Abstract
Background
Amino acids (AAs) and acylcarnitines play a key role in metabolic disease and can be used as biomarkers of various diseases such as malignancies, type 2 diabetes (T2D), insulin resistance, and cardiovascular diseases, therefore, designing an accurate and simple laboratory method that simultaneously measure both groups of substances, could improve the process of analytes quantification. In this research, a flow injection tandem mass spectrometry (FI-MS/MS) method for simultaneous measurement of AAs and acylcarnitines in addition to results of validation is explained.
Methods
Samples were mixed with internal standards and after derivatization (with butanolic-HCL), AAs, and acylcarnitines were quantified by tandem mass spectrometry (SCIEX API 3200). Analytical performance studies were designed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines including precision, accuracy, linearity, and limit of detection-quantification (LOD-LOQ) experiments. Samples from patients with T2D in different stages of kidney disease were also analyzed to ensure the clinical usage of the method.
Results
Performance evaluation of the method demonstrated adequate results. The mean of estimated inter-assay precision (reported as a coefficient variation) for AAs and acylcarnitines were less than 8.7% and 12.3%, the estimated mean bias was below 8.8% and 10.2% respectively. LOD of analytes ranged between 0.6–10 μmol per liter (μmol/L) for AAs and 0.02-1 μmol/L for acylcarnitines. LOQ analytes showed a range of 2–25 μmol/L and 0.05–5 μmol/L for AAs and carnitine/acylcarnitines respectively. In diabetic patients sample analysis, a significant increase in acylcarnitines (C2, C4, C5DC, C6, C8, C10, C14) and citrulline with a significant decrease in valine were seen in patients with severely increased albuminuria.
Conclusion
FI-MS/MS method with pre-injection derivatization with butanolic-HCL can be used for concurrent measurement of AAs and carnitine/acylcarnitines in a short time and it satisfies the analytical performance requirements. This method is applied for AAs and carnitine/acylcarnitines measurement in patient with T2DM and results show some of the acylcarnitines and AAs can be involved in diabetic nephropathy development.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40200-021-00786-3.
Keywords: Amino acid, Carnitine, Acylcarnitine, Tandem mass spectrometry, Diabetes, Nephropathy
Introduction
Recent years have seen increasing research interest in metabolome measurement to identify pathophysiological mechanisms involved in disease development, novel diagnostic and prognostic biomarkers [1, 2]. Of the various analytes which have been extensively studied, amino acids, carnitine, and acylcarnitine have received much attention in recent years. Specifically, amino acids are involved in different physiologic actions such as cell signaling, gene expression, nutrient metabolism, and production of hormones [3]. Recent studies have shown that branched amino acids (BCAAs) and their derivatives can be used as emerging biomarkers of various diseases such as malignancies, type 2 diabetes (T2D), insulin resistance, and cardiovascular diseases [4, 5]. As for carnitine and acylcarnitine, pieces of evidence showed that their dysregulation plays a role in the initiation and development of various diseases such as insulin resistance and metabolic syndrome [6–8], neurological diseases [9, 10], and coronary heart disease [11, 12].
Several analytical methods have been introduced for AAs analysis such as thin-layer chromatography, high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), tandem mass spectrometry (MS/MS), gas chromatography-mass spectrometry (GC-MS), capillary electrophoresis (CE), nuclear magnetic resonance (NMR), and electrochemical sensor. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS) are the most sensitive and effective methods [13]. There are various methods to measure carnitine/acylcarnitines such as chromatography [14, 15], enzymatic methods [16], and mass spectrometry which is the most frequently used method [7]. Considering the importance of both amino acids and acylcarnitines, designing an accurate and simple laboratory method that simultaneously measures both groups of substances, could improve the process of analytes quantification.
Liquid chromatography-tandem mass spectrometry is made of two powerful techniques that can separate and measure the chosen analyte in complex biologic samples. HPLC separates the analytes by their physicochemical properties. In tandem mass-spectrometer, molecules are ionized in ionization source (electrospray ionization), the first MS separates ionized molecules based on the mass to charge (m/z) ratio (precursor ions). Subsequently, fragmentation by collision-induced dissociation (CID) occurs, and second MS allows the chosen fragmented ions (product ions) to pass toward a detector where measurement happens [17–21]. Although coupling the chromatography with mass spectrometry improves the sensitivity and selectivity of measurement, it is time-consuming. An alternative analytical approach is flow-injection (FI) analysis which allows the prepared sample to be injected into the MS/MS system by an autosampler without passing through the column. Using sensitive systems such as triple quadrupole instruments and selecting specific ion transitions, improve the selectivity and sensitivity of the method and make FI-MS/MS a high-throughput quantitative method for metabolomics experiments [22, 23].
Here, we intended to explain and validate a flow-injection electrospray ionization tandem mass spectrometry method for simultaneous measurement of AAs and carnitine/ acylcarnitines in plasma/serum from patients with T2D at different stages of kidney disease.
Method
Instruments
For flow-injection MS/MS Analysis, the Thermo Scientific Dionex UltiMate 3000 standard HPLC system with a binary pump (the column was bypassed) along with a triple quadrupole mass spectrometer system API 3200 (SCIEX) with electrospray ionization were used. The mobile phase solution was composed of water and acetonitrile (25:75, volume/volume), and the optimal gradient elution was as follows: 0–0.65 min: 0.05 mL/min, 0.65-1 min: 0.1 mL/min, 1–1.3 min: 1 mL/min, 1.3–1.5 min: 0.05 mL/min. Sample injection volume was 5 μL and total run time was 1.5 min.
The setting of the MS ionization source was in the following manner: Collision energy: 25.5 V, Curtain gas: 20 psi, collision gas:5 psi, temperature:450 °C, ion spray voltage: 5500 V, ion source gas1: 60 psi, ion source gas2: 50 psi, interface heater: on. Nitrogen was used as the curtain gas and the collision gas. Multiple reaction monitoring (MRM) with positive ion mode was used. Precursor and product ion transitions were adjusted based on previous researches [24, 25].
Fasting blood glucose (FBG), urea, creatinine, and urine albumin was measured by Roche C311 chemistry analyzer. HbA1c was measured by HPLC TOSOH G8.
Chemicals and reagents
Amino acid calibrator and quality control (QC) material in two levels were purchased from RECIPE (Munich, Germany). Two levels of QC material for acylcarnitines were obtained from Chromsystems. A combination of labeled amino acids and carnitine/acylcarnitines obtained from Chromsystems was used as an internal standard (IS). Acetonitrile, mass grade water, methanol, acetyl chloride, and 1-butanol were purchased from Merck Company.
Standards and QC material preparation
Lyophilized calibrators and quality control materials were prepared and maintained according to the manufacturer’s protocol. The mixture of internal standard was reconstituted by adding 25 mL buffer.
Derivatization procedure
Derivatization solution was daily prepared by mixing 10 μL acetyl chloride and 90 μL 1-butanol. 10 μL of each sample (plasma, calibrators, or QC materials) were placed into 1.5 mL vials, then 200 μL of IS was added and mixed. After centrifugation at 10000 g for 10 min at 4 °C, supernatant fluids were transferred into new vials and dried by a flow of nitrogen 99.9% at 45 °C. After that, the derivatization solution (50 μL) was added to vials and mixed by vortexing, then incubated at 65 °C for 15 min. The samples were dried by a flow of nitrogen 99.9% at 45 °C and reconstituted by 100 μL of mobile phase (acetonitrile and water). The prepared samples were transferred to insert vials and put in an HPLC autosampler.
Data processing
Data were processed using Multiquant software (ABI Sciex). Ratios of the signals of the metabolites relative to the internal standards were used to make calibration curves and calculate analyte concentrations in the QC materials and samples. Where the calibrators were not available (some of the acylcarnitines), metabolites were estimated considering internal standards concentration. Linear regression models with appropriate weighting factor (1/x) was used (supplement 2).
Analytical performance studies
Analytical performance studies were designed based on Clinical and Laboratory Standards Institute guidelines (EP05- EP06- EP17) and included precision, accuracy, linearity, and limit of detection-quantification (LOD-LOQ) experiments. Briefly, two-level of QC materials were used to evaluate intra-assay and inter-assay precision in 20 days, and the results were analyzed based on CLSI EP05. The mean value of results from QC material were compared to the target values claimed by the manufacturer to evaluate the trueness of the method. By preparing different dilutions of calibrator/ samples (9–11 dilutions), the linearity of the method was assessed. Limit of detection (LOD) which is the lowest detectable concentration of the analytes was calculated as two standard deviations above that of the blank. The lowest level of each analyte with coefficient variation less than 20% is considered as the limit of quantification (LOQ). The stability of derivatized samples (that were ready to inject) was determined by analyzing 10 samples at baseline and after 24 h in 2–8 °C.
Patients
Patients with T2D (n = 52) aged between 35 and 75, who were referred to Diabetes and Metabolic Research Center affiliated to Endocrinology and Metabolism Research Institute (EMRI) were enrolled in the study. Patients were in different stages of kidney disease: with increased albumin/ creatinine excretion (ACR) more than 30 mg/g (n = 29) and normal ACR (n = 23). Patients with increased ACR were sub-grouped into ACR 30–299 mg/g (moderately increased albuminuria)(n = 19) and more than 300 mg/g (severely increased albuminuria)(n = 10).
Patients with type 1 diabetes, diabetes duration less than 5 years, HbA1c > 9% and any other cause of protein excretion were excluded. Fasting blood samples were obtained then plasma and serum were separated within one hour of venipuncture. The samples were kept frozen at −80 °C until analysis. The estimated glomerular filtration rate (eGFR) was calculated based on the Cockcroft-Gault equation. The study was approved by the Ethics Committee of EMRI and the purpose of the study was explained to the patients.
Statistical analysis was made by SPSS version 23 and Microsoft excel software and P Value less 0.05 is considered as significant.
Results
The quantitation was executed using positive MRM mode and the optimized ion transitions are listed in Table 1. The declustering potential (DP), entrance potential (EP), collision energy (CE), collision cell ent. Potential (CEP), and cell exit potential (CXP) were optimized to obtain the best results (Supplement Table 1).
Table 1.
The multiple reaction monitoring (MRM) transitions
| Aminoacids | Precursor ion (m/z) | Product ion (m/z) | Carnitine/acylcarnitines | Precursor ion (m/z) | Product ion (m/z) |
|---|---|---|---|---|---|
| Alanine | 146.1 | 44 | C0 | 218.2 | 103 |
| Alanine D4 (IS) | 150.1 | 48 | C0-IS | 227.2 | 103 |
| Aspartic Acid | 246.1 | 144.1 | C2- Carnitine | 260.2 | 85 |
| Aspartic Acid D3 (IS) | 249.1 | 147.1 | C2- Carnitine-D9 (IS) | 263.2 | 85 |
| Glutamic Acid | 260.1 | 158.1 | C3- Carnitine | 274.2 | 85 |
| Glutamic Acid D5 (IS) | 265.1 | 163.1 | C3- Carnitine-D3(IS) | 277.2 | 85 |
| Leucine | 188.1 | 86.1 | C4- Carnitine | 288.2 | 85 |
| Leucine D3 (IS) | 191.1 | 89.1 | C4- Carnitine-D3 (IS) | 291.2 | 85 |
| Methionine | 206.2 | 104.1 | C5- Carnitine | 302.2 | 85 |
| Methionine D3 (IS) | 209.2 | 107.1 | C5- Carnitine-D9 (IS) | 311.2 | 85 |
| Phenylalanine | 222.1 | 120.1 | C5DC- Carnitine | 388.3 | 85 |
| Phenylalanine D5 (IS) | 227.1 | 125.1 | C5DC- Carnitine-D6 (IS) | 394.3 | 85 |
| Tyrosine | 238.2 | 136.1 | C6- Carnitine | 316.2 | 85 |
| Tyrosine D4 (IS) | 242.1 | 140.1 | C6- Carnitine-D3 (IS) | 319.2 | 85 |
| Valine | 174.1 | 72.1 | C8- Carnitine | 344.2 | 85 |
| Valine D8 (IS) | 182.1 | 80.1 | C8- Carnitine-D3 (IS) | 347.2 | 85 |
| Arginine | 231.1 | 70.1 | C10- Carnitine | 372.3 | 85 |
| Arginine D7 (IS) | 238.1 | 77.1 | C10- Carnitine-D3 (IS) | 375.3 | 85 |
| Citrulline | 232.2 | 113.1 | C14- Carnitine | 428.4 | 85 |
| Citrulline D2 (IS) | 234.2 | 115.1 | C14- Carnitine-D3 (IS) | 431.4 | 85 |
| Glycine | 132.1 | 76.1 | C16- Carnitine | 456.4 | 85 |
| Glycine-13C2,15 N (IS) | 135.1 | 79.1 | C16- Carnitine-D3 (IS) | 459.4 | 85 |
| Ornithine | 189.1 | 70.1 | C18- Carnitine | 484.4 | 85 |
| Ornithine (IS) | 195.1 | 76.1 | C18- Carnitine-D3 (IS) | 487.4 | 85 |
| Proline | 172.1 | 116.1 | |||
| Proline (IS) | 179.1 | 123.1 | |||
| Threonine | 176.1 | 74.1 | |||
| Serine | 162.1 | 60.1 | |||
| Histidine | 212.1 | 110.1 | |||
| Lysine | 203.1 | 84.1 | |||
| Tryptophane | 261.1 | 244.1 | |||
| Asparagine | 188.9 | 73.9 | |||
| Glutamine | 203 | 84 |
Table 2 recaps the precision and trueness of quantification using QC materials. Inter-assay precision of QC samples reported as coefficient variation (CV %) were 3.4–16.2% and 7.7–17% for AAs and acylcarnitines respectively.
Table 2.
Precision and trueness of analytes in two-level
| QC level 1 | QC level 2 | |||||||
|---|---|---|---|---|---|---|---|---|
| Analytes (μmol/L) | Concentration | Intra assay (CV%) | Interassay (CV%) | Bias % | Concentration | Intra assay (CV%) | Interassay (CV%) | Bias % |
| Alanine | 421 | 1.9 | 3.4 | 1.4 | 718 | 2.2 | 4.8 | 1.8 |
| Aspartic Acid | 15.4 | 8.5 | 11.7 | 19.1 | 1010 | 2.5 | 4.8 | 9.9 |
| Glutamic Acid | 122 | 2.9 | 4.9 | – | 194 | 2.9 | 5 | – |
| Leucine | 122 | 1.4 | 10.1 | 8.2 | 318 | 2.5 | 6.2 | 16.6 |
| Methionine | 30 | 2.7 | 11.8 | 13.2 | 79 | 3.5 | 12.1 | 7.7 |
| Phenylalanine | 79 | 1.9 | 9.7 | 1.1 | 421 | 3 | 5.8 | 0.5 |
| Tyrosine | 63 | 2.15 | 5.2 | 1.2 | 204 | 2.4 | 5.5 | 3.8 |
| Valine | 283 | 1.8 | 4.2 | 11.4 | 454 | 1.7 | 6.1 | 12.8 |
| Arginine | 44 | 4 | 7.7 | 7.8 | 237 | 3 | 8.1 | 12.5 |
| Citrulline | 24 | 3.52 | 10.6 | 3.9 | 122.9 | 3.4 | 7.3 | 0.7 |
| Glycine | 209 | 1.6 | 4.9 | 9.7 | 667 | 1.7 | 4.9 | 2.5 |
| Ornithine | 142 | 2.7 | 3.4 | 2.5 | 342 | 2.4 | 5.7 | 2.2 |
| Proline | 214 | 1.8 | 4.7 | 9.8 | 465 | 2.2 | 6 | 9.9 |
| Threonine | 111 | 8.2 | 5.9 | 2.7 | 208 | 9.2 | 8.9 | 1.7 |
| Serine | 104 | 9.3 | 8.7 | 16.7 | 275 | 9.1 | 7.6 | 11.8 |
| Histidine | 72 | 14.8 | 15 | 10.6 | 153 | 13.2 | 9.1 | 9.8 |
| Lysine | 143 | 10.9 | 14.3 | 18 | 273 | 9.6 | 12.5 | 5.3 |
| Tryptophane | 49 | 6.5 | 11.5 | 5.8 | 220 | 4.4 | 10.2 | 4.3 |
| Asparagine | 20 | 10.9 | 16.2 | 14.5 | 179.7 | 8.4 | 16.1 | 6.3 |
| Glutamine | 414 | 3.9 | 10.7 | – | 864 | 8.4 | 9.6 | – |
| Glu + GLN | 536 | – | – | 10.5 | 1058 | – | – | 3.3 |
| C0 | 51.5 | 4.9 | 7.9 | 9.4 | 103.0 | 4.5 | 7.7 | 10.7 |
| C2- Carnitine | 27.2 | 5.9 | 12.5 | 17.7 | 63.0 | 5.3 | 8.4 | 0.1 |
| C3- Carnitine | 4.7 | 5.4 | 11.3 | 2.0 | 12.3 | 5.2 | 8.4 | 10.8 |
| C4- Carnitine | 1.0 | 7.7 | 11.7 | 12.0 | 3.8 | 4.1 | 8.7 | 6.6 |
| C5- Carnitine | 0.5 | 6.6 | 11.5 | 14.7 | 2.1 | 7.5 | 8.4 | 1.8 |
| C5DC- Carnitine | 0.5 | 6.7 | 13.5 | 2.7 | 1.9 | 6.1 | 8.5 | 6.5 |
| C6- Carnitine | 0.5 | 6.6 | 12.3 | 7.1 | 2.0 | 4.0 | 9.2 | 3.4 |
| C8- Carnitine | 0.5 | 5.9 | 14.3 | 14.3 | 2.1 | 5.9 | 7.8 | 7.5 |
| C10- Carnitine | 0.5 | 8.2 | 17.0 | 13.1 | 2.0 | 6.6 | 10.5 | 4.7 |
| C14- Carnitine | 0.7 | 6.3 | 14.8 | 20.0 | 2.6 | 6.2 | 15.2 | 26.0 |
| C16- Carnitine | 4.7 | 6.3 | 9.5 | 6.0 | 11.9 | 4.0 | 10.8 | 11.7 |
| C18 | 2.5 | 5.2 | 15.5 | 8.0 | 7.9 | 5.3 | 13.0 | 8.1 |
The mean comparison between the obtained result and the target value of QC materials showed a mean bias of 8.8% and 6.5% for different levels of control material for AAs. The results for the carnitine/acylcarnitines were 10.2% and 5.2%. Since glutamic acid can be easily converted to glutamine, the bias of each one was not calculated separately, instead, it is calculated by comparing the sum of obtained results to QC materials (Table 2).
Linearity
Correlation coefficients (r) were > 0.961 for AAs and > 0.9 for carnitine/acylcarnitines which signify a suitable linear relationship. The linearity range for each compound is depicted in supplement Table 2.
Limit of detection (LOD) and quantification (LOQ)
LOD of analytes ranged between 0.6–10 μmol/L for AAs and 0.02-1 μmol/L for carnitines. LOQ analytes showed a range of 2–25 μmol/L and 0.05–5 μmol/L for AAs and carnitine/acylcarnitines respectively (supplement Table 2).
Stability of prepared samples
Derivatized samples were kept in 2–8 C for 24 h and analyzed to check the stability however no significant differences were observed after 24 h. The results of recovery ranged from 90.6–106.6% (mean: 99.5) for AAs and 95–116% (mean:103.6%) for carnitine/acylcarnitines. The highest change in AAs and acylcarnitines were observed for lysine (90.6%) and C5DC (116%) (Supplement Table 3).
Patient’s results
Table 3 summarizes the results of demographic and biochemical analysis of patients (female: 21, male: 31). The number of patients with normoalbuminuria, moderately increased albuminuria, and severely increased albuminuria were 23, 19, and 10 respectively. There were no significant differences in age, duration of diabetes, body mass index (BMI), FBG, and A1c between the three groups. As expected the results of urea, creatinine, ACR, and GFR were significantly different.
Table 3.
Comparison of clinical and biochemical variables and concentrations of amino acids and acylcarnitines between diabetic groups patients using َANOVA and Post Hoc Bonferroni analysis
| Variables | ACR < 30 mg/g (n = 23) | ACR 30-299 mg/g (n = 19) | ACR > 300 mg/g (n = 10) |
|---|---|---|---|
| Age(year) | 61 (7.42) | 58 (9) | 62 (7) |
| BMI(kg/m2) | 32(11.1) | 30 (4.7) | 27(4.2) |
| Diabetes duration (year) | 13(7.3) | 10 (7.4) | 17(11.1) |
| FBG (mg/dL) | 129(25) | 147 (62) | 122(36) |
| A1C (%) | 7.3(0.74) | 7.5 (0.78) | 7.0(0.87) |
| Urea (mg/dL) | 32.3(9.5) | 31 (8.9) | 63.3(40.3) *‡ |
| Creatinine (mg/dL) | 0.9(0.21) | 0.98 (0.18) | 1.6(0.69) *‡ |
| ACR (mg/g) | 9 (7.4) | 129 (70)† | 486(222) *‡ |
| GFR (ml/min/1.73m2) | 87(15.6) | 75 (17) | 56(14.5)*‡ |
| Alanine (μmol/L) | 526 (172) | 482 (163) | 486(186) |
| Aspartic Acid (μmol/L) | 26 (7) | 28(15) | 27(6) |
| Glutamic Acid (μmol/L) | 104(25) | 108(33) | 108(20) |
| Leucine (μmol/L) | 134 (27) | 136(28) | 135(28) |
| Methionine (μmol/L) | 26 (5) | 24(7) | 26(3) |
| Phenylealanine (μmol/L) | 72(11) | 68(14) | 73(11) |
| Tyrosine (μmol/L) | 67(18) | 65(19) | 61(3) |
| valine (μmol/L) | 286(40) | 255(63) | 214(19)* |
| Arginine (μmol/L) | 105(24) | 99 (20) | 103(22) |
| Citrulin (μmol/L) | 31(13) | 29(14) | 47(28)*‡ |
| glycine (μmol/L) | 305(122) | 343 (161) | 307(65) |
| Ornithine (μmol/L) | 83(31) | 101 (72) | 102(26) |
| Proline (μmol/L) | 256(82) | 297(96) | 309(84) |
| Threonine (μmol/L) | 167(56) | 156 (55) | 176(34) |
| Serine (μmol/L) | 150(77) | 134 (72) | 139(33) |
| Histidine (μmol/L) | 86(26) | 105 (43) | 98(9) |
| Lysine (μmol/L) | 152(34) | 188 (46)† | 158(32) |
| Tryptophane (μmol/L) | 51(11) | 52 (15) | 44(12) |
| Asparagine (μmol/L) | 38(15) | 51 (24) | 41 (8) |
| Glutamine (μmol/L) | 513(119) | 538 (218) | 466(141)‡ |
| C0- Carnitine (μmol/L) | 64(13.4) | 62(12.7) | 69(32.6) |
| C2 - Carnitine (μmol/L) | 19(4.9) | 18.4 (3.5) | 27(17)*‡ |
| C3- Carnitine (μmol/L) | 1.13(0.49) | 1.16 (0.41) | 1.15(0.32) |
| C4- Carnitine (μmol/L) | 0.51(0.22) | 0.57 (0.37) | 0.88(0.68) |
| C5- Carnitine (μmol/L) | 0.24(0.09) | 0.24 (0.09) | 0.24(0.09) |
| C5DC- Carnitine (μmol/L) | 0.37(0.10) | 0.38 (0.12) | 0.63(0.50)*‡ |
| C6- Carnitine (μmol/L) | 0.14(0.05) | 0.16 (0.06) | 0.31(0.2)*‡ |
| C8- Carnitine (μmol/L) | 0.29(0.13) | 0.31 (0.13) | 0.75(0.71)*‡ |
| C10 - Carnitine(μmol/L) | 0.34(0.13) | 0.38 (0.15) | 0.94(0.87)*‡ |
| C14 - Carnitine (μmol/L) | 0.08(0.03) | 0.09 (0.02) | 0.12(0.07)*‡ |
| C16- Carnitine (μmol/L) | 0.17(0.06) | 0.19 (0.07) | 0.22(0.09) |
| C18- Carnitine (μmol/L) | 0.07(0.02) | 0.09 (0.04) | 0.09(0.03) |
Data are presented as mean(SD)
BMI: Body mass index, FBG: Fasting blood glucose, ACR: Albumin creatinine ratio, GFR: Glomerular filtration rate
(*) indicates significant difference (P value <0.05) between normoalbuminura and severely increased albuminuria
(‡) indicates significant difference (P value <0.05) between moderately and severely increased albuminuria
(†) indicates significant difference(P value <0.05) between normoalbuminura and moderately increased albuminuria
Maximum alteration in acylcarnitines was identified in the severely increased albuminuria group compared to normoalbuminuria and moderately increased albuminuria patients. The value of acylcarnitines including C2, C4, C5DC, C6, C8, C10, C14 were significantly higher than other groups. AAs level showed less deviation between groups. Citrulline level was significantly higher in severely increased albuminuria patients compared to both normoalbuminuria and microalbuminuria whereas valine level was lower than only the normoalbuminuria group.
Discussion
Recent years have witnessed a surge in research interest in unraveling the role of AAs and carnitine/acylcarnitines in the development of chronic diseases. Since they can be used for different purposes such as prediction and treatment monitoring, laboratory measurement of both AAs and carnitine/acylcarnitines is being popular.
Here, a simple and precise flow injection mass spectrometry method has been used for concurrent measurement of 20 amino acids and 12 carnitine/acylcarnitines in human plasma/serum samples. Using pre-injection derivatization enabled the system to analyze 32 parameters in a short time (less than 5 min).
Several studies have been published so far measuring amino acids and acylcarnitines without derivatization [26, 27], however, to obtain a better sensitivity and selectivity of analysis different derivatization reagents have been introduced [13]. Although AAs can be derivatized by different reagents such as 6-aminoquinolyl-N-hydroxy succinimidyl carbamate(AQC) [28], dansyl chloride (DNS) [29], fluorenylmethyl chloroformate (FMOC-Cl) [30],diethyl ethoxymethylenemalonate (DEEMM) [31], and butanolic hydrochloride [32], Acylcarnitines are frequently derivatized to butyl or methyl esters and the number of derivitization methods are limited [33, 34]. Therefore derivitization with Butanolic-HCL possesses the benefit of simultaneous derivitization operation of AAs and acetylcarnitines which naturally makes the measurement, cheaper and faster. Other advantages of the method used are easy preparation and effective destruction of phospholipids at 60 °C [25, 35]. Using FI-MS/MS method significantly reduces the test time as it eliminates the necessity of passing the sample through the column.
Given that a smaller sample volume (only 10 μL) is required in the presented method compared to the other methods (20–100 μL) [25, 26, 36], and the time needed for sample preparation (less than one hour) and measurement (less than five minutes) is relatively short, this method can be used in different conditions such as large scale researches as well as analysis of low volume samples (samples of children or the elderly).
Performance evaluation of the method demonstrated adequate results. The mean of estimated inter-assay precision for AAs and carnitines were less than 8.7% and 12.3% respectively. The estimated mean of biases were less than 8.8% and 10.2%. The results were comparable with previous publications [15, 25, 35]. Considering the biological variation database [37, 38], both inter and intra assay precision of alanine, arginine, aspartic acid, citrulline, glutamic acid, glycine, leucine, ornithine, proline, valine, tryptophan, threonine was acceptable.
Here, we also performed a quantitative analysis of AAs and carnitines in T2D patients with different stages of kidney disease based on ACR. We observed that patients with severely increased albuminuria had a higher level of free carnitine, acylcarnitines including C2, C4, C5, C6, C8, C10, C14, C16, and C18 than patients with moderately increased albuminuria and normoalbuminuria. Whereas the carnitine profile of patients with microalbuminuria and normoalbuminuria were roughly the same. Hence, it is tempting to speculate that T2D patients with severely increased albuminuria have increased incomplete fatty acid oxidation which in turn leads to an increase in plasma levels of acylcarnitines. As for AAs, a significant decrease in valine level and a non-significant reduction in glutamine, tryptophan, and tyrosine were revealed in the severely increased albuminuria group compared to other groups. A significant elevation of citrulline was also observed which can be due to a drop in the conversion of citrulline to arginine [39]. In patients with severely increased albuminuria levels of tyrosine and tryptophane were negatively correlated with ACR.
Previous researches have shown AAs, free carnitine, and acylcarnitines alteration in patients with T2D patients and prediabetic condition compared to healthy people [39–42] nevertheless there are fewer data about AAs and carnitine deviations in diabetic nephropathy. Saleem et al. have revealed a branched amino acid reduction in diabetic nephropathy compared to the healthy and normoalbuminuria groups. They also reported a significant change in ornithine and phenylalanine in the macroalbuminuria group compared to the other groups. In another study by Chuang et al. patients with macroalbuminuria had higher short and medium-chain acylcarnitines [43]. Pena et al. reported a significant decrease in the level of histidine and an increase in C4 in patients with macroalbuminuria. In the present study, a statistically significant decrease in valine, and an increase in citrulline was observed in patients with severely increased albuminuria in comparison with the normoalbuminuria group.
In a study by Goek et al., it was demonstrated that most of the carnitines have a negative correlation with GFR [44]. In our study, we observed a significant increase in carnitines in patients with severely increased ACR which could be due to kidney impaired excretory function and by reducing mitochondrial β-oxidation which has happened in the insulin resistance situation [45].
The controversy in amino acid and carnitine/acylcarnitines alterations that have been observed in various studies can occur due to pre-analytic variables such as environment, nutrition, medication in the studied population as well as analytical variables such as instruments, laboratory methods, and calibration.
Similar to other FI-MS/MS methods, this method also suffers from a less selectivity and sensitivity compared to LC/MS/MS methods. We have tried to improve the performance by selecting specific ion transitions. On the other hand, we analysed the application of the presented method on serum and plasma samples but not other biological samples. Given that for derivitization, heating and acidic condition is necessary, hydrolysis of acylcarnitine ester bonds might occur which leads to a decrease in acylcarnitines. We tried to compensate for the results by concurrent use of internal standards. Another limitation of this study is that a complete information about the participants’ diets was not provided.
Considering the heterogeneity in ethnicity and genetic background and other pre-analytic variables, conducting more comprehensive researches could improve our knowledge about DN developments as well as other complications of diabetes.
Conclusion
FI-MS/MS method can be successfully used for concurrent measurement of AAs and carnitine/acylcarnitines in a short time which makes it a suitable technique for studies with a high number of samples. The peak shape, sensitivity, and reproducibility are improved using pre-injection derivatization with butanolic-HCL. This method is applied for AAs and carnitine/acylcarnitines measurement and results show some of the acylcarnitines and AAs can be involved in diabetic nephropathy development. However, more extensive researches are needed to confirm the results of the present study.
Supplementary Information
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(PDF 174 kb)
Authors’ contributions
All authors participated and approved the paper before submission. FR planned the study, NN, AK, and ShH did the analytical examination and analyzed the result, AS contributed in optimization and supervision of systems, BA and SE participated in the study design, PE contributed in technical issues, statistical analysis and preparation of the manuscript.
Funding
This study is funded by Endocrinology and Metabolism Research Institute.
Data availability
The dataset supporting the conclusions of this article is available and can be accessed by contacting the corresponding author.
Declarations
Ethics approval
The study was approved by the Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences.
Conflict of interest
The authors declare that they have no conflict of interest.
Footnotes
Publisher’s note
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Contributor Information
Parsa Esmati, Email: qm18554@bristol.ac.uk.
Niloufar Najjar, Email: nilou.najjar1369@gmail.com.
Solaleh Emamgholipour, Email: semamgholipour@sina.tums.ac.ir.
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Babak Arjmand, Email: barjmand@sina.tums.ac.ir.
Amin Soleimani, Email: a.soleimani1359@gmail.com.
Ardeshir Kakaii, Email: ardeshirkakaei@yahoo.com.
Farideh Razi, Email: f-razi@tums.ac.ir.
References
- 1.Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R. Metabolomics for laboratory diagnostics. J Pharm Biomed Anal. 2015;113:108–120. doi: 10.1016/j.jpba.2014.12.017. [DOI] [PubMed] [Google Scholar]
- 2.Newgard CB. Metabolomics and metabolic diseases: where do we stand? Cell Metab. 2017;25(1):43–56. doi: 10.1016/j.cmet.2016.09.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wu G. Amino acids: metabolism, functions, and nutrition. Amino Acids. 2009;37(1):1–17. doi: 10.1007/s00726-009-0269-0. [DOI] [PubMed] [Google Scholar]
- 4.Gannon NP, Schnuck JK, Vaughan RA. BCAA metabolism and insulin sensitivity–Dysregulated by metabolic status? Mol Nutr Food Res. 2018;62(6):1700756. doi: 10.1002/mnfr.201700756. [DOI] [PubMed] [Google Scholar]
- 5.Nie C, He T, Zhang W, Zhang G, Ma X. Branched chain amino acids: beyond nutrition metabolism. Int J Mol Sci. 2018;19(4):954. doi: 10.3390/ijms19040954. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Bene J, Márton M, Mohás M, Bagosi Z, Bujtor Z, Oroszlán T, Gasztonyi B, Wittmann I, Melegh B. Similarities in serum acylcarnitine patterns in type 1 and type 2 diabetes mellitus and in metabolic syndrome. Ann Nutr Metab. 2013;62(1):80–85. doi: 10.1159/000345759. [DOI] [PubMed] [Google Scholar]
- 7.Mansour FR, Wei W, Danielson ND. Separation of carnitine and acylcarnitines in biological samples: a review. Biomed Chromatogr. 2013;27(10):1339–1353. doi: 10.1002/bmc.2995. [DOI] [PubMed] [Google Scholar]
- 8.Mingrone G. Carnitine in type 2 diabetes. Ann N Y Acad Sci. 2004;1033(1):99–107. doi: 10.1196/annals.1320.009. [DOI] [PubMed] [Google Scholar]
- 9.Ahmed AT, MahmoudianDehkordi S, Bhattacharyya S, Arnold M, Liu D, Neavin D, Moseley MA, Thompson JW, Williams LSJ, Louie G, Skime MK, Wang L, Riva-Posse P, McDonald W, Bobo WV, Craighead WE, Krishnan R, Weinshilboum RM, Dunlop BW, Millington DS, Rush AJ, Frye MA, Kaddurah-Daouk R, Mood Disorders Precision Medicine Consortium (MDPMC) Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes. J Affect Disord. 2020;264:90–97. doi: 10.1016/j.jad.2019.11.122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Jones LL, McDonald DA, Borum PR. Acylcarnitines: role in brain. Prog Lipid Res. 2010;49(1):61–75. doi: 10.1016/j.plipres.2009.08.004. [DOI] [PubMed] [Google Scholar]
- 11.Guasch-Ferré M, Zheng Y, Ruiz-Canela M, Hruby A, Martínez-González MA, Clish CB, Corella D, Estruch R, Ros E, Fitó M, Dennis C, Morales-Gil IM, Arós F, Fiol M, Lapetra J, Serra-Majem L, Hu FB, Salas-Salvadó J. Plasma acylcarnitines and risk of cardiovascular disease: effect of Mediterranean diet interventions. Am J Clin Nutr. 2016;103(6):1408–1416. doi: 10.3945/ajcn.116.130492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kalim S, Clish CB, Wenger J, Elmariah S, Yeh RW, Deferio JJ, Pierce K, Deik A, Gerszten RE, Thadhani R, Rhee EP. A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients. J Am Heart Assoc. 2013;2(6):e000542. doi: 10.1161/JAHA.113.000542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Xu W, Zhong C, Zou C, Wang B, Zhang N. Analytical methods for amino acid determination in organisms. Amino Acids. 2020:1–18. [DOI] [PubMed]
- 14.de Sousa C, English NR, Stacey TE, Chalmers RA. Measurement of L-carnitine and acylcarnitines in body fluids and tissues in children and in adults. Clin Chim Acta. 1990;187(3):317–328. doi: 10.1016/0009-8981(90)90117-B. [DOI] [PubMed] [Google Scholar]
- 15.Kerner J, Bieber L. A radioisotopic-exchange method for quantitation of short-chain (acid-soluble) acylcarnitines. Anal Biochem. 1983;134(2):459–466. doi: 10.1016/0003-2697(83)90323-8. [DOI] [PubMed] [Google Scholar]
- 16.Bieber L, Lewin L. [13] Measurement of carnitine and O-acylcarnitines. Methods in enzymology. 72: Elsevier; 1981. p. 276–87. [DOI] [PubMed]
- 17.Sargent M. Guide to achieving reliable quantitative LC-MS measurements. RSC analytical methods committee 2013.
- 18.Awad H, Khamis MM, El-Aneed A. Mass spectrometry, review of the basics: ionization. Appl Spectrosc Rev. 2015;50(2):158–175. doi: 10.1080/05704928.2014.954046. [DOI] [Google Scholar]
- 19.Banerjee S, Mazumdar S. Electrospray ionization mass spectrometry: a technique to access the information beyond the molecular weight of the analyte. International journal of analytical chemistry. 2012;2012:1–40. doi: 10.1155/2012/282574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.El-Aneed A, Cohen A, Banoub J. Mass spectrometry, review of the basics: electrospray, MALDI, and commonly used mass analyzers. Appl Spectrosc Rev. 2009;44(3):210–230. doi: 10.1080/05704920902717872. [DOI] [Google Scholar]
- 21.Lane C. Mass spectrometry-based proteomics in the life sciences. Cellular and Molecular Life Sciences CMLS. 2005;62(7–8):848–869. doi: 10.1007/s00018-005-5006-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Nanita SC, Kaldon LG. Emerging flow injection mass spectrometry methods for high-throughput quantitative analysis. Anal Bioanal Chem. 2016;408(1):23–33. doi: 10.1007/s00216-015-9193-1. [DOI] [PubMed] [Google Scholar]
- 23.Sarvin B, Lagziel S, Sarvin N, Mukha D, Kumar P, Aizenshtein E, Shlomi T Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions. Nat Commun 2020;11(1):1–11, 3186. [DOI] [PMC free article] [PubMed]
- 24.Gaugler SC, Rykl J, Cebolla VL. Validation of an automated extraction procedure for amino acids and acylcarnitines for use with tandem mass spectrometry for newborn screening. 2019.
- 25.Ni J, Xu L, Li W, Wu L. Simultaneous determination of thirteen kinds of amino acid and eight kinds of acylcarnitine in human serum by LC–MS/MS and its application to measure the serum concentration of lung cancer patients. Biomed Chromatogr. 2016;30(11):1796–1806. doi: 10.1002/bmc.3755. [DOI] [PubMed] [Google Scholar]
- 26.Teav T, Gallart-Ayala H, van der Velpen V, Mehl F, Henry H, Ivanisevic J. Merged targeted quantification and untargeted profiling for comprehensive assessment of acylcarnitine and amino acid metabolism. Anal Chem. 2019;91(18):11757–11769. doi: 10.1021/acs.analchem.9b02373. [DOI] [PubMed] [Google Scholar]
- 27.Corso G, Paglia G, Garofalo D, D'apolito O. Neutral loss analysis of amino acids by desorption electrospray ionization using an unmodified tandem quadrupole mass spectrometer. Rapid Communications in Mass Spectrometry: An International Journal Devoted to the Rapid Dissemination of Up-to-the-Minute Research in Mass Spectrometry. 2007;21(23):3777–3784. doi: 10.1002/rcm.3280. [DOI] [PubMed] [Google Scholar]
- 28.Sharma G, Attri SV, Behra B, Bhisikar S, Kumar P, Tageja M, Sharda S, Singhi P, Singhi S. Analysis of 26 amino acids in human plasma by HPLC using AQC as derivatizing agent and its application in metabolic laboratory. Amino Acids. 2014;46(5):1253–1263. doi: 10.1007/s00726-014-1682-6. [DOI] [PubMed] [Google Scholar]
- 29.Rebane R, Oldekop M-L, Herodes K. Comparison of amino acid derivatization reagents for LC–ESI-MS analysis. Introducing a novel phosphazene-based derivatization reagent. J Chromatogr B. 2012;904:99–106. doi: 10.1016/j.jchromb.2012.07.029. [DOI] [PubMed] [Google Scholar]
- 30.de Puit M, Ismail M, Xu X. LCMS analysis of fingerprints, the amino acid profile of 20 donors. J Forensic Sci. 2014;59(2):364–370. doi: 10.1111/1556-4029.12327. [DOI] [PubMed] [Google Scholar]
- 31.Oldekop M-L, Rebane R, Herodes K. Dependence of matrix effect on ionization polarity during LC–ESI–MS analysis of derivatized amino acids in some natural samples. European Journal of Mass Spectrometry. 2017;23(5):245–253. doi: 10.1177/1469066717711026. [DOI] [PubMed] [Google Scholar]
- 32.Casetta B, Tagliacozzi D, Shushan B, Federici G. Development of a method for rapid quantitation of amino acids by liquid chromatography-tandem mass spectrometry (LC-MSMS) in plasma. Clin Chem Lab Med. 2000;38(5):391–401. doi: 10.1515/CCLM.2000.057. [DOI] [PubMed] [Google Scholar]
- 33.Miller MJ, Cusmano-Ozog K, Oglesbee D, Young S. Laboratory analysis of acylcarnitines Update: a technical standard of the American College of Medical Genetics and Genomics (ACMG) Genetics in Medicine. 2020;2020:1–10. doi: 10.1038/s41436-020-00990-1. [DOI] [PubMed] [Google Scholar]
- 34.Li S, Gao D, Jiang Y. Function, detection and alteration of acylcarnitine metabolism in hepatocellular carcinoma. Metabolites. 2019;9(2):36. doi: 10.3390/metabo9020036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Harder U, Koletzko B, Peissner W. Quantification of 22 plasma amino acids combining derivatization and ion-pair LC–MS/MS. J Chromatogr B. 2011;879(7–8):495–504. doi: 10.1016/j.jchromb.2011.01.010. [DOI] [PubMed] [Google Scholar]
- 36.Carling RS, John K, Churchus R, Turner C, Dalton RN. Validation of a rapid, comprehensive and clinically relevant amino acid profile by underivatised liquid chromatography tandem mass spectrometry. Clinical Chemistry and Laboratory Medicine (CCLM). 2020;58(5):758–768. doi: 10.1515/cclm-2019-0604. [DOI] [PubMed] [Google Scholar]
- 37.Perich C, Minchinela J, Ricós C, Fernández-Calle P, Alvarez V, Doménech MV, Simón M, Biosca C, Boned B, García-Lario JV, Cava F, Fernández-Fernández P, Fraser CG. Biological variation database: structure and criteria used for generation and update. Clinical Chemistry and Laboratory Medicine (CCLM) 2015;53(2):299–305. doi: 10.1515/cclm-2014-0739. [DOI] [PubMed] [Google Scholar]
- 38.Westgard Q. Biological variation database, and quality specifications for imprecision, bias and total error (desirable and minimum). The 2014 update. 2014.
- 39.Lau T, Owen W, Yu YM, Noviski N, Lyons J, Zurakowski D, Tsay R, Ajami A, Young VR, Castillo L. Arginine, citrulline, and nitric oxide metabolism in end-stage renal disease patients. J Clin Invest. 2000;105(9):1217–1225. doi: 10.1172/JCI7199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Mai M, Tönjes A, Kovacs P, Stumvoll M, Fiedler GM, Leichtle AB. Serum levels of acylcarnitines are altered in prediabetic conditions. PLoS One. 2013;8(12):e82459. doi: 10.1371/journal.pone.0082459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Rahimi N, Razi F, Nasli-Esfahani E, Qorbani M, Shirzad N, Larijani B. Amino acid profiling in the gestational diabetes mellitus. Journal of Diabetes & Metabolic Disorders. 2017;16(1):13. doi: 10.1186/s40200-016-0283-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Sun L, Liang L, Gao X, Zhang H, Yao P, Hu Y, Ma Y, Wang F, Jin Q, Li H, Li R, Liu Y, Hu FB, Zeng R, Lin X, Wu J. Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study. Diabetes Care. 2016;39(9):1563–1570. doi: 10.2337/dc16-0232. [DOI] [PubMed] [Google Scholar]
- 43.Chuang W-H, Arundhathi A, Lu C, Chen C-C, Wu W-C, Susanto H, Purnomo JDT, Wang CH. Altered plasma acylcarnitine and amino acid profiles in type 2 diabetic kidney disease. Metabolomics. 2016;12(6):108. doi: 10.1007/s11306-016-1049-y. [DOI] [Google Scholar]
- 44.Goek O-N, Döring A, Gieger C, Heier M, Koenig W, Prehn C, Römisch-Margl W, Wang-Sattler R, Illig T, Suhre K, Sekula P, Zhai G, Adamski J, Köttgen A, Meisinger C. Serum metabolite concentrations and decreased GFR in the general population. Am J Kidney Dis. 2012;60(2):197–206. doi: 10.1053/j.ajkd.2012.01.014. [DOI] [PubMed] [Google Scholar]
- 45.Fouque D, Holt S, Guebre-Egziabher F, Nakamura K, Vianey-Saban C, Hadj-Aïssa A, Hoppel CL, Kopple JD. Relationship between serum carnitine, acylcarnitines, and renal function in patients with chronic renal disease. J Ren Nutr. 2006;16(2):125–131. doi: 10.1053/j.jrn.2006.01.004. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
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(PDF 174 kb)
Data Availability Statement
The dataset supporting the conclusions of this article is available and can be accessed by contacting the corresponding author.