Identification of traumatic acid as a potential plasma biomarker for sarcopenia using a metabolomics‐based approach

Introduction

Sarcopenia, a major modifiable cause of geriatric frailty, is characterized by age-related involuntary loss of skeletal muscle mass, quality, and strength.1 The prevalence of sarcopenia was found to increase from 13–24% in persons younger than 70 years to >50% in persons older than 80 years.2 Sarcopenia is associated with poor balance, decreased motility and function, low gait speeds, and a greater risk of falls and fracture.3, 4 Thus, sarcopenia links poor muscle function to increased all-cause mortality rates in older people and represents a leading cause of disability, morbidity, and mortality.5 Sarcopenia is also associated with higher medical costs. For example, in the USA, the healthcare cost of sarcopenia was estimated to be $18.5bn in 2000.6 Accordingly, sarcopenia is a serious issue in geriatrics.

Several possible mechanisms have been suggested for sarcopenia; however, none could fully explain the underlying pathophysiology. Wasting of muscle mass and function is believed to be a complex and multifactorial process, including decreased physical activity, decreased energy intake, poor nutritional status, deteriorated immunity, and metabolic disturbances.7 Numerous factors have been identified to be associated with the pathogenesis of sarcopenia, including increased production of inflammatory cytokines (such as interleukin-6), vitamin D deficiency, insulin resistance, and growth hormone and insulin-like growth factor-1 deficiency, low plasma irisin, and elevated plasma growth differentiation factor 15 (GDF-15).8-11 From a translational perspective, the identification of potential biomarkers represents a promising strategy for exploring the mechanism of sarcopenia.12

Metabolomics is an approach to quantitatively measure the dynamic multi-parametric metabolic responses of living systems to pathophysiological stimuli.13 Metabolomics is instrumental for understanding the pathogenesis and diagnosis of diseases14 and has been recognized as a powerful tool for assessing complex disease mechanisms, such as systemic lupus erythematosus (SLE).15 Searching for potential markers of sarcopenia using the metabolomic approach is thus a reasonable strategy. Serum is often used as an object of metabolomic analysis as the serum metabolome is rich in various classes of important metabolites.16, 17 Two studies had adopted metabolomic approaches to identify certain plasma amino acids associated with muscle mass and quality in middle-aged UK female twins18 and older people from the Baltimore Longitudinal Study of Aging.19 A study also identified some metabolites associated with circulating interleukin-6 in older adults,20 and a recent study revealed the differences in skeletal muscle between nonsarcopenic and sarcopenic older adults using metabolomics.21 However, these studies did not compare the differences in plasma metabolites between elderly subjects with and without sarcopenia. As a result, the aim of this study was to identify potential biomarkers of sarcopenia through metabolomic analyses of the plasma metabolites in sarcopenic elderly subjects.

Methods Ethics statement

The study protocol was approved by the Ethics Committee of the National Taiwan University Hospital (Registration Number: 200701017R).22 Written informed consent was obtained from all participants before their inclusion in the study. The items included on the consent form were aims, inclusion and exclusion criteria, procedures, potential harm and benefit, medical care received, privacy and right of the participants, and the right to withdraw. All procedures were in accordance with the Declaration of Helsinki. Further, subjects that declined to participate or otherwise did not participate were assured that they would remain in the care of their family physician and would not be subjects to any disadvantages.

Subjects

Elderly subjects in this study were selected according to the criteria for sarcopenia from the Comprehensive Geriatric Assessment and Frailty Study of Elderly Outpatients.22, 23 All geriatric ambulatory outpatients (with chronic diseases) were eligible for recruitment if they had one of the following conditions: (i) functional decline (as measured by new disabilities of activity of daily living or instrumental activity daily living), (ii) geriatric syndromes (fall, weight loss, multiple co-morbidities, etc.), (iii) behavioural disorders (depression or dementia), (iv) expected high healthcare utilization, or (v) age 80 and older. Subjects who were bedridden, resided in nursing homes for long-term conditions, had a life expectancy of <6 months, and had impaired vision, hearing, or communication capacity were excluded.

All elderly participants were subjected to body composition examination by bioelectrical impedance analysis.23-25 In accordance with the characteristics of this bioelectrical impedance analysis model (Tanita BC-418, Tanita Corp., Tokyo, Japan), a constant high-frequency current (50 kHz, 500 μA) and an eight-contact electrode were employed to measure the body composition in segmental parts of the whole body, including both arms, legs, and the trunk area. The subjects dressed in light clothing, in a fasted state, and after voiding, were asked to stand on the analyser barefooted in close contact with the electrodes and grasp both hand holders as shown in the user's manual. Fat mass, fat-free mass, the predicted muscle mass of the appendicular fractions, and appendicular skeletal muscle mass (ASM) could be estimated by the sum of each segment, except for the ‘trunk part’, as validated previously.25 Of note, the appendicular skeletal muscle mass index (ASMI) could be estimated via this model (ASMI = ASM divided by squared height in metres). All examinations were conducted in compliance with the standard procedure.23, 24 Subjects on medical devices were excluded for safety concerns. Elderly subjects were divided according to the presence or absence of sarcopenia using the criteria of low muscle mass (narrow definition of sarcopenia) based on the norm of domestic young healthy adults.23, 26 The cut-off points of sarcopenia are 6.76 kg/m2 for men and 5.28 kg/m2 for women, as validated in previous studies.23, 25 Additionally, sex-matched and age-matched (within 5 year interval) elderly subjects without sarcopenia were selected as the control group. We randomly recruited the same number of adults who were younger than 65, consecutively received annual physical examinations at the outpatient clinics in the same hospital, and did not have malignancies, acute or chronic infections, to serve as another control group.

Data collection

Data were collected by experienced nurses using a structured questionnaire with the following: demographic information, diseases, smoking and drinking habits, current medication, geriatric syndromes, blood pressure level, and body mass index (BMI).22 Body weight and standing height were measured with subjects dressed in light clothing and barefooted.

Biochemical assays

Blood samples were obtained from the antecubital vein after an 8 h fast to measure complete blood count and biochemical analysis. Blood was immediately centrifuged to obtain plasma samples, which were subsequently frozen at −80°C until analysis. Plasma tumour necrosis factor-α (TNF-α) levels were measured using commercial enzyme-linked immunosorbent assay (ELISA) kits (Assaypro LLC, Saint Charles, MO, USA); the intraassay and interassay coefficients of variation were 5.6% and 7.5%, respectively. Plasma C-reactive protein (CRP) levels were measured using the latex agglutination test (Denka Seiken, Gosen, Niigata, Japan); the intraassay and interassay coefficients of variation were 4.0% and 8.5%, respectively. Plasma irisin levels were measured using commercial ELISA kits (Cell Biolabs, San Diego, CA, USA); the intraassay and interassay coefficients of variation were 7.8% and 6.2%, respectively. Plasma GDF-15 levels were measured using commercial ELISA kits (BioVendor, Karasek, Brno, Czechia); the intraassay and interassay coefficients of variation were 2.0% and 7.9%, respectively. All samples were measured according to the manufacturer's recommended procedures and were tested in duplicate.

Experimental method for metabolic analysis of plasma samples Chemicals

Mass spectrometry (MS)-grade water and methanol were purchased from Scharlau (Sentmenat, Spain). Acetonitrile was procured from J.T. Baker (Phillipsburg, NJ, USA). Formic acid of 99% concentration was obtained from Sigma-Aldrich (St. Louis, MO, USA).

Sample preparation

Plasma samples were stored at −80°C before use. Before extraction, the samples were thawed at room temperature. Four hundred microlitres of methanol was added to 100 μL of human plasma to extract metabolites from plasma. The extraction was performed using Geno/Grinder2010 (SPEX, Metuchen, NJ, USA) at 1000 r.p.m. for 2 min. Thereafter, the samples were centrifuged at 15 000 g for 5 min at 4°C. The extraction was repeated twice. Four hundred microlitres of the supernatant was collected and dried under nitrogen stream. For liquid chromatography (LC)–MS profiling, the dried extracts were reconstituted with 200 μL of 50% methanol and centrifuged at 15 000 g for 5 min. The supernatant was then filtered with a 0.2 μm Minisart RC4 filter (Sartorius Stedim Biotech GmbH, Göttingen, Germany). All aliquots were transferred to a glass insert for LC–MS analysis.

Metabolomic profiling

Metabolomic profiling via LC–MS was performed using the Agilent 1290 UHPLC system (Agilent Technologies, Santa Clara, CA, USA) coupled with Bruker maXis QTOF (Bruker Daltonics, Bremen, Germany). A 2 μL sample was injected into an Acquity HSS T3 column (2.1 × 100 mm, 1.8 μm) (Waters, Milford, MA, USA) maintained at 40°C. The mobile phase was composed of solvent A: water/0.1% formic acid and solvent B: acetonitrile/0.1% formic acid. The following gradient elution programme was employed: 0–1.5 min: 2% B; 1.5–9 min: linear gradient from 2% to 50% B; and 9–14 min: linear gradient from 50% to 95% B, and maintained at 95% B for 3 min. The flow rate was 300 μL/min. For sample ionization, an electrospray ionization source was employed with a capillary and endplate offset voltage of 4 K and 500 V, respectively, in both positive and negative modes. The MS parameters were set as follows: 200°C, drying gas temperature; 8 L/min, drying gas flow; and 2 bar, nebulizer flow. The mass spectrometer was calibrated with 5 mM sodium formate before daily use with lockmass between runs.

Plasma sample preparation for traumatic acid quantification

A 100 μL volume of plasma sample was extracted with 400 μL methanol. The extraction was performed via shaking at 1000 r.p.m. for 2 min using Geno/Grinder 2010 (SPEX SamplePrep). The extract was then centrifuged using the Eppendorf Centrifuge 5810R at 15 000 g for 5 min at 4°C. The supernatant was collected in another Eppendorf tube and evaporated using the EYELA CVE-200D Centrifugal Evaporator (TOKYO RIKAKIKAI CO., Tokyo, Japan) until dry. The residue was re-reconstituted in 1000 μL of 50% methanol. The reconstituted sample was sonicated for 10 min and centrifuged at 15 000 g for 5 min at 4°C. The supernatant was then filtered using a 0.2 μm Minisart RC4 filter (Sartorius Stedim Biotech GmbH) and subjected to LC–MS/MS analysis.

Liquid chromatography–tandem mass spectrometry method for traumatic acid quantification

Traumatic acid was analysed using Agilent 1290 UHPLC coupled with an Agilent 6460 triple quadrupole mass spectrometer (Agilent Technologies). The separation was performed on a Phenomenex Kinetex C18 column (2.1 × 50 mm, 2.6 μm, Phenomenex, Torrance, CA, USA), and the column was maintained at 40°C during the analysis. The mobile phase was composed of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile). A 0.3 mL/min linear gradient elution was employed as follows: 0–1.5 min, 5% solvent B; 1.5–5 min, 5–95% solvent B; 5–7 min, 95–95% solvent B; and column re-equilibration with 5% solvent B for 1 min. The injection volume was 5 μL. Negative electrospray ionization mode was utilized with the following parameters: 325°C, drying gas temperature; 8 L/min, drying gas flow; 45 psi nebulizer pressure; 325°C, sheath gas temperature; 11 L/min, sheath gas flow rate; and 3500 V, capillary voltage. Nozzle voltage was set at 500 V. The mass spectrometer was configured in multiple reaction monitoring mode, and the monitored transitions for traumatic acid were m/z 227.1 → 183.1 and 227.1 → 165. The concentrations of traumatic acid in samples were determined using the peak area of the analyte.

Data analysis

Mass spectrometry raw files were converted to the mzXML format using Trapper (ISB).27 The mzXML data were processed using our in-house package, TIPick,28 which was developed to remove background signals and detect each user-specific metabolite for UHPLC–MS data. By subtracting the blank chromatogram, TIPick can eliminate chemical signals appearing in blank injections. For target analysis, TIPick utilizes the length and intensity of chromatographic peaks to perform chromatographic peak enhancement and detection. Data analyses were performed using the R statistical software (Version 2.14.2).29

Statistical analyses

The t-tests or Wilcoxon–Mann–Whitney tests, and analyses of variance (ANOVAs) or Kruskal–Wallis tests were used to compare the distribution of continuous variables among elderly subjects without sarcopenia, elderly subjects with sarcopenia, and younger adults. For the categorical variables, χ2 tests or Fisher's exact tests were used to assess the difference in proportion between the different groups.

In the screening stage, t-tests or Wilcoxon–Mann–Whitney tests were performed to determine the mean differences of metabolites between elderly subjects without sarcopenia and younger adults, and between elderly subjects with and without sarcopenia, respectively. The metabolites that did not differ between elderly subjects without sarcopenia and young adults and those that significantly differed between elderly subjects with and without sarcopenia were subjected to the analyses mentioned in this study.

Finally, the relationships between metabolites, physical examination, and laboratory tests were evaluated using the Pearson/Spearman correlation coefficients. Statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA) and a P value <0.05 was considered statistically significant.

Results

Among the 168 candidates in our previous Comprehensive Geriatric Assessment and Frailty Study of Elderly Outpatients, 24 elderly subjects (≥65 years of age) met the diagnostic criteria for sarcopenia. Additionally, 24 sex-matched and age-matched elderly subjects without sarcopenia were employed as the control group. A total of 72 participants were enrolled in the analyses, including 24 elderly subjects without sarcopenia, 24 elderly subjects with sarcopenia, and 24 younger adults [age range: 23–37 years, 12 (50%) men] (Figure 1). Demographic data are summarized in Table 1. Ten (41.67%) men and 14 (58.33%) women were enrolled in both groups of elderly subjects. The median ages were 82 (range: 67–88) years and 81.5 (range: 67–87) years for the groups of elderly subjects with and without sarcopenia, respectively. There were no significant differences in age, sex distribution, and smoking status between these two groups. The co-morbidities were not significantly different between elderly subjects with and without sarcopenia, except for hypertension (P = 0.0045), and most medications taken by these two groups did not significantly differ, except for beta-blockers (P = 0.033) and calcium channel blockers (P = 0.0192).

image

CONSORT diagram of study subjects.

Table 1. Demographic data of study participants Variable Elders without sarcopenia (n = 24) Elders with sarcopenia (n = 24) Young adults (n = 24) P value (two groups)a P value (three groups)b Age (mean ± SD, years) 79.0 ± 5.9 79.4 ± 6.2 29.3 ± 4.3 0.7483c <0.0001d Sex 1.0000 0.7985 Male 10 (41.7) 10 (41.7) 12 (50.0) Female 14 (58.3) 14 (58.3) 12 (50.0) Smoking status 0.3412e 0.0508e Never 15 (62.5) 18 (75.0) 22 (91.7) Quitted 9 (37.5) 5 (20.8) 2 (8.3) Smoking 0 (0.0) 1 (4.2) 0 (0.00) Co-morbidity Hypertension 23 (95.8) 15 (62.5) 0 (0.0) 0.0045 <0.0001 Hyperlipidaemia 12 (50.0) 9 (37.5) 2 (8.3) 0.3827 0.0064 Diabetes mellitus 11 (45.8) 11 (45.8) 0 (0.0) 1.0000 0.0004 Coronary artery disease 8 (33.3) 4 (16.7) 0 (0.0) 0.1824 0.0045e Stroke 8 (33.3) 10 (41.7) 0 (0.0) 0.5510 0.0020 Medication Aspirin 11 (45.8) 12 (50.0) 0 (0.0) 0.7726 0.0002 Beta-blockers 8 (33.3) 2 (8.3) 0 (0.0) 0.0330 0.0026e Calcium channel blockers 14 (58.3) 6 (25.0) 0 (0.0) 0.0192 <0.0001 ACEIs or ARBs 16 (66.7) 12 (50.0) 0 (0.0) 0.2416 <0.0001 Metformin 5 (20.8) 6 (25.0) 0 (0.0) 0.7313 0.0328e Sulfonylureas 7 (29.2) 7 (29.2) 0 (0.0) 1.0000 0.0071e Thiazolidinediones 3 (12.5) 2 (8.3) 0 (0.0) 1.0000e 0.3580e Acarbose 0 (0.0) 2 (8.3) 0 (0.0) 0.4894e 0.3239e Repaglinide 0 (0.0) 1 (4.2) 0 (0.0) 1.0000e 1.0000e Statins 7 (29.2) 8 (33.3) 0 (0.0) 0.7555 0.0082 ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; SD, standard deviation.

A comparison of the physical examinations and laboratory tests among elderly subjects without sarcopenia, elderly subjects with sarcopenia, and younger adults is summarized in Table 2. The medians of weight (64.1 vs. 50.7 kg), BMI (26.3 vs. 21.2 kg/m2), ASMI (6.5 vs. 5.3 kg/m2), fat mass percentage (37.7% vs. 31.7%), waist circumference (94.5 vs. 82.0 cm), muscle strength (21.0 vs. 15.0 kg), and gait speed (0.9 vs. 0.7 m/s) of elderly subjects without sarcopenia were significantly higher than those of elderly subjects with sarcopenia. However, in the biochemical analyses, except for triglyceride (1.7 vs. 1.3 mmol/L, P = 0.0370), there was no significant difference between elderly subjects without sarcopenia and elder subjects with sarcopenia in red blood cell, haemoglobin, platelet, white blood cell, neutrophil, lymphocyte, albumin, glucose AC, total cholesterol, aspartate aminotransferase, alanine aminotransferase, blood urine nitrogen, creatinine, uric acid, log-transformed median plasma TNF-α (pg/mL), log-transformed median plasma CRP (nmol/mL), log-transformed median plasma irisin (ng/mL), and log-transformed median plasma GDF-15 (pg/mL).

Table 2. Results of the physical examination and laboratory tests Variable Elders without sarcopenia (n = 24) Elders with sarcopenia (n = 24) Young adults (n = 24) P valuea (two groups) P valueb (three groups) Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3) Physical examination Height (cm) 155.1 (149.5, 161.8) 155.2 (150.8, 163.6) 165.6 (162.8, 175.3) 0.6467 <0.0001 Weight (kg) 64.1 (58.1, 69.7) 50.7 (46.4, 55.2) 62.3 (54.1, 75.5) <0.0001 <0.0001 Body mass index (kg/m2) 26.3 (25.1, 27.0) 21.2 (19.7, 22.0) 22.5 (20.9, 25.4) <0.0001 <0.0001 ASMI (kg/m2) 6.5 (5.8, 7.7) 5.3 (5.1, 6.3) NA <0.0001d Fat mass percentage (%) 37.7 (30.7, 43.1) 31.7 (22.3, 35.9) NA 0.0080 Waist circumstance (cm) 94.5 (88.0, 98.5) 82.0 (77.8, 85.5) 79.0 (70.8, 87.0) <0.0001 <0.0001 Muscle strength (kg) 21.0 (16.0, 31.0) 15.0 (12.0, 21.5) NA 0.0231 Gait speed (m/s) 0.9 (0.6, 1.3) 0.7 (0.5, 0.9) NA 0.0461 Blood pressure Systolic (mmHg) 129.0 (119.0, 139.0) 130.0 (119.5, 138.5) 112.0 (108.5, 121.5) 0.9433 0.0005 Diastolic (mmHg) 70.0 (69.0, 76.0) 71.5 (69.5, 80.0) 78.0 (67.5, 83.5) 0.2880d 0.2010e Laboratory tests RBC (M/μL) 4.4 (3.8, 4.5) 4.3 (4.1, 4.5) 4.8 (4.4, 5.3) 0.5983 0.0004e Haemoglobin (g/dL) 12.7 (11.3, 13.3) 13.0 (12.4, 13.7) 13.9 (13.1, 15.4) 0.3217 0.0014e Platelet (K/μL) 240.5 (189.5, 272.5) 223.0 (185.0, 265.0) 259.5 (227.5, 289.5) 0.7571d 0.0989e WBC (K/μL) 6.2 (5.5, 7.2) 6.5 (5.5, 7.0) 6.5 (6.1, 7.8) 0.8874 0.2924 Neutrophil (%) 57.0 (53.5, 62.4) 60.5 (49.9, 66.3) 59.1 (56.1, 63.2) 0.6678 0.7083 Lymphocyte (%) 33.6 (28.7, 35.7) 30.5 (25.6, 42.8) 32.0 (30.0, 35.2) 0.8778 0.8619 Albumin (g/dL) 4.5 (4.4, 4.0) 4.6 (4.4, 4.8) 4.9 (4.9, 5.1) 0.5829 0.0003 Glucose AC (mmol/L) 5.7 (5.2, 7.4) 5.8 (4.9, 7.4) 4.6

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