and their ratio were quantified from 120 healthy non-Hispanic human subjects (aged between 20-85 years) with completed information of physical characteristics and performance. We identified associations between serum levels and physical performance assessments, and further investigated the potential of these aminobutyric acids to be used as biomarkers of physical performance and age. These markers can also potentially serve as the cornerstone to investigate new therapies for aging-related musculoskeletal diseases.

2.1 Recruitment of human subjects

Serum samples and data were obtained from 120 individuals who had visited the Musculoskeletal Function, Imaging, and Tissue Resource Core (FIT Core) of the Indiana Center for Musculoskeletal Health’s Clinical Research Center (Indianapolis, Indiana) between 3/2018 and 4/2019. The FIT Core serves to provide: 1) standardized performance of physical function tests and patient reported outcomes related to physical function, 2) imaging outcomes for body composition and bone health, and 3) the collection and banking of biological samples within the Indiana Biobank.

Participants are recruited to the FIT Core by investigators seeking outcomes related to musculoskeletal health for their research subjects, as well as via self-referral from the local community. The Core has Institutional Review Board approval from Indiana University to test all-comers who provide written informed consent, irrespective of current or previous health status.

The FIT Core collected samples and data from 1,518 individuals between 3/2018 and 4/2019. To be included in the current analyses, individuals needed to be 20-85 years of age, self-reported white and non-Hispanic, and without a self-reported major chronic disease. Individuals within each sex and each 15 year age group (20-35, 35-50, 50-65, and 65+ yrs) were ranked for their performance on the FIT Core’s hand grip strength test and test of the number of chair stands completed in 30 seconds. The 5 individuals within each sex and age range with the lowest, average, and highest composite rank were selected and grouped as low (LP), average (AP), and high (HP) performers, respectively (Table 1). The detailed characteristics of the study subjects shown in Supplementary Table 1.

2.2 Physical function

The FIT Core assessed dominant hand grip strength (Jamar Plus+ digital hand dynamometer; Sammons Preston, Bolingbrook, IL), the number of chair stands completed in 30 seconds, and the time taken to complete 5 chair stands, as we have previously described (25). In addition to raw values, grip strength and repeat chair stand outcomes were converted to age- and sex-matched z scores relative to reference data obtained in the FIT Core (25). Time to walk 4-m from a stationary start at normal speed (usual gait speed) and as quickly as possible without running (fast gait speed) were measured with a stopwatch and converted to speed (m/s), as we have previously reported (26).

Results from the repeat chair stand, usual gait speed, and a static balance test (ability balance for 10 seconds with feet side-by-side, semi-tandem, and tandem) were used to calculate the Short Physical Performance Battery (SPPB) score out of 12 (27). Distance walked in 6 minutes was measured according to the American Thoracic Society (28). The physical function (PF) domain of the NIH Patient Reported Outcomes Measurement Information System (PROMIS) computerized adaptive test (CAT) (PROMIS-CAT-PF) (version 1.2) and the physical functioning subscale of the Short Form 36 (SF-36 PF) were used to assess self-reported functional health.

2.3 Body composition and bone health

Appendicular skeletal muscle mass relative to height (ASM/height²; kg/m²) and whole-body aBMD, fat mass, percent were assessed by whole-body dual-energy x-ray absorptiometry (DXA) (Norland Elite; Norland at Swissray, Fort Atkinson, WI). Regional DXA using the same scanner assessed hip and spine aBMD.

2.4 Chemicals and Reagents

Aminobutyric acid standard compounds (S)-2-aminobutyric acid (L-AABA) and (R)-2-aminobutyric acid (D-AABA) were purchased from Thermo Fisher Scientific (Waltham, MA), and 4-aminobutyric acid (GABA) was purchased from Sigma-Aldrich (St. Louis, MO). Isotopic internal standard (IS) compounds DL-2-aminobutyric-2,3,3,4,4,4-d₆ acid (D,L-AABA-d₆) and (±)-3-amino-iso-butyric-2,3,3-d₃ acid (D,L-BAIBA-d₃) were obtained from CDN Isotopes (Pointe-Claire, Quebec, Canada), and 4-aminobutyric-d₆ acid (GABA-d₆) was purchased from Sigma-Aldrich (St. Louis, MO). Formic acid (reagent grade, ≥ 95%), Bovine Serum Albumin (BSA) were obtained from Sigma–Aldrich (St. Louis, MO). Phosphate Buffered Saline (PBS) was purchased from Fisher Scientific (Pittsburgh, PA). HPLC-MS grade acetonitrile, water, and methanol were purchased from J.T. Baker (Phillipsburg, NJ).

2.5 LC-MS/MS conditions

All components of LC-MS/MS system are from Shimadzu Scientific Instruments, Inc. (Columbia, MD). the LC system was equipped with pumps A and B (LC-30AD), and autosampler (SIL-30AC). The LC separation was conducted on a chiral SPP-TeicoShell column (150 × 4.6 mm, 2.7 µm, AZYP LLC., Arlington, TX) configured with a Synergi™ 4 µm Max-RP column as guard column (50 × 2.0 mm, Phenomenex, Torrance, CA). The MS/MS analysis was performed on Shimadzu LCMS-8050 triple quadrupole mass spectrometer.

Quantification of isomeric aminobutyric acids in human serum samples was followed the LC-MS/MS method published by our laboratory (15). Briefly, mobile phases are methanol (A) and water containing 0.005% formic acid and 2.5 mM ammonium formate (B). The MS instrument was operated and optimized under positive electrospray (+ESI) and multiple reaction monitoring modes (MRM). The m/z transitions (precursor to product ions) and their tuning voltages were selected from published paper (15) and further optimized based on the best MRM responses from instrumental method optimization software. All analyses and data processing were completed on Shimadzu LabSolutions V5.91 software (Shimadzu Scientific Instruments, Inc., Columbia, MD).

2.6 Sample Preparation for LC-MS/MS analysis

Ten microliter human serum samples and equal volume of IS mixture solution (1.2 µM, 0.1% formic acid in methanol, v/v) were added to 35 µL 0.1% (v/v) formic acid in methanol, followed by 20 min-shaking at room temperature and another 15 min-centrifugation at 15,000 ×g, 4°C to precipitate the proteins. The supernatant was directly transferred to autosampler vial and 45 μL of each sample was injected for LC-MS/MS analysis.

The samples of standard calibration curves were prepared by spiking the pure standards in surrogate matrix 5% (w/v) BSA in PBS (pH7.4). The samples for ten-point calibration curves were prepared by diluting the working solution to 0.02-10.24 µM for GABA, and 0.08-81.92 µM for AABA. Then ten microliters of each standard sample were taken and treated following the same preparation procedures of serum samples for LC-MS/MS analysis.

2.7 Statistical analysis

Data were summarized as mean ± SD. Comparisons among groupss were performed using Student’s t-test and one-way ANOVA with Tukey’s post-Hoc test (α=0.05). Association analysis was performed using both Pearson (r) correlations and Spearman (ρ) correlations, while scatter plots were used to decide the appropriate correlations for interpretations. To control for the effects of age and BMI, partial correlations were further calculated. SAS 9.4 (SAS Institute, Cary, NC, USA) was used for statistical analysis. Two-sided p-values <0.05 were considered as significant.

3.1 Quantification of GABA and AABA in human serum and correlations with age

Quantitation of the aminobutyric acids GABA and L-AABA from all 120 human samples gave concentrations of 0.146 ± 0.035 µM, and 20.6 ± 7.3 µM, respectively (Figure 1). Concentrations of GABA and L-AABA, and the ratio of GABA to L-AABA levels (G/A ratio) are summarized in Table 2. Next serum levels were analyzed comparing age, gender, and physical parameters. Serum GABA levels significantly increased with age in the overall cohort (p=0.0008, n=120) and HP group (p=0.0025, n=40). The average serum levels of GABA in each age group were 0.131 µM (20-35 yrs), 0.142 µM (35-50 yrs), 0.145 µM (50-65 yrs), and 0.166 µM (65 yrs and up), respectively. These results indicate a substantial increase in the serum levels of GABA at age 65, which is significantly higher than that in the younger populations aged 20-35 yrs group (p=0.00038) and 35-50 yrs group (p=0.032) (Figure 2A). Similar results were also obtained when calculating G/A ratio in serum. Average values of serum G/A ratio in different age groups were 0.0069 for 20-35 yrs, 0.0074 for 35-50 yrs, 0.0075 for 50-65 yrs, and 0.0097 for 65 yrs and up, respectively, and significant differences were also observed between the most senior group (age 65+) and other younger age groups (p<0.01, Figure 2B). No significant gender-specific differences (p < 0.05) were observed (Table 3).

Next both Pearson and Spearman correlation tests were applied to investigate the associations of GABA and AABA serum levels with age in all 120 and on men and women populations (n=60) separately. GABA showed a significant and positive association with age in the overall cohort (r = 0.349, ρ = 0.317, both p < 0.001). Furthermore, this association is stronger in women with increased correlation coefficients, but weaker in men with reduced correlation coefficients (Figure 3). No significant association was obtained between serum L-AABA levels with age. The serum G/A ratio, exhibited similar positive associations with age as GABA (Figure 3). A significant and positive correlation was observed between serum G/A ratio and age in the overall cohort (r = 0.305, p=0.00072; ρ = 0.273, p = 0.0025) and in men (r = 0.369, p=0.0037; ρ = 0.312, p = 0.016), but it becomes much weaker in women (r = 0.231, p=0.076; ρ = 0.251, p = 0.053).

3.2 Significant correlations between aminobutyric acids with physical parameters and physical performance

All associations of GABA and AABA with p-values < 0.1 for physical performance and parameters are summarized in Table 3. Whereas GABA has positive association with age, it showed negative associations with physical performance (fast gait speed, 6MWT, PROMIS score, and SF36 PFS raw score) in whole cohort. This suggests that elevated physical performance might link with reduced serum GABA levels. GABA levels in serum also negatively correlated with two BMD values (subtotal, and femoral neck) in 120 human subjects. Furthermore, remarkable gender-related differences were observed when investigating the associations between GABA levels and physical characteristics and performance as GABA was only correlated negatively with femoral neck BMD in female subjects.

As GABA significantly associated with age (Pearson r=0.3486, p=0.0001; Spearman ρ=0.3168, p=0.0004 Table 3, Supplementary Figure 1), and it is well known that both age and BMI are highly associated with physical performance, BMD values, and lean/fat mass in both genders (Supplementary Table 2), we further assessed these correlations after controlling the effect of age and/or BMI by partial correlation tests. The results summarized in Table 4, Supplementary Figure 2 indicating that associations between serum GABA levels and physical performance scales, BMDs, and lean/fat mass values diminished in partial correlation tests in all physical capacity groups. Only two performance scales, best grip strength in the male HP group (Pearson and Spearman) and SF36 PFS raw score in male AP group (Pearson), showed negative associations with GABA levels after controlling the effects of age and BMI (Table 4, Supplementary Figure 2).

3.3 Significant positive correlations between AABA with physical parameters and physical performance, but not with age.

L-AABA also exhibited associations with physical performance, but unlike GABA, all associations of L-AABA are positive. L-AABA is positively associated with usual gait speed, 6MWT, total SPPB score, and SF36 PFS raw score in 120 human subjects, also with 6MWT and SF36 PFS raw score in the 60 male subjects, but no correlations were observed in the 60 female participants (Table 3, Supplementary Figure 1). Interestingly, L-AABA showed more noteworthy gender-dependent differences in the individuals with high (HP) and low physical performance (LP) based on the results of physical performance assessment. Results summarized in Table 5 show that L-AABA positively associated with usual gait speed, fast gait speed, 6MWT, total SPPB score, and SF36 PFS raw score in 80 subjects from HP and LP groups, and stronger positive associations were found in males from both young (20-50 years old) and the aged (50-85 years old) populations. For example, associations (Pearson correlation) with 6MWT in all participants (HP and LP) is 0.236 (p = 0.038, n = 80) for both genders and 0.387 (p = 0.015, n = 40) for men. In the young participants (HP and LP), associations with 6MWT are 0.290 (p = 0.069, n = 40) for both genders and 0.439 (p = 0.053, n = 20) for men. In addition, almost no correlations between L-AABA and physical performance scales were observed in females in the HP group. All these results suggest that the serum levels of both GABA and L-AABA might be indicative of physical performance, but in opposite directions, which together could make them extremely useful biomarkers.

3.4 Correlations between serum G/A ratio with physical parameters and performance

As the associations of serum levels of GABA and L-AABA with human physical capacity were found to be opposite, we proposed that the ratio of GABA and L-AABA levels in serum could potentially serve as a more precise predictor of physical performance. Thus, we explored the relationships of serum G/A ratio with physical parameters in males and females separately, by using both Pearson and Spearman correlation tests (Table 6, Figure 4). Serum G/A ratio in males was found to be negatively associated with 6 physical performance scales, including total SPPB score (r = -0.401, p = 0.002), usual gait speed (r = -0.334, p = 0.009), fast gait speed (r = -0.344, p = 0.007), 6MWT (r = -0.399, p = 0.002), and SF36 PFS raw score (r = -0.515, p < 0.0001). Additionally, serum G/A ratios negatively correlated with femoral neck BMD and total lean mass, and positively correlated with total fat percent in men (Table 6, Figure 4). However, no associations were observed for serum G/A ratio with any physical performance and physical parameters but age and BMI in females. These results indicate that serum G/A ratio could be potentially linked to physical performance in the male population. Then we further performed partial Pearson and partial Spearman tests to evaluate the correlations of serum G/A ratio with physical parameters after controlling the effect of age and/or BMI. Pearson correlation results revealed that 5 out of 6 statistically significant associations without the covariate adjustments (ordinary) still have significantly negative associations after controlling the effect of age and age + BMI (partial) in the male population (Table 6, Figure 4). All associations with BMD and lean/fat mass diminished greatly in partial correlation tests. This suggests that serum G/A ratios might negatively associate with physical performance in men, no matter the age and or metabolic status. A positive Spearman and Pearson association of G/A ratio with the total fat percentage (ρ 0.56, p 0.01, and r 0.616, p 0.004) and negative with the total lean mass (ρ -0.52, p=0.02 and r -0.518, p=0.019) was found in the subgroup of HP males, and still present after controlling the effect of age and age + BMI (partial). This group also had a negative Pearson correlation with the grip strength, PROMIS score and SF36 PFS raw score. The LP male group similarly showed a negative Pearson correlation with the physical parameters of fast gait speed, SF36 PFS raw score and total SPPB score. The HP female group had a negative Pearson and Spearman correlation with the usual gait speed and the LP female group with the PROMIS score.

Aging muscle undergoes several morphological changes along with declining regenerative capacity, which affects muscle strength and physical performance (8). As the inability to modulate corticospinal excitability has been linked to declined motor performance with advancing age (29), probing the roles GABA, a main inhibitory neurotransmitter in brain, in the age related musculoskeletal disorders is significant and can lead to new therapeutic venues for these diseases, including sarcopenia. GABA is a highly conserved molecule from bacteria to humans with essential roles in all kingdoms (20-22). It has a dual action on neurotransmission in the mammalian central nervous system via two distinct classes of GABA receptors, ionotropic GABA-A and metabotropic GABA-B receptors, which differ in their pharmacological, electrophysiological, and biochemical properties (30). GABA possesses an excitatory effect mediated by GABA-A receptors, while activation of presynaptical GABA-B receptors impedes opening of voltage-activated Ca²⁺ channels and reduces neurotransmitter release, thus GABA can inhibit its own release through a negative feedback loop via GABA-B receptors present on GABAergic axons (31). This dual action of GABA could be dose-dependent and inhibitory at low concentrations and excitatory to some GABA receptors at higher concentrations (32). Currently most studies on aging -related GABA changes focus on altered brain GABA levels (33, 34), while the associations between circulating GABA levels, age, and aging-related physical performance are seldom reported. Age related CNS GABA disorders are noteworth and include: Parkinson’s disease, Alzheirmer’s disease, anxiety, depression, schiszophrenia, autism spectrum disorder, etc (35). Another very large group of diseases that finds its cause in defects in GABA metabolism are spasticity disorders. It is interesting that these diseases possess a very large peripheral muscle component to their pathophysiology. Since GABA is an isomer of BAIBA and we have reported that ex vivo contracting EDL and soleus muscles release BAIBA in a myokine fashion (36) , and C2C12 muscle cells release both BAIBA and GABA (37), and GABA has been considered to have limited ability to cross well the blood brain barrier, in humans it is feasible that to assume that GABA measured in serum derives mostly from skeletal muscles.

In the 120 serum samples collected from healthy humans covering both genders, from young to the elderly, and with different physical capacities, we demonstrated that GABA levels in the circulation are significantly associated with age. It increases consistently with aging in both men and women, however a positive association with physical activity was also observed but only in the female HP and female LP groups. The G/A ratio could be another good biomarker for aging with the same positive, but stronger correlations than GABA. Interestingly, when considering physical performance, a significant serum G/A ratio was only obtained in the male HP and male LP groups. This gender-related difference between GABA and the G/A ratio may come from L-AABA. Even though L-AABA levels did not show any associations with the age in the whole cohort, it was found to be negatively correlated with age in HP and LP males. Taking both blood GABA levels and G/A ratios into consideration may significantly increase the strength of studies to discover more universal aging biomarkers in the general population.

Skeletal muscle physical performance is regulated by factors associated with the nervous, muscular, and skeletal systems. It has been well studied that muscle function is significantly related to the measures of BMD (femoral neck BMD, spine BMD, hip BMD, etc.) and body composition (fat percentage, lean mass, etc.) (38-40). Aging-related muscle function loss is often accompanied by an increased fat mass and percentage along with reduced lean mass and BMD. Our group has recently reported that two genes coding for the GABA receptors GABBR2 and GLRA1 and the gene coding for glutamate decarboxylase GAD1, showed an association with total body BMD, and that GABBR2 is associated with bone fractures (15). These genes are involved in both bone and muscle function and likely further influence overall musculoskeletal function and body metabolism (15). In the present study, femoral neck BMD was found to be negatively correlated with GABA in the whole cohort and female group, and with G/A ratio in male group. When physical performance was taken into consideration, a strong negative association was observed between GABA and spine BMD in female HP, while the G/A ratio negatively associated with femoral neck BMD in male AP and LP groups. Moreover, serum GABA levels and G/A ratios also exhibited generally negative associations with lean mass and positive associations with fat mass/percentage in the different physical performance groups.

GABA levels were also negatively associated with various physical performance scales in our study, except for the 5min-chair-stand. But this positive association with the chair-stand further confirmed that serum GABA concentrations increased when the physical performance declines. This negative correlation between serum GABA levels and physical performance might be age-related, as the results of further partial association tests indicated that these correlations between GABA and performance scales diminished when controlling for the effects from age. All these findings suggest that aging is a key factor affecting GABA levels in circulation.

Even though no direct significant positive correlations were observed between GABA and physical performance assessments, GABA levels in the body are known to be affected by physical activity. Exercise can stimulate PGC1α-mediated GABA secretion from muscle (41). GABA concentrations in systemic circulation, gastrocnemius and quadriceps were increased in exercising mice and humans, suggesting that GABA may function as an exercise/PGC1α-mediated myokine (41). Exercise also increases GABA levels in brain (42). Our previous studies in 136 young to middle-aged healthy Caucasian women (aged 21-41 years) reported that serum GABA showed a positive association (ρ=0.31, p=0.0055) with physical activity (times per week) in lean women (BMI 18.5-24.9 kg m^-2), but no association in obese women with BMI ≥ 25.0 kg m^-2 (15). This might be caused by a differential metabolomic profile of GABA in muscle and adipose tissues, indicating that metabolic homeostasis (anabolic/catabolic balance, hormone/protein/amino acid deficiencies, etc.) during aging may affect GABA levels in the circulation. In the present study, regardless of the influence of age, serum GABA concentrations in the HP group were higher than that in AP and LP groups in the whole cohort. As the level of performers is classified according to performance scales on grip strength and repeat chair stand test, this result further supports a widely accepted concept that the higher physical performance is always linked to more dynamic physical activities. Exercise can increase myofiber size, whole muscle mass, muscle quality, and attenuates age-related decreases in muscle strength (8). Elevated GABA level in serum induced by physical activity may help enhance muscle hypertrophy, then assist muscle growth and maintain the physical performance during aging, and GABA also plays key roles in maintaining optimal muscle tonicity (32).

Unlike GABA levels, the associations between G/A ratio with physical performance assessments appear to be more independent from age. Particularly in the 60 male participants, 5 out of 6 negative associations with various physical performance tests of mobility (total SPPB score, usual gait speed, fast gait speed, 6 min walk test, and SF36 PFS raw score) were still significant after controlling for the effect of age using partial Pearson correlation analysis. SF36 PFS raw scores also showed medium to strong correlations (r-values ranged from -0.4 to -0.6) with serum GABA levels in all three male HP, AP and LP groups. The short Form-36 Physical Functioning Scale (SF36 PFS) questionnaire is a set of patient self-reporting measures to indicate health status. The SF-36 PFS is composed of 10 items encompassing a hierarchical range of difficulties on everyday activities, including vigorous activities, moderate activities, lifting/carrying, climbing stairs, bending/kneeling/stooping, and waling, etc., and it has been used as a reliable and valid “stand-alone” instrument in research describing activity limitations in the elderly living in the community, and patients with different minor pathophysiological conditions and chronic diseases (43-45). These results strongly suggest that G/A ratio could be a potential circulating biomarker to indicate change in physical performance, particularly mobility performance in men.

One of major limitations of published studies on the biomarkers for physical activity/performance is its cross-sectional nature. The direction of the causal pathways between physical activity and potential biological markers remains unclear. It is difficult to know if the altered muscle status (mass, strength, power, fatigue, etc.) is responsible for or affects the levels of biological factors in circulation, or vice versa. However, the significant associations with age in our study directly indicate that the change in serum GABA levels and G/A ratio in humans are caused by the aging progress.

Together, these observations suggest that serum aminobutyric acids, especially GABA and G/A ratio, are closely associated with aging-related physical performance. As both intrinsic (inflammation, apoptosis, mitochondria, calcium metabolism, etc.) and extrinsic (endocrine, nutritional status, immobility, etc.) factors contribute to progressively defective myogenesis and muscle dysfunction during aging, considering multiple serum biomarkers may more accurately predict the correlations among aging, physical performance, and the beneficial effects from exercise. These studies could also open up new therapeutical venues for muscle disorders such as stiff man syndrome and sarcopenia.

6MWT, six-minute walk test; AABA, α-aminobutyric acid; BMD, bone mineral density; BMI, body mass index; BSA, bovine serum albumin; ESI, electrospray Ionization; GABA, γ-aminobutyric acid; IS, Internal standard; LC, liquid chromatography; LC-MS/MS, liquid chromatography-tandem mass spectrometry; MRM, multiple reaction monitoring; MS, mass spectrometry; m/z, mass-to-charge ratio; PBS, phosphate-buffered saline; MSK, musculoskeletal; SD, standard deviation.

Ethics approval and consent to participate

Written informed consent was obtained prior any study procedure according to the declaration of Helsinki and Indiana University Institutional Review Board (IU – IRB, study number 1707550885). After the protocol consent form has been signed and dated by each study participant, 20 mL of blood was collected. Specimens were then aliquoted, prepared for storage and frozen at −80 °C until further analyses at the Indiana Biobank

Consent for publication

Not applicable

Availability of data and materials

All data generated and analyzed during this study are included in this published article and its supplementary information files.

Competing interests

The authors declare that they have no competing interests

Funding

This work was supported by: the National Institutes of Health [Grant P01AG039355 (LFB, MB)] and the National Institute of Arthritis and Musculoskeletal and Skin Disease (grant P30AR072581, PI Dr. Moe, Sharon M, SJW). We also acknowledge the Clinical Research Center (CRC) Indiana Center of Musculoskeletal Health (ICMH) Musculoskeletal Function, Imaging and Tissue Resource Core (MSK – FIT core), Indiana University School of Medicine, USA.

Authors’ Contributions

All authors contributed substantially to the preparation of this manuscript. MB and LB conceived the hypothesis for this study. SJW provided the 120 serum samples and measures of physical performance, BMI and BMD measures. MB and ZW contributed reagents, materials, or analysis tools. ZW was responsible for quantification of aminobutyric acids in serum samples using LC-MS/MS and data acquisition. ZW and ZL were responsible for data analysis. All authors interpreted the data. ZW and CL wrote the manuscript. MB, LB, and SW provided critical advice, discussed the work, and edited the manuscript. All authors revised and finalized the manuscript before submission.

Acknowledgements

We are grateful for instrumentation support from Shimadzu Scientific Instruments, Inc. We also acknowledge the Clinical Research Center (CRC) Indiana Center of Musculoskeletal Health (ICMH) Musculoskeletal Function, Imaging and Tissue Resource Core (MSK – FIT core), Indiana University School of Medicine, USA.

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Table 1. Characteristics on females and males by performance (low, average, high).

Table 2. Concentrations of aminobutyric acids GABA and L-AABA, and ratios of GABA/L-AABA (G/A ratio) in serum from different age or gender populations.

Physical performance

Aminobutyric acids

Whole cohort

Age group

Gender

20-35 yrs

35-50 yrs

50-65 yrs

65+ yrs

p-value

Female

Male

p-value

Overall

120

GABA (µM)

0.146 ± 0.035

0.131 ± 0.031

0.142 ± 0.023

0.145 ± 0.029

0.166 ± 0.043

0.0008***

0.150 ± 0.034

0.142 ± 0.035

0.248

L-AABA (µM)

20.6 ± 7.3

20.5 ± 6.2

21.8 ± 8.8

20.8 ± 7.3

19.2 ± 6.7

0.589

20.7 ± 7.3

20.5 ± 7.3

0.879

G/A ratio

0.0078 ± 0.0031

0.0069 ± 0.0024

0.0074 ± 0.0026

0.0075 ± 0.0022

0.0097 ± 0.0042

0.0021**

0.0079 ± 0.0030

0.0078 ± 0.0032

0.773

High (HP)

GABA (µM)

0.155 ± 0.040

0.139 ± 0.037

0.141 ± 0.021

0.147 ± 0.031

0.194 ± 0.044

0.0025**

0.158 ± 0.039

0.152 ± 0.041

0.653

L-AABA (µM)

21.4 ± 8.5

21.8 ± 5.4

21.4 ± 11.4

21.7 ± 9.5

20.4 ± 8.0

0.984

22.2 ± 10.4

20.5 ± 6.2

0.519

G/A ratio

0.0082 ± 0.0037

0.0066 ± 0.0017

0.0078 ± 0.0032

0.0075 ± 0.0025

0.0110 ± 0.0054

0.043*

0.0083 ± 0.0039

0.10082 ± 0.0036

0.904

Average (AP)

GABA (µM)

0.140 ± 0.034

0.128 ± 0.034

0.143 ± 0.022

0.143 ± 0.037

0.146 ± 0.043

0.675

0.150 ± 0.034

0.130 ± 0.032

0.063

L-AABA (µM)

20.0 ± 5.7

17.5 ± 5.0

22.7 ± 6.5

19.7 ± 4.5

20.1 ± 6.1

0.230

19.7 ± 5.2

20.3 ± 6.2

0.737

G/A ratio

0.0075 ± 0.0026

0.0079 ± 0.0031

0.0068 ± 0.0023

0.0075 ± 0.0021

0.0078 ± 0.0029

0.779

0.0081 ± 0.0026

0.0069 ± 0.0024

0.151

Low (LP)

GABA (µM)

0.143 ± 0.028

0.126 ± 0.024

0.143 ± 0.027

0.144 ± 0.022

0.158 ± 0.030

0.065

0.141 ± 0.026

0.145 ± 0.030

0.669

L-AABA (µM)

20.4 ± 7.5

22.2 ± 7.4

21.2 ± 8.7

21.1 ± 7.6

17.0 ± 6.1

0.431

20.1 ± 5.1

20.6 ± 9.4

0.821

G/A ratio

0.0078 ± 0.0029

0.0062 ± 0.0022

0.0074 ± 0.0022

0.0075 ± 0.0022

0.0102 ± 0.0036

0.012*

0.0075 ± 0.0022

0.0083 ± 0.0035

0.381

Each data represents mean ± SD. ANOVA was applied for statistics analysis. **p<0.01, ***p<0.001.

Table 3. Correlations of aminobutyric acids GABA and L-AABA with physical parameters and performance in 120 white non-Hispanic individuals (aged 20–85 years).

Gender	Parameters	n	GABA				L-AABA
			Pearson		Spearman		Pearson		Spearman
			r	p-value	ρ	p-value	r	p-value	ρ	p-value
Both	Age	120	0.3486	0.0001	0.3168	0.0004
	Height	120	-0.2269	0.0127	-0.2537	0.0052
	Usual gait speed	120					0.1566	0.0875
	Fast gait speed	120	-0.1706	0.0625	-0.2078	0.0228
	Six Min Distance	117			-0.1611	0.0828	0.2059	0.0259
	Total SPPB score	120					0.1534	0.0944
	PROMIS score	120			-0.1627	0.0759
	SF36 PFS raw score	120			-0.2407	0.0081	0.1957	0.0322
	Femoral neck BMD	117	-0.2175	0.0185	-0.2039	0.0275
Female	Age	60	0.4100	0.0011	0.4419	0.0004
Female	Femoral neck BMD	57	-0.2888	0.0294	-0.3051	0.0210
Male	Age	60	0.2884	0.0254	0.2788	0.0815			-0.3289	0.0383
	BMI	60			0.2381	0.0669
	Height	60	-0.3791	0.0028
	Six Min Distance	58					0.3353	0.0101	0.2501	0.0583
	PROMIS score	60	-0.2398	0.0650	-0.2338	0.0723
	SF36 PFS raw score	60			-0.3519	0.0058	0.2904	0.0244
	Femoral Neck BMD	60					0.2291	0.0783	0.3214	0.0123

Pearson correlations (r) and Spearman Rank correlations (ρ) with p-value < 0.1 are shown in the Table.

Table 4. Correlations of the GABA in 120 white non-Hispanic individuals (aged 20–85 years) with different gender.

Pysical Capacity Group	Gender	Variable	N	Pearson Correlations						Spearman Rank Correlations
				Ordinary		Partial on age		Partial on age, BMI		Ordinary		Partial on age		Partial on age, BMI
				r	p-value	r'	p-value	r'	p-value	ρ	p-value	ρ'	p-value	ρ'	p-value
Overall	Male	Age	60	0.2884	0.0254	-	-	-	-			-	-	-	-
		BMI	60					-	-	0.2381	0.0669			-	-
		SF36 PFS raw score	60							-0.3519	0.0058
		PROMIS score	60	-0.2396	0.0652					-0.2323	0.0741
	Female	Age	60	0.4100	0.0011	.	.	.	.	0.4419	0.0004	.	.	.	.
		Femoral neck BMD	57	-0.2888	0.0294					-0.3051	0.0210
		Total Lean Mass	60											0.2561	0.0592
High (HP)	Male	Best Grip Strength	20	-0.5316	0.0157	-0.4540	0.0508	-0.5366	0.0217	-0.4197	0.0654	-0.4014	0.0885	-0.4478	0.0624
		PROMIS score	20	-0.5046	0.0233					-0.4214	0.0642
		SF36 PFS raw score	20							-0.3919	0.0875
		Total Lean Mass	20	-0.3868	0.0920			-0.4342	0.0718	-0.3985	0.0818			-0.4175	0.0847
		Total Fat Percentage	20	0.4115	0.0714
	Female	Age	20	0.5426	0.0134	-	-	-	-	0.5594	0.0103	-	-	-	-
		Usual gait speed	20							-0.4778	0.0331
		Fast gait speed	20	-0.4843	0.0305					-0.4756	0.0341
		Time for 5 chair stands	20	0.4027	0.0784					0.4511	0.0459
		RCS 30s	20	-0.5030	0.0238					-0.4936	0.0270
		Spinal BMD	19	-0.5836	0.0087	-0.4275	0.0768	-0.4181	0.0949	-0.4695	0.0425
		Total Fat Mass	19					-0.4146	0.0980			-0.4483	0.0621	-0.5977	0.0113
Average (AP)	Male	Six Min Distance	19	-0.5418	0.0166	-0.5302	0.0236
		Fast gait speed	20	-0.3952	0.0869
		SF36 PFS raw score	20	-0.5722	0.0084	-0.5592	0.0128	-0.5040	0.0330
		Total Lean Mass	20					-0.4633	0.0528					-0.4899	0.0391
		Total Fat Mass	20	0.4500	0.0465	0.4466	0.0553			0.5158	0.0199	0.5206	0.0223	0.5030	0.0333
	Female	Time for 5 chair stands	20							0.4045	0.0769
	Female	SF36 PFS raw score	20							-0.4118	0.0712
Low (LP)	Female	Age	20	0.5999	0.0052	-	-	-	-	0.5940	0.0058	-	-	-	-
		PROMIS score	20							-0.4122	0.0709
		Total Lean Mass	18					0.4303	0.0962

Pearson correlations (r) and Spearman Rank correlations (ρ) with p-value < 0.1 are shown in the Table.

Table 5. Correlations of serum L-AABA levels in the combined extreme physical capacity groups–80 participants from both high and low performer groups, with physical parameters and performance scales.

Age group	Gender	N	Variable	Pearson		Spearman
Age group	Gender	N	Variable	r	p-value	ρ	p-value
All	Both	80	Total SPPB score	0.2232	0.0466
		80	Usual gait speed	0.2550	0.0224	0.2740	0.0139
		80	Fast gait speed	0.1855	0.0995
		78	Six Min Walk Test	0.2358	0.0377
		80	SF36 PFS raw score	0.2244	0.0454
	Male	40	Age			-0.3248	0.0409
		40	Total SPPB score	0.2684	0.0940
		40	Usual gait speed	0.2683	0.0941	0.3218	0.0429
		39	Six Min Walk Test	0.3871	0.0149	0.3226	0.0452
		40	SF36 PFS raw score	0.3322	0.0363
		40	Total % fat mass			-0.2984	0.0615
		40	Femoral neck BMD	0.3062	0.0547	0.3850	0.0142
Young (20-50 years)	Both	40	Six Min Walk Test	0.2902	0.0693
		38	Spinal BMD	0.3207	0.0496	0.3342	0.0403
		38	Femoral neck BMD	0.3165	0.0529	0.3253	0.0462
	Male	20	Six Min Walk Test	0.4385	0.0531
		20	Spinal BMD	0.4558	0.0434	0.4632	0.0397
		20	Femoral neck BMD	0.4048	0.0767	0.4556	0.0435
Aged (50-85 years)	Both	40	Usual gait speed	0.3968	0.0112	0.3070	0.0540
	Male	20	Usual gait speed			0.4283	0.0596
		20	SF36 PFS raw score	0.4155	0.0685
		20	Appendicular lean/height²	0.4418	0.0511	0.4782	0.0330
	Female	20	Usual gait speed	0.4866	0.0296	0.4108	0.0720

Pearson correlations (r) and Spearman Rank correlations (ρ) with p-value < 0.1 are shown in the Table.

Table 6. Correlations of the G/A ratio in 120 white non-Hispanic individuals (aged 20–85 years) with different gender.

Physical Capacity Group	Gender	Variable	N	Pearson Correlations						Spearman Rank Correlations
				Ordinary		Partial on age		Partial on age, BMI		Ordinary		Partial on age		Partial on age, BMI
				r	p-value	r'	p-value	r'	p-value	ρ	p-value	ρ'	p-value	ρ'	p-value
Overall	Male	Age	60	0.368	0.004	.	.	.	.	0.311	0.015	.	.	.	.
		Total SPPB score	60	-0.401	0.002	-0.315	0.015	-0.315	0.016
		Usual gait speed	60	-0.334	0.009	-0.275	0.035	-0.28	0.033	-0.291	0.024	-0.248	0.059	-0.242	0.067
		Fast gait speed	60	-0.344	0.007	-0.249	0.057	-0.25	0.059	-0.221	0.09
		Best Grip Strength	60	-0.243	0.062
		6 min walk test	58	-0.399	0.002	-0.299	0.024	-0.301	0.024	-0.317	0.015
		SF36 PFS raw score	60	-0.515	<.0001	-0.41	0.001	-0.416	0.001	-0.364	0.004	-0.251	0.055	-0.247	0.062
		Femoral neck BMD	60	-0.303	0.019					-0.378	0.003	-0.267	0.041	-0.272	0.039
		Total Lean Mass	60	-0.24	0.065
		Total Fat Percent	60	0.245	0.059					0.247	0.057
	Female	Age	60	0.231	0.076	.	.	.	.	0.251	0.053	.	.	.	.
	Female	BMI	60			.	.	.	.	-0.224	0.085	.	.	.	.
High (HP)	Male	Age	20	0.445	0.050	.	.	.	.			.	.	.	.
		BMI	20			.	.	.	.	0.383	0.096	.	.	.	.
		Usual gait speed	20							-0.449	0.047	-0.436	0.062
		Best Grip Strength	20	-0.474	0.035
		PROMIS score	20	-0.442	0.051
		SF36 PFS raw score	20	-0.540	0.014	-0.391	0.098
		Total Lean Mass	20	-0.518	0.019	-0.455	0.051	-0.573	0.013	-0.522	0.018	-0.467	0.044	-0.610	0.007
		Total Fat Mass	20	0.552	0.012	0.433	0.064			0.516	0.020	0.453	0.052
		Total Fat Percent	20	0.616	0.004	0.506	0.027	0.456	0.058	0.559	0.010	0.500	0.029	0.405	0.095
	Female	Usual gait speed	20	-0.453	0.045					-0.723	0.000	-0.680	0.001	-0.687	0.002
		6-Min Walk Test	20									0.505	0.027	0.448	0.062
		Total Fat Mass	19							-0.467	0.044	-0.487	0.040	-0.455	0.067
		BMI	60			.	.	.	.	-0.224	0.085	.	.	.	.
Average (AP)	Male	Six Min Distance	19	-0.413	0.079	-0.416	0.086	-0.418	0.095
		SF36 PFS raw score	20	-0.479	0.033	-0.486	0.035	-0.457	0.057
		Femoral neck BMD	20									-0.391	0.098
Low (LP)	Male	Age	20	0.488	0.029	.	.	.	.	0.496	0.026	.	.	.	.
		Total SPPB score	20	-0.680	0.001	-0.565	0.012	-0.575	0.013
		Usual gait speed	20	-0.450	0.047					-0.394	0.086
		Fast gait speed	20	-0.532	0.016	-0.420	0.073	-0.502	0.034	-0.423	0.063
		Six Min Distance	20	-0.488	0.029			-0.433	0.073	-0.415	0.069
		SF36 PFS raw score	20	-0.676	0.001	-0.547	0.015	-0.638	0.004	-0.435	0.055
		Femoral neck BMD	20	-0.408	0.075					-0.448	0.048
		Total Fat Mass	20			-0.395	0.094					-0.430	0.066
	Female	Age	20	0.420	0.066	.	.					.	.
		Total SPPB score	20	-0.400	0.081
		PROMIS score	20	-0.445	0.049					-0.457	0.043

Pearson correlations (r) and Spearman Rank correlations (ρ) with p-value < 0.1 are shown in the Table.

No competing interests reported.

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γ-Aminobutyric acids (GABA) and serum GABA/AABA (G/A) ratio as potential biomarkers of physical performance and aging

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