Biomarkers Identified by Urinary Metabonomics for ... - ACS Publications

Article pubs.acs.org/jpr

Biomarkers Identified by Urinary Metabonomics for Noninvasive Diagnosis of Nutritional Rickets Maoqing Wang,† Xue Yang,† Lihong Ren,‡ Songtao Li,† Xuan He,† Xiaoyan Wu,† Tingting Liu,† Liqun Lin,† Ying Li,*,† and Changhao Sun*,† †

National Key Disciplines of Nutrition and Food Hygiene, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin 150081, P. R. China ‡ Department of Pediatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, 148 BaoJian Street, Nan Gang District, Harbin, Heilongjiang Province, P. R. China S Supporting Information *

ABSTRACT: Nutritional rickets is a worldwide public health problem; however, the current diagnostic methods retain shortcomings for accurate diagnosis of nutritional rickets. To identify urinary biomarkers associated with nutritional rickets and establish a noninvasive diagnosis method, urinary metabonomics analysis by ultra-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis were employed to investigate the metabolic alterations associated with nutritional rickets in 200 children with or without nutritional rickets. The pathophysiological changes and pathogenesis of nutritional rickets were illustrated by the identified biomarkers. By urinary metabolic profiling, 31 biomarkers of nutritional rickets were identified and five candidate biomarkers for clinical diagnosis were screened and identified by quantitative analysis and receiver operating curve analysis. Urinary levels of five candidate biomarkers were measured using mass spectrometry or commercial kits. In the validation step, the combination of phosphate and sebacic acid was able to give a noninvasive and accurate diagnostic with high sensitivity (94.0%) and specificity (71.2%). Furthermore, on the basis of the pathway analysis of biomarkers, our urinary metabonomics analysis gives new insight into the pathogenesis and pathophysiology of nutritional rickets. KEYWORDS: biomarker, metabonomics, nutritional rickets, urine, UPLC−Q-TOF-MS/MS

■

INTRODUCTION Nutritional rickets is a mineralization disorder of growing bone, especially in infants.1,2 Nutritional rickets remains a worldwide, long-standing public health issue and is prevalent in both developing3,4 and developed countries.5,6 In China, the most conservative prevalence rate of nutritional rickets among children under age 5 years is reported to be 15.9%, with rates among infants of 26.7%.4 Although serum biochemical tests, clinical symptoms, and radiography suffice for the diagnosis of active nutritional rickets and are available in most settings, these diagnostic tools retain shortcomings in the diagnosis of nutritional rickets,7 especially for early stage of nutritional rickets. Serum biochemical tests (25OHD3, calcium, phosphorus, alkaline phosphatase (AP), and PTH) are invasive and unpleasant for children and their parents; moreover, the sensitivity and specificity are relatively poor.8,9 For example, serum calcium levels may be normal or reduced in the early stage of rickets. The levels of 25OHD3 not only identify vitamin D deficiency but also may be low in calcium-deficient nutritional rickets.10 Clinical symptoms and bone radiographic changes may reflect active or long-term nutritional rickets, and © 2014 American Chemical Society

the sequelae of nutritional rickets or healed nutritional rickets,4.11,12 Long-term or active nutritional rickets leads to severe growth retardation and bony deformities of children. Therefore, clinical symptoms and bone radiographic changes are not well-suited for the early diagnosis of nutritional rickets. Additionally, the detailed pathophysiological changes and pathogenesis of nutritional rickets remain elusive. For example, was nutritional rickets due to vitamin D deficiency, calcium deficiency, or both? For the aforementioned reasons, the need for screening of biomarkers associated with rickets is urgent for early guiding diagnosis and clinical treatment of nutritional rickets as well as elucidating the pathophysiological changes and pathogenesis underlying the conditions. Metabonomics, the recent omics approach, offers an alternative approach to characterize biomarkers or define perturbations of diseases in accessible biological samples,13 such as urine14,15 and plasma.16 Ultra-performance liquid chromatography−tandem mass spectrometry (UPLC−MS/ Received: May 27, 2014 Published: July 22, 2014 4131

dx.doi.org/10.1021/pr500517u | J. Proteome Res. 2014, 13, 4131−4142

Journal of Proteome Research

Article

Figure 1. Overview of workflow utilized in the identification of biomarkers of nutritional rickets.

■

MS) has been applied widely in metabonomics study for its high sensitivity, resolution, and rapid separation and has confirmed potential in biomarker discovery17−19 and pathogenesis elucidation of various diseases.14,20,21 To date, there has been no report of metabonomics approach focused on nutritional rickets. Therefore, in this study, an unbiased global urinary metabonomics based on UPLC− quadrupole time-of-flight tandem mass spectrometry (Q-TOFMS/MS) coupled TO multivariate statistical analysis was used to identify potential biomarkers and unravel the molecular mechanisms of nutritional rickets. First, 200 urine samples from children with nutritional rickets and healthy controls were analyzed for the metabonomics study using UPLC−Q-TOFMS/MS. Prospective biomarkers of nutritional rickets were revealed, and two candidate biomarkers were selected for the diagnosis of nutritional rickets. Second, to validate the diagnostic ability of the candidate biomarkers and to establish a noninvasive and accurate diagnostic method for nutritional rickets, the concentrations of two candidate biomarkers in urine were measured in another 225 children. Finally, we tried to characterize the pathophysiological changes and pathogenesis of nutritional rickets using the identified biomarkers.

MATERIALS AND METHODS

Chemicals and Regents

Acetonitrile and methanol (chromatographic grade) were purchased from Honeywell, Burdick, & Jackson (Muskegon, MI). Formic acid and ethyl acetate (analytical grade) were purchased from the Beijing Reagent Company (Beijing, China). The 25OHD3 and leucine-enkephalin were purchased from Sigma Aldrich (St. Louis, MO). Standards of the highest grade available were obtained from commercial sources. Ultrapure water was prepared by an ultra clear system (PURELAB Ultra, Veolia Water Solutions & Technologies, France). Subjects

The overall workflow utilized in the identification of biomarkers of nutritional rickets is summarized in Figure 1. The project was approved by the Ethics Committee of Harbin Medical University. Informed consent was obtained from the parents or guardians of all children before entering this study. Children with nutritional rickets (6−38 months old) were recruited from the pediatric outpatient department of the Second Affiliated Hospital of Harbin Medical University from June 20 to July 18, 2010. Clinical examinations of all children (health and rachitic children) were conducted by experienced pediatricians. The 5 mL of fasting blood was collected, and serum concentrations of calcium, phosphorus, AP, PTH, and 25OHD3 were measured. Radiographic signs of nutritional rickets were supportive of the 4132



Article

control and analyzed every 15th samples throughout the analytical run. UPLC−QTOF-MS/MS Analysis. A 2 μL sample solution was injected into an Acquity UPLC HSS T3 column (100 × 2.1 mm; id. 1.7 μm; Waters) at 35 °C using the Waters Acquity UPLC system (Waters, Milford, MA). The flow rate of the mobile phase was 0.35 mL/min. Analytes were eluted from the column under a gradient (solvent A, 0.1% formic acid in water; solvent B, acetonitrile). The elution gradient was as follows: 2% B for 0.5 min, 2−20% B over 0.5−6.0 min, 20−35% B over 6.0−7.0 min; 35−70% B over 7.0−9.0 min; 70−98% B over 9.0−10.5 min; 98% B for 2.0 min, returned to 2% B for 6.0 min. Acetonitrile as a blank solution was run every fifth sample, and the urine samples in two analysis batches were injected alternately between five health infants and five infants with rickets. Mass spectrometry was performed using a Waters Micromass Q-TOF (Waters, Manchester, U.K.) equipped with an electrospray ionization source operating in negative-ion mode (ESI−). The parameters were as follows: capillary voltage, 2800 V; sample cone voltage, 35 V; collision energy, 6 eV; source temperature, 100 °C; desolvation gas (nitrogen) flow, 650 L/h; desolvation temperature, 320 °C; cone gas (nitrogen) flow, 50 L/ h; collision gas, argon; MCP detector voltage, 2550 V. The mass acquisition rate was set at 0.4 s with a 0.1 s interscan delay. The scan mass range was from 50−1000 m/z. The Q-TOF-MS/MS data were collected in centroid mode using the lock spray to ensure accuracy and reproducibility. A concentration of 200 pg/ mL leucine−enkephalin was used as lock mass (m/z 554.2615) in ESI−. The lock spray frequency was set at 10 s, and the lock mass data were averaged over 10 scans for correction. The MS/ MS spectra of potential biomarkers were obtained by UPLC− MS/MS. Data Processing. The raw data were imported into the MarkerLynx software incorporated in the Masslynx software (version 4.1 SCN 714). MarkerLynx ApexTrack peak integration was used for peak detection and alignment. The ApexTrack peak parameters were set as follows: peak width at 5% height, 1 s, and peak-to-peak baseline noise (calculated automatically). Collection parameters were set as follows: retention time (RT) range 0.5−10.5 min, mass range 50−1000 Da, mass tolerance, 0.05 Da; RT tolerance, 0.1 min; minimum intensity, 80; noise elimination level, 6.0; deisotope data, Yes. After being recognized and aligned, the peak area of each ion was normalized to the summed total ion intensity of each chromatogram to take into account the variation in urine concentration and volume. The 3-D data including peak number (RT−m/z pair), sample name and normalized peak areas were exported to the EZinfo software (version 2.0.0.0, June 5, 2008, Waters) for multivariate statistics analysis. Data reduction was handled according to the “80% rule”; thus, only the variables with values greater than zero presenting in at least 80% of each group were kept for the following analysis.23 The data were Pareto-scaled prior to multivariate statistical analysis. The supervised partial leastsquares-discriminant analysis (PLS-DA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were applied to visualize the maximal difference between two groups. A default seven-fold (leave-1/seventh samples-out) crossvalidation procedure and 100 random permutations testing were carried out to avoid over fitting of supervised PLS-DA models by SIMCA-P version 11.5 software (Umetrics, Umeå, Sweden). The variable importance in the projection (VIP) values of all peaks from the OPLS-DA model was taken as a coefficient for

diagnosis. The diagnosis of rickets was made after evaluating the available clinical, radiological, and biochemical parameters. The inclusion criteria were as follows: children with a low serum level of 25OHD3 (250 IU/L. Exclusion criteria included current treatment with antiinflammatory or other medications; primary hyperparathyroidism; gastrointestinal disorders; liver disease; renal insufficiency; poliomyelitis; inherited bone disease; acute or chronic infection with clinical or biochemical evidence; heritable disorder of vitamin D metabolism including 1-α-hydroxylase deficiency and vitamin D receptor defects; or hypophosphatemic rickets. Children with nutritional rickets who had recently received vitamin D3 supplementation during the previous 4 weeks were excluded before 25OHD3 was tested. Children with nutritional rickets were excluded based on the inclusion criteria (7) and were not collected urine or serum samples (3). At last, 200 children with nutritional rickets (88) and healthy controls (112) were recruited for the urinary metabonomics study using UPLC−QTOF-MS/MS. The matched healthy children were recruited from the infant schools and the communities in Harbin city. Detailed face-to-face interviews of parents or guardians of children with and without nutritional rickets were conducted by trained interviewers to collect questionnaire information on 3 day diet records, VD3 supplement history, type of feeding, duration of exposure to sunlight, and demographic characteristics. Using Chinese foodcomposition Tables (2002), we calculated dietary calcium and phosphate intake by asking parents or guardians about the infant’s food intake. Dietary intake of vitamin D was not determined. A total of 20 mL of fasting urine samples was collected from all children in the morning by their parents. All urine samples were collected and taken to lab within 5 h. Quickly, the urine samples were centrifuged at 3000 rpm for 10 min at 4 °C to remove suspended debris, and the supernatants without any preservatives were immediately stored at −80 °C until analysis. Serum Biochemical Analysis

Serum calcium, phosphorus, and AP were determined using commercial Kits (Wako Pure Chemical Industries) using a Hitachi 7100 Automatic Biochemistry Analyzer (Hitachi HighTechnologies, International Trading,, Shanghai, China). PTH was determined using commercial PTH Elisa Kits (Uscn Life Science). Serum 25OHD3 levels were measured by a modified method (Supplementary Method 1 in the Supporting Information).22 Global Urinary Metabolic Profiling Analysis by UPLC−Q-TOF-MS/MS

The method of global urinary metabolic profiling analysis by UPLC−Q-TOF-MS/MS was the same as that of ref 29. Sample Preparation. Urine samples were thawed at room temperature, diluted 1:1 (v/v) with water, vortex-mixed for 1 min, and then centrifuged at 12 000 rpm for 10 min. The supernatants were transferred to autosampler vials. To verify the reproducibility and reliability of the data, we prepared a typical pooled quality-control (QC) sample by mixing equal volumes of urine samples from five children with rickets and five health 4133



Article

Table 1. Characteristics of Children with and without Nutritional Rickets in the Screening Step characteristic

children without nutritional rickets

children with nutritional rickets

number age (months) 6−12 13−24 24−36 gender dairy intake of calcium (mg/day) dairy intake of phosphorus (mg/day) vitamin D supplementation (%) duration of exposure to sunlight (>2 h) (%) serum calcium (mmol/L) serum phosphorus (mmol/L) serum alkaline phosphatase (U/L) PTH (pmol/L) serum 25OHD3 (ng/mL) tetany craniotabes fontanelle delayed closure frontal bossing leg bowing rib eversion rachitic rosary wrist/ankle swelling radiographic changes

112 19 (6−35) 17 39 56 64 male/38 female 421 ± 83 552 ± 102 40.2 57.1 2.54 ± 0.03 1.74 ± 0.04 192.0 ± 15.7 2.3 ± 0.2 22.5 ± 7.7

88 19 (6−36) 17 33 38 58 male/30 female 418 ± 101 564 ± 137 19.3 28.4 2.48 ± 0.02 1.64 ± 0.04 231.4 ± 20.5 5.2 ± 0.3 14.2 ± 7.4 6 17 26 65 4 16 6 4 54

peak selection. The Student’s t test or Wilcoxon (Mann− Whitney) test was applied to measure the significance of each metabolite. Metabolites with both multivariate and univariate statistical significance (VIP >1.5 and P < 0.05) were considered to be markers responsible for the differentiation of children with rickets from healthy children. Heatmap visualization of the peak area of the biomarkers between two groups was performed with MetaboAnalyst2.0 (a comprehensive server for metabolomic data analysis). Identification of Biomarker. The identification of biomarkers was achieved by comparison with online free databases such as human metabolome database (http://www.hmdb.ca/) and Metlin (http://metlin.scripps.edu/) using exact m/z values and MS/MS spectra and validation with available standard compounds. If the exact mass was not found in online databases, the MassFragment application manager (Waters MassLynx v4.1, Waters) was used to facilitate the MS/MS fragment ion analysis process and achieve the molecular formula of the biomarkers by way of chemically intelligent peak-matching algorithms. Metabolic Pathway Analysis. The pathway analysis of potential biomarkers (including pathway enrichment analysis, pathway topology analysis and visualization) was first performed with the MetaboAnalyst based on database sources including KEGG (http://www.genome.jp/kegg/) and the Human Metabolome Database (http://www.hmdb.ca/) to identify the top altered pathways analysis and visualization.24 Then, other implicated pathways of biomarkers were further interpreted using references and databases.

P

0.650 0.752 0.561 0.002 0.003 0.026 0.012 0.035 0.008 0.001

(AAT Bioquest), respectively. Quantitative assays were performed according to the protocol of the commercial assay kits. The levels of citric acid and sebacic acid in urine were measured by UPLC−Q-TOF-MS/MS. The concentrations of five biomarkers in urine were normalized to the concentration of urine creatinine (Supplemental Table 1 in the Supporting Information). The line ranges and limits of detection of five biomarkers were provided in Supplementary Table 3 in the Supporting Information. Validation Step of Candidate Biomarkers of Nutritional Rickets

Children with (n = 130) and without nutritional rickets (n = 95) were recruited in Harbin city from December 2, 2012 to January 26, 2013. The inclusion and exclusion criteria of children with and without nutritional rickets were the same as the screening step. The serum and urine samples were collected from 225 children, pretreated, and measured by the same methods as the screening steps. The urine samples in the validation step were not used to investigate the metabolism alterations by urinary metabonomics. Only candidate biomarkers for diagnosis of nutritional rickets in urine samples were measured by the previously described quantitative methods. Statistical Analysis

Serum biochemical indicators were expressed as mean ± SD. Differences between the two groups were analyzed by an independent t test. A two-tailed value of P < 0.05 was considered to be statistically significant. The receiver operating characteristic curve analysis (ROC) analysis, area under the curve (AUC), Chisquare test, and multifactor logistic regression were performed using SPSS software version 16.0 (SPSS, Chicago, IL). SigmaPlot 11.0 software (Systat Software, San Jose, CA) was used to analyze differences between the AUCs of the biomarkers.

Quantitative Analysis of Five Biomarkers in Urine

Urine creatinine and phosphate were determined using commercial kits (Beijing Bioassay Technologies and Wako Pure Chemical Industries) using a Hitachi 7100 Automatic Biochemistry Analyzer. Urine cAMP and pyrophosphate were measured using a cAMP Parameter Assay Kit (R&D Systems) and PhosphoWorks Fluorimetric Pyrophosphate Assay Kit 4134



■

Article

RESULTS

alterations between the two groups. The validation plot strongly indicated that the PLS-DA model was valid (Supplementary Figure 2 in the Supporting Information) because the Q2 regression line in blue had a negative intercept and all permuted R2-values in green on the left were lower than the original point of the R2 value on the right. Orthogonal projection to latent structures−discriminant analysis (OPLS-DA) (Two components: R2Y = 0.83 and Q2 = 0.70) showed more clear differences in the urinary metabolite between the two groups. In total, 31 different metabolites between children with nutritional rickets and healthy children were selected by VIP in Table 2. As shown in Supplementary Figure 3−5 in the Supporting Information, clear separations were observed between children with nutritional rickets and healthy children for all subgroups (6−12, 13−24, and 25−38 months). This showed more clear differences in the urinary metabolites. The different metabolites between children with nutritional rickets and healthy children for all subgroups were also screened by VIP of PLS-DA. In particular, as shown in Supplementary Table 2 in the Supporting Information, the 31 identified metabolites were all found in all three age subgroups. Therefore, we could conclude that the 31 metabolites in urine were the biomarkers of nutritional rickets instead of the age-related alterations. The heatmap visualization (Figure 3) of 31 biomarkers between the children with nutritional rickets and healthy children showed distinct segregation. Compared with children with nutritional rickets, 7 biomarkers were upregulated in healthy children, whereas 24 biomarkers were downregulated. Identities of 10 biomarkers were confirmed using standard compounds. The other biomarkers were identified only by the MS/MS spectra (6) from web databases or MassFragment application manager (15) by way of chemically intelligent peak-matching algorithms (Supplementary Data 1 in the Supporting Information).

Characteristics of Subjects in the Two Groups

In comparison with the healthy subjects, significant differences in the serum concentrations of 25(OH)VD3, calcium, PTH, phosphorus, AP, proportion of vitamin D supplementation (%), and the duration of exposure to sunlight were observed in the children with nutritional rickets (Table 1). Children with nutritional rickets all had clinical manifestations of nutritional rickets, such as craniotabes, fontanelle-delayed closure, or frontal bossing. Fifty-four children with nutritional rickets had radiographic changes. There were no significant differences for age, gender, and calcium and phosphate dietary intakes between the two groups (Table 1). Data Quality Assessment of Metabonomics Platform

The relative standard deviations (RSDs; %) of RT and peak area in the QC samples ranged from 0.0−1.0 and 0.8−4.0, respectively. The precision and repeatability of the experiments were excellent for both discovery and validation sample (Supplementary Table 1 in the Supporting Information). Urinary Metabolic Profiling of the Children with Nutritional Rickets and Healthy Children

The typical based peak intensity (BPI) chromatograms of urinary samples of the two groups are shown in Supplementary Figure 1 in the Supporting Information. The data (2176 variables) were used for multivariate statistical analysis. For the analysis, the three age subgroups were considered as a single cohort (aged 6−38 months). As displayed by the PLS-DA and OPLS-DA scores plots (Figure 2), children with nutritional rickets and healthy children could be separated into distinct clusters (PLS-DA: Four components: R2X = 0.244, R2Y = 0.882, and Q2 = 0.726), which further revealed distinct metabolic

Quantifications of Five Biomarkers and ROC Analysis in the Screening Step

The normalized concentrations of five biomarkers (M1, phosphate; M2, pyrophosphate; M3, citric acid; M4, cAMP; M5, sebacic acid) are shown in Table 3. Compared with control, the levels of citric acid, cAMP, and sebacic acid were significantly lower in children with nutritional rickets (P < 0.001), whereas phosphate and pyrophosphate concentrations were significantly higher (P < 0.000). The AUCs of ROC for single biomarkers are shown in Table 3 and Figure 3. The AUC values of sebacic acid were the highest between the AUCs of single biomarkers. Logistic regression was used to combine several different variables into a multivariable. The AUCs of all different combinations of five biomarkers are shown in Supplementary Table 4 in the Supporting Information. When the two biomarkers were combined, only the AUCs of sebacic acid and phosphate combination (AUC: 0.842; sensitivity: 93.5% and specificity: 69.3%) were significantly higher than the AUC of phosphate (P = 0.037) (the highest AUC in five biomarkers). When the three different biomarkers were combined, only the AUCs of three combinations (M125, M135, and M145) were significantly higher than the AUC of phosphate. When the four different biomarkers were combined, the AUCs of the combinations of M1235, M1345, and M1245 were higher than the AUC of phosphate. When the five biomarkers were combined, the AUC reached 0.853 (95% confidence interval: 0.798 to 0.907) with a sensitivity of 94.7% and specificity of 71.7% (Figure 4). No statistically significant differences were

Figure 2. PLS-DA and OPLS-DA scores plots of infant with nutritional rickets and without nutritional rickets. Red triangle: children without nutritional rickets. Black triangle: children with nutritional rickets. (A) PLS-DA scores plots. (B) OPLS-DA scores plots. One data point stands for one subject. 4135



Article

Table 2. Identifications of Biomarkers of Nutrition Ricketsa no.

RT (min)

actual mass

exact mass

mass error (ppm)

molecule composition

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1.20 0.67 5.89 0.84 0.99 1.02 8.19 0.65 6.43 2.29 9.30 6.44 0.67 0.66 8.84 7.77 9.50

191.0173 176.9366 192.0629 129.0193 293.0520 243.0625 201.1134 124.0059 212.0020 328.0451 407.2809 175.0236 194.9456 193.0349 229.1464 245.1393 267.1249

191.0192 176.9354 192.0661 129.0188 293.0484 243.0617 201.1127 124.0068 212.0018 328.0447 407.2797 175.0243 194.9460 193.0348 229.1440 245.1389 267.1232

9.9 6.8 16.7 3.9 12.3 3.3 3.5 7.3 0.9 1.2 2.9 4.0 2.1 0.5 10.5 1.6 6.4

C6H8O7 H4P2O7 C10H11No3 C5h6o4 C14H14O5S C9H12N2O6 C10H18O4 C2H7No3S C8H7No4S C10H11N5O6P C24H40O5 C6H8O6 H6P2O8 C6H10O7 C12H22O4 C12H22O5 C14H20O5

18

8.26

213.1140

213.1127

6.1

C11H18O4

19 20 21 22 23

0.58 8.62 3.62 8.36 1.8

216.9082 227.1392 194.0459 200.1296 263.0251

216.9089 227.1396 194.0453 200.1287 263.0225

3.2 1.8 3.1 4.5 9.9

Nah3S2O8 C11H20N2O3 C9H9No4 C10H19No3 C9H12O7S

24 25 26 27 28 29 30

1.91 5.2 5.93 2.56 7.99 7.87 8.22

260.0267 211.9989 302.1141 181.0379 539.2399 541.2615 481.2432

260.0324 211.9960 302.1141 181.0362 539.2492 541.2649 481.2438

21.9 13.7 0 9.4 17.2 6.3 1.2

C9H12No6P C4H8No7P C15H17N3O4 C6H6N4O3 C27H40O11 C27H42O11 C25H38O9

31

4.02

194.0445

194.0453

4.1

C9H9No4

identify citric acidb,e pyrophosphoric acidb,e phenylacetylglycineb,e monomethyl fumarated,e d,e L-glutamic acid, N-amino]carbonyl] b,e pseudouridine sebacic acidb,e taurineb,e indoxyl sulfatec,e cAMPb,e cholanoic acidc,e c,e D-glucurono-6,3-lactone b,e phosphoric acid 3-dehydro-L-gulonated,f dodecanedioic acidb,f 3-hydroxydodecanedioic acidd,f 3-carboxy-4-methyl-5-pentyl-2furanpropionic acidd,f butanedioic acid, (3-ethylcyclopentyl)-(9ci)d,f sodium sulfateb d L-leucyl-L-proline 3-hydroxyhippuric acidd capryloylglycinec 3-methoxy-4-hydroxyphenylethylene glycol sulfatec O-phosphotyrosined,f d L-aspartyl-4-phosphate indoleacetyl glutamined 3-methyluric acidc tetrahydroaldosterone-3-glucuronided,f cortolone-3-glucuronided,f 11-beta-hydroxyandrosterone-3glucuronided,f dopaquinoned

VIP

fold change

AUC

P

10.1 9.7 2.5 2.7 2.2 8.1 6.9 5.4 2.9 7.1 3.5 3.1 14.0 2.9 3.3 4.3 4.4

−1.7 6.1 1.7 1.9 −1.7 −1.5 −1.9 −1.9 1.4 −1.6 −2.1 3.1 5.3 2.1 −4.3 −1.8 −1.7

0.751 0.871 0.655 0.643 0.645 0.657 0.786 0.829 0.613 0.729 0.732 0.735 0.793 0.732 0.654 0.693 0.652

0.000 0.000 0.000 0.000 0.02 0.000 0.000 0.003 0.002 0.000 0.003 0.002 0.000 0.000 0.002 0.000 0.000

6.6

−1.6

0.68

0.000

4.8 5.2 4.8 4.3 16.7

−1.7 −2.0 −1.8 −1.4 −1.6

0.615 0.774 0.653 0.664 0.718

0.001 0.000 0.001 0.000 0.000

8.6 7.9 3.3 3.3 13.2 7.1 3.0

−1.3 −1.5 −1.3 −1.6 −1.6 −1.5 −1.5

0.677 0.621 0.611 0.671 0.76 0.674 0.650

0.000 0.000 0.013 0.000 0.000 0.000 0.000

5.6

−1.7

0.612

0.000

a

RT, retention time; fold change, children with rickets/health children; bMetabolites were confirmed using standard samples. cMetabolites were identified by MS/MS spectra of database and MS fragmentation. dPossible elemental compositions determined based on MS fragmentation, exact mass data, and retention time. eCalcium deficiency biomarkers identified in calcium-deficient rat. fBiomarkers that could be associated with calcium deficiency.

Metabolic Pathways

observed between the AUCs of the above eight different combinations of two, three, four, and five biomarkers. Thus, the combination of sebacic acid and phosphate was selected as the candidate biomarkers for diagnosis of nutritional rickets.

The potential targets metabolic pathway analysis (impact value ≥0.10) with MetaboAnalyst 2.0 revealed that three metabolic pathways including ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and taurine and hypotaurine metabolism were found to be associated with nutritional rickets (Figure 5, Supplementary Table 6 and Figures 6−8 in the Supporting Information).

Validation of Candidate Biomarkers for Diagnosis of Nutritional Rickets

Significant differences in the serum concentrations of 25OHD3, calcium, phosphate, AP, PTH, proportion of vitamin D supplementation (%), and the duration of exposure to sunlight were observed in the children with nutritional rickets compared with the healthy children (Supplementary Table 5 in the Supporting Information). Children with nutritional rickets all had clinical manifestations of nutritional rickets. In comparison with the healthy children, significant differences in the urine concentrations of sebacic acid and phosphate were observed in the children with nutritional rickets (P < 0.001) (Table 4). As shown in Table 4, the AUC of the combination of sebacic acid and phosphate was 0.850 (95% confidence interval: 0.803 to 0.897) with a sensitivity of 94.0% and specificity of 71.2%.

■

DISCUSSION

Biomarkers for the Diagnosis of Nutritional Rickets

Nutritional rickets is a worldwide public health problem. However, current diagnostic tools fall short in the diagnosis of nutritional rickets. For example, serum biochemical tests and Xrays are invasive and unpleasant for children and their parents. Moreover, the sensitivity and specificity are relatively poor. As shown in Table 1, only 54 of 88 children with early nutritional rickets had bone radiographic changes. Bone radiographic changes reflected active or long-term nutritional rickets and had difficulty in diagnosing the early rickets. Moreover, the X-ray 4136



Article

Figure 3. Heatmap visualization constructed based on 32 biomarkers implemented in MetaboAnalyst2.0. Rows: samples; columns: biomarkers. Green: children with nutritional rickets; red: children without nutritional rickets. Color key indicates metabolite expression value: dark blue: lowest; dark red: highest.

test could increase the unnecessary radiation exposure for the children. Therefore, it is critical to establish a noninvasive and accurate diagnosis method for nutritional rickets. In this study, we first have identified 31 biomarkers associated with nutritional rickets by the urinary metabonomics analysis using UPLC−Q-TOF-MS/MS. These biomarkers revealed great potential for the diagnosis of nutritional rickets. However, it is

unrealistic to apply all 31 biomarkers for diagnosis of nutritional rickets in the clinical setting. Therefore, the key of this study was to select a single or several specific biomarkers from the 31 biomarkers for establishing a noninvasive and accurate diagnostic method for nutritional rickets. To select the candidate biomarkers for diagnosis of nutritional rickets, the candidate biomarker selection rationale was as follows: first, the 4137



Article

Table 3. Results of Concentrations and AUCs of Five Biomarkers in the Screening Step biomarker names



P

AUC

phosphate (μM/mmol) pyrophosphate (μM/mmol) citric acid (μg/mmol) cAMP (pmol/mmol) sebacic acid (ng/mmol) creatinine (mmol/L)

329 ± 331 7.72 ± 11.4 221.5 ± 69 67 ± 41 211.3 ± 99.3 446 ± 335

478 ± 364 28.8 ± 38.5 141.7 ± 138 43 ± 31 92.8 ± 50.8 486 ± 311

0.000 0.000 0.000 0.000 0.000 0.354

0.764 0.679 0.706 0.724 0.756

Figure 4. Areas under ROC curve of the biomarkers and combination of the five biomarkers. Marker 1, phosphate; marker 2, pyrophosphate; marker 3, citric acid; marker 4, cAMP; marker 5, sebacic acid. Red line combination of five biomarkers: M12345, AUC = 0.853.

Table 4. Results of Concentrations and AUCs of Two Candidate Biomarkers and 25OHD3 in the Validation Stepa,b

biomarker names sebacic acid (ng/mmol) phosphate (μM/mmol) combination of sebacic acid and phosphate 25OHD3 (ng/mL) creatinine (mmol/L)



P

AUC

234 ± 188 195 ± 68

120 ± 77 323 ± 16

0.000 0.000

0.724 0.777 0.850

20.5 ± 7.3 648 ± 446

14.3 ± 4.4 621 ± 352

0.000 0.414

0.834

Figure 5. Summary of pathway analysis for biomarkers with in MetaboAnalyst2.0: (1) Ascorbate and aldarate metabolism; (2) pentose and glucuronate interconversions; (3) taurine and hypotaurine metabolism; (4) citrate cycle (TCA cycle); and (5) lysine biosynthesis.

nutritional rickets, its sensitivity and specificity of diagnosis were not high (Table 3). To improve the sensitivity and specificity of diagnosis, we selected several biomarkers from five biomarkers as a combination for the diagnosis of nutritional rickets. As shown in Supplementary Table 2 in the Supporting Information, the combinations of five, four (including M1235, M1345, and M1245), three (including M125, M135, and M145), and two (M15) biomarkers could all more accurately discriminate children with nutritional rickets from healthy children, with higher sensitivity and specificity than the separate biomarker. Compared with the combinations of five, four, and three biomarkers, the combination of phosphate and sebacic acid was not significantly different between the AUCs. However, the combination of phosphate and sebacic acid was easier to detect and cheaper clinically. Additionally, potential confounders (dietary differences and others) may result in different expression levels of phosphate or sebacic acid, but on the basis of 3 day diet records, no significant difference in the phosphate dietary intake between the disease and control groups was observed. The dietary structure was used to assess the difference of dietary between the children with rickets and healthy controls. In the multivariate logistic regression, total energy intake was used as the substitute variable of dietary structure, and no significant impact on sebacic acid was observed when this variable (total energy intake) was put in model or moved out. The beta value of

a

Combination of phosphate and sebacic acid: sensitivity: 94.0%; specificity: 71.2%. b25OHD3: sensitivity: 94.0%; specificity: 72.9%

biomarkers, which must be confirmed by standards; second, the biomarkers with higher VIP values and AUCs; and last, the biomarkers were easier an measurement using more clinically accessible assays. So, five biomarkers (phosphate, sebacic acid, pyrophosphate, cAMP, and citric acid) were selected as the candidate biomarkers. To facilitate and assess the clinical application of the candidate biomarkers, we quantitatively measured the levels of five biomarkers in urine using an automatic biochemistry analyzer, ELISA assay, or UPLC−Q-TOF MS as well as ROC analysis. Consistent with the result of the metabonomics study (relative quantitative analysis), statistically significant differences in the concentrations of five biomarkers were observed between the two groups (Table 3). Although the results of the ROC analysis suggested that the separate biomarker could be used to diagnose 4138



Article

significantly increased pyrophosphate and inorganic phosphate and decreased cAMP were found in the urine of children with nutritional rickets. The increased secretion of PTH in serum in rachitic infants was responsible for the activation of adenylate cyclase and the production of cytoplasmic cAMP and pyrophosphate. For restoring serum calcium levels, the increased cAMP in serum could result in an increased concentration of cytoplasmic Ca2+ and promoted renal tubule reabsorption of calcium as the second messenger. Therefore, the excretion of cAMP in urine was decreased. In our previous study, three biomarkers were found to be associated with calcium deficiency by urinary metabonomics.29 These metabolic alterations suggested that children with nutritional rickets could be in calcium deficiency, and calcium deficiency led to impaired calcium and phosphorus metabolism and ultimately led to nutritional rickets. Fatty Acid Oxidation Metabolism. Sebacic acid, dodecanedioic acid, 3-hydroxydodecanedioic acid, 3-carboxy-4-methyl5-pentyl-2-furanpropionic acid, and butanedioic acid (3-ethylcyclopentyl)-(9CI) were identified as the biomarkers of early nutritional rickets in children. Sebacic acid, dodecanedioic acid, and 3-hydroxydodecanedioic acid are 10- or 12-carbon mediumchain dicarboxylic acids. Another two biomarkers also are dicarboxylic acids. Previous reports have documented that the medium-chain dicarboxylic acids (such as sebacic acid and dodecanedioic acid) are naturally occurring substances formed by cytochrome P-450-mediated ω-oxidation of fatty acids in the cytosol of the cells.30 These significantly decreased dicarboxylic acids in the urine of children with nutritional rickets suggest that ω-oxidation of fatty acids could be impaired in children with nutritional rickets. Glucuronidation Metabolism. Glucuronidation is used to assist in the excretion of toxic and potentially toxic substances from our system (such as steroid hormones), drugs, or other substances that cannot be used as an energy source. Cortolone-3glucuronide, 11-beta-hydroxyandrosterone-3-glucuronide, and tetrahydroaldosterone-3-glucuronide are the natural human metabolites of 11-beta-hydroxyandrosterone, tetrahydroaldosterone, and cortolone generated in the liver by UDP glucuronyltransferase. A previous study indicated that Ca2+ played an important role in glucuronidation reactions, and the formation of glucuronides was markedly enhanced by Ca2+ addition in isolated hepatocytes.31 Therefore, three significantly decreased glucuronidated products in urine suggested that children with nutritional rickets could be in calcium deficiency; calcium deficiency led to the reduction of glucuronidation reactions. Other Metabolic Pathways. Phenylacetylglycine, indoxyl sulfate, and citric acid were observed in our previous urinary metabonomics study of calcium deficiency.29 Therefore, the significantly increased phenylacetylglycine and indoxyl sulfate and decreased citric acid suggest that children with nutritional rickets were in calcium deficiency, and calcium deficiency led to nutritional rickets. To summarize the metabolic pathway analysis of biomarkers, we found that 23 biomarkers of nutritional rickets were associated with calcium metabolism. In this study, ophosphotyrosine, cortolone-3-glucuronide, 3-dehydro-L-gulonate, dodecanedioic acid, 3-hydroxydodecanedioic acid, 11beta-hydroxyandrosterone-3-glucuronide, tetrahydroaldosterone-3-glucuronide, 3-carboxy-4-methyl-5-pentyl-2-furanpropionic acid, and butanedioic acid (3-ethylcyclopentyl)-(9CI) were found to be associated with calcium metabolism.

sebacic acid in the multivariate logistic regression remained unchanged as −0.018(P < 0.000). Therefore, we concluded that the different expression level of phosphate and sebacic acid in urine was not associated with dietary or aromatics and antiseptics between two groups. Therefore, we selected phosphate and sebacic acid as a combination for diagnosis of nutritional rickets in the clinical setting. To eliminate potential confounders and confirm the clinical diagnostic ability of the combination, another 225 children with or without nutritional rickets were recruited, and the quantitative analysis of phosphate and sebacic acid in the urine was repeated. The result of the ROC analysis indicated that the panel of two biomarkers also was able to diagnosis nutritional rickets with high sensitivity and specificity (AUC: 0.850; sensitivity: 94.0%; specificity: 71.2%) (Table 4). These results indicated that phosphate or sebacic acid were the reliable biomarkers of nutritional rickets. The serum level of 25OHD3 was considered to be a common diagnostic indicator for nutritional rickets.3,5 The diagnostic value for the combination of serum calcium and 25OHD3 (sensitivity: 95.0% and specificity: 82.3%) and five serum markers (sensitivity: 95.1% and specificity: 84.2%) were all higher than that of the combination of phosphate and sebacic acid (sensitivity: 94.0%; specificity: 71.2%) from urine. However, compared with the AUC of 25OHD3 (AUC: 0.834; sensitivity: 94.0%; specificity: 72.9%), the AUC of the combination of phosphate and sebacic acid was not significantly different (P = 0.735). Taking into consideration the convenience and noninvasive nature of the test, urinary analysis was more easily and pleasantly accepted by children and their parents than the serum test. The results indicated that the combination of phosphate and sebacic acid in the urine not only had sufficient sensitivity and specificity to distinguish nutritional rickets from healthy controls but also had the potential to be developed into a clinically useful diagnostic method. Metabolic Pathways Analysis

Aside from the diagnostic value of the biomarkers, the biomarkers also could contribute to further insights into the pathophysiological changes and pathogenesis of nutritional rickets. Metabolic Pathways Analysis by MetaboAnalyst 2.0. Ascorbate and Aldarate Metabolism. 3-Dehydro-L-gulonate and D-glucurono-6,3-lactone are the intermediates in ascorbate and aldarate metabolism. It has been reported that ascorbic acid is necessary for the maintenance of connective tissue and bone, and it is an electron donor for enzymes involved in collagen hydroxylation.25,26 The results suggest that the alteration of ascorbate and aldarate metabolism could interpret the reason for the damages of the connective tissue, bone, and bone collagen of children with nutritional rickets. Taurine and Hypotaurine Metabolism. Taurine is a facilitator in the transport of calcium ions and influences bone metabolism.27,28 In our previous urinary metabonomics study of calcium deficiency, we have found that the decreased taurine level was associated with calcium deficiency.29 In this study, the significantly decreased taurine suggested that the children with nutritional rickets were in calcium deficiency, and calcium deficiency led to nutritional rickets. With our current knowledge and experiment results, we cannot interpret the specific relationship between pentose and glucuronate interconversions with nutritional rickets. Metabolic Pathways Analysis by References and Databases. Calcium and Phosphorus Metabolism. The 4139



Article

Interestingly, 13 of 23 biomarkers (Table 2, footnote e) of nutritional rickets have been identified as reliable calciumdeficient biomarkers using the same urinary metabonomics.29

The pathway analysis of the identified biomarkers gave a new and comprehensive insight into the pathophysiological changes of nutritional rickets.

■

Vitamin D Deficiency, Calcium Deficiency, and Nutritional Rickets

ASSOCIATED CONTENT

S Supporting Information *

Nutritional rickets is used to be considered as a disease caused by vitamin D deficiency,32 but more and more studies indicated that calcium deficiency was an important cause of rickets, particularly in tropical countries.33 However, to date, the underlying molecular mechanism of pathogenesis of nutritional rickets was not clear. Vitamin-D-Deficiency Rickets. Vitamin D deficiency could lead to the depletion of vitamin D stores, which cause the decreased calcium absorption in small intestine and further result in the clinical manifestations of rickets. Until recently, it was widespread agreement that rickets can be curved only by vitamin D, but Underwood and Weinstein provided firm evidence that vitamin D, and more specifically 1,25(OH)VD3, was not directly necessary for bone growth and mineralization34,35 and decreased availability of calcium and phosphorus may be the sole basis of the mineralization defect seen in vitamin D deficiency.36,37 These studies suggested that vitamin D deficiency resulted in inadequate calcium absorption, which led to calcium deficiency; calcium deficiency impaired mineralization of bone and induced the clinical features of rickets Calcium-Deficiency Rickets. It has not yet reached a broad consensus that calcium deficiency by itself can cause rickets, but more and more reports of rickets among infants attributable to extremely low dietary calcium intakes in the presence of adequate vitamin D intakes were published from a number of countries, such as South Africa,38,39 Nigeria,5,40 Belgium,41 the United States,7 and India.42 Clements proposed that the pathogenesis of rickets in the Asian community in the United Kingdom was attributable to low-calcium diet, which induced mild hyperparathyroidism and elevation of 1,25(OH)2D concentrations, with a resultant reduction in vitamin D status.43 Nigerian children with calcium-deficiency rickets cannot be cured by given an oral dose of vitamin D, but this rickets can be healed or prevented by the supplementation with high dietary calcium alone or a combination of calcium and vitamin D.5,44,45 Therefore, a low calcium intake or calcium deficiency could induce rickets by itself and plays an important role in the pathogenesis of nutritional rickets. Our results, combined with the results of previous studies, illustrated the pathogenesis of nutritional rickets. Calcium deficiency brought about the pathophysiological changes in bone and ultimately led to nutritional rickets. Therefore, our results suggest the level of metabolites related to calcium deficiency (insufficient calcium intake, inadequate calcium absorption, or both) could be the main pathogenesis of nutritional rickets, and the adequate calcium intake is a key point in preventing nutritional rickets. Therefore, we recommend the adequate intake of calcium must be ensured before supplying vitamin D for the infants and young children with nutritional rickets.

Precision and reproducibility of method from four ions of the QC sample in the negative ESI mode (Supplementary Table 1). Lists of biomarkers associated with nutritional rickets at difference age subgroups (Supplementary Table 2). Lines ranges and limits of detection of five biomarkers (Supplementary Table 3). All AUCs of difference combinations of five biomarkers (Supplementary Table 4). Characteristics of infants with and without nutritional rickets in the validation step (Supplementary Table 5). Result from pathway analysis by MetaboAnalyst 2.0 (Supplementary Table 6). Typical UPLC−Q-TOF-MS/MS based peak intensity chromatogram of infant urine in ESI negative ion mode (Supplementary Figure 1). The validation plot of permutation testing of PLS-DA model (Supplementary Figure 2). PLS-DA scores plots of infants with nutritional rickets and without nutritional rickets for all age subgroups and each age subgroup and the validation plot of permutation testing of PLSDA model (Supplementary Figures 3−5). Construction of metabolism pathways associated with rickets (Supplementary Figures 6−8). Detailed information about metabolites identification by MassFragment (Supplementary Data). This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Authors

*Fax: (86)-451-87502885. Tel: (86)-451-87502731. E-mail: [email protected] (Y.L.). *Fax: (86)-451-87502885. Tel: (86)-451-87502801. E-mail: [email protected] (C.S.). Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was funded by the grant from the National Nature Foundation (81102113 and 81202282), National Natural Science Fund of China key project (81130049), National High Technology Research and the National 12th Five-Year Scientific and Technical Support Program of China (2012BAI02B02), and Research special fund of the Ministry of Health public service sectors funded projects (2015SQ00032).

■

ABBREVIATIONS: UPLC−MS, ultra-high-performance liquid chromatography mass spectrometry; QTOF-MS/MS, quadrupole time-of-flight tandem mass spectrometry; ESI, electrospray ionization; PCA, principal component analysis; PLS-DA, partial least-squaresdiscriminant analysis; OPLS-DA, orthogonal partial leastsquares-discriminant analysis; VIP, variable importance in the projection; AUC, area under the curve; ROC, receiver operating curve

■

■

CONCLUSIONS We characterized for the first time the altered metabolites in nutritional rickets by a distinct urinary metabolic profiling and identified 31 biomarkers related to nutritional rickets. The combination of phosphate and sebacic acid could be used as a noninvasive, accurate diagnostic of infantile nutritional rickets.

REFERENCES

(1) Bereket, A. Nutritional rickets: still a problem for the pediatric population. Pediatr. Health 2010, 4 (1), 75−87. (2) Robinson, P. D.; Högler, W.; Craig, M. E.; Verge, C. F.; Walker, J. L.; Piper, A. C.; Woodhead, H. J.; Cowell, C. T.; Ambler, G. R. The re-

4140



Article

emerging burden of rickets: a decade of experience from Sydney. Arch. Dis. Child. 2006, 91 (7), 564−568. (3) Pai, B.; Shaw, N. Understanding rickets. Paediatr. Child Health 2011, 21 (7), 315−321. (4) Strand, M. A.; Perry, J.; Jin, M.; Tracer, D. P.; Fischer, P. R.; Zhang, P.; Xi, W.; Li, S. Diagnosis of rickets and reassessment of prevalence among rural children in northern China. Pediatr. Int. 2007, 49 (2), 202− 209. (5) Thacher, T. D.; Fischer, P. R.; Pettifor, J. M.; Lawson, J. O.; Isichei, C. O.; Reading, J. C.; Chan, G. M. A comparison of calcium, vitamin D, or both for nutritional rickets in Nigerian children. N. Engl. J. Med. 1999, 341 (8), 563−568. (6) Weisberg, P.; Scanlon, K. S.; Li, R.; Cogswell, M. E. Nutritional rickets among children in the United States: review of cases reported between 1986 and 2003. Am. J. Clin. Nutr. 2004, 80 (6), 1697S−1705S. (7) DeLucia, M. C.; Mitnick, M. E.; Carpenter, T. O. Nutritional rickets with normal circulating 25-hydroxyvitamin D: a call for reexamining the role of dietary calcium intake in North American infants. J. Clin. Endocrinol. Metab. 2003, 88 (8), 3539−3545. (8) Faerk, J.; Peitersen, B.; Petersen, S.; Michaelsen, K. F. Bone mineralisation in premature infants cannot be predicted from serum alkaline phosphatase or serum phosphate. Arch. Dis. Child. Fetal Neonatal Ed. 2002, 87 (2), F133−F136. (9) Taylor, J. A.; Richter, M.; Done, S.; Feldman, K. W., The utility of alkaline phosphatase measurement as a screening test for rickets in breast-fed infants and toddlers: a study from the Puget Sound pediatric research network. Clin. Pediatr. (Philadelphia) 49 (12), 1103−1110. (10) Ge, K.; Chang, S. Dietary intake of micronutrients of Chinese inhabitants. Acta Nutr. Sin. 1999, 21, 322−327. (11) Pettifor, J. M.; Isdale, J.; Sahakian, J.; Hansen, J. Diagnosis of subclinical rickets. Arch. Dis. Child. 1980, 55 (2), 155−157. (12) Thacher, T. D.; Fischer, P. R.; Pettifor, J. M. The usefulness of clinical features to identify active rickets. Ann. Tropical Paediatr.: Int. Child Health 2002, 22 (3), 229−237. (13) Nicholson, J. K.; Lindon, J. C.; Holmes, E. ’Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29, 1181−1189. (14) Cheng, Y.; Xie, G.; Chen, T.; Qiu, Y.; Zou, X.; Zheng, M.; Tan, B.; Feng, B.; Dong, T.; He, P. Distinct urinary metabolic profile of human colorectal cancer. J. Proteome Res. 2011, 11 (2), 1354−1363. (15) Want, E. J.; Wilson, I. D.; Gika, H.; Theodoridis, G.; Plumb, R. S.; Shockcor, J.; Holmes, E.; Nicholson, J. K. Global metabolic profiling procedures for urine using UPLC−MS. Nat. Protoc. 2010, 5 (6), 1005− 1018. (16) Qiu, Y.; Cai, G.; Su, M.; Chen, T.; Zheng, X.; Xu, Y.; Ni, Y.; Zhao, A.; Xu, L. X.; Cai, S. Serum metabolite profiling of human colorectal cancer using GC− TOFMS and UPLC− QTOFMS. J. Proteome Res. 2009, 8 (10), 4844−4850. (17) Heinzmann, S. S.; Brown, I. J.; Chan, Q.; Bictash, M.; Dumas, M.E.; Kochhar, S.; Stamler, J.; Holmes, E.; Elliott, P.; Nicholson, J. K. Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am. J. Clin. Nutr. 2010, 92 (2), 436−443. (18) Wu, R.; Wu, Z.; Wang, X.; Yang, P.; Yu, D.; Zhao, C.; Xu, G.; Kang, L. Metabolomic analysis reveals that carnitines are key regulatory metabolites in phase transition of the locusts. Proc. Natl. Acad. Sci. U. S. A. 2012, 109 (9), 3259−3263. (19) Liu, L.; Wang, M.; Yang, X.; Bin, M.; Na, L.; Niu, Y.; Li, Y.; Sun, C. Fasting Serum Lipid and Dehydroepiandrosterone Sulfate as Important Metabolites for Detecting Isolated Postchallenge Diabetes: Serum Metabolomics via Ultra-High-Performance Liquid Chromatography/ Mass Spectrometry. Clin. Chem. 2013, 59 (9), 1338−1348. (20) Saric, J.; Li, J. V.; Utzinger, J.; Wang, Y.; Keiser, J.; Dirnhofer, S.; Beckonert, O.; Sharabiani, M. T.; Fonville, J. M.; Nicholson, J. K. Systems parasitology: effects of Fasciola hepatica on the neurochemical profile in the rat brain. Mol. Syst. Biol. 2010, 6 (1), 396. (21) Yang, J.; Sun, X.; Feng, Z.; Hao, D.; Wang, M.; Zhao, X.; Sun, C. Metabolomic analysis of the toxic effects of chronic exposure to low-

level dichlorvos on rats using ultra-performance liquid chromatography−mass spectrometry. Toxicol. Lett. 2011, 206 (3), 306−313. (22) Knox, S.; Harris, J.; Calton, L.; Wallace, A. M. A simple automated solid-phase extraction procedure for measurement of 25-hydroxyvitamin D3 and D2 by liquid chromatography-tandem mass spectrometry. Ann. Clin. Biochem. 2009, 46 (3), 226−230. (23) Bijlsma, S.; Bobeldijk, I.; Verheij, E. R.; Ramaker, R.; Kochhar, S.; Macdonald, I. A.; van Ommen, B.; Smilde, A. K. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal. Chem. 2006, 78 (2), 567−574. (24) Xia, J.; Mandal, R.; Sinelnikov, I. V.; Broadhurst, D.; Wishart, D. S. MetaboAnalyst 2.0a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012, 40 (W1), W127−W133. (25) Franceschi, R. T.; Young, J. Regulation of alkaline phosphatase by 1, 25-dihydroxyvitamin D3 and ascorbic acid in bone-derived cells. J. Bone Miner. Res. 1990, 5 (11), 1157−1167. (26) Franceschi, R. T.; Iyer, B. S.; Cui, Y. Effects of ascorbic acid on collagen matrix formation and osteoblast differentiation in murine MC3T3-E1 cells. J. Bone Miner. Res. 1994, 9 (6), 843−854. (27) Yuan, L.-Q.; Xie, H.; Luo, X.-H.; Wu, X.-P.; Zhou, H.-D.; Lu, Y.; Liao, E.-Y. Taurine transporter is expressed in osteoblasts. Amino Acids 2006, 31 (2), 157−163. (28) Kim, S.-J.; Lee, H. W.; Gupta, R. C. Taurine, Bone Growth and Bone Development. Curr. Nutr. Food Sci. 2008, 4 (2), 135−144. (29) Wang, M.; Yang, X.; Wang, F.; Li, R.; Ning, H.; Na, L.; Huang, Y.; Song, Y.; Liu, L.; Pan, H. Calcium-deficiency assessment and biomarker identification by an integrated urinary metabonomics analysis. BMC Med. 2013, 11 (1), 86. (30) Horton, J. D.; Goldstein, J. L.; Brown, M. S. SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver. J. Clin. Invest. 2002, 109 (9), 1125−1132. (31) Andersson, B.; Jones, D. P.; Orrenius, S. Effect of calcium ions on ethanol oxidation and drug glucuronidation in isolated hepatocytes. Biochem. J. 1979, 184 (3), 709. (32) Ö zkan, B. Nutritional rickets. J. Clin. Res. Pediatr. Endocrinol. 2010, 2 (4), 137. (33) Pettifor, J. M. Vitamin D and/or calcium deficiency rickets in infants and children: a global perspective. Indian J. Med. Res. 2008, 127 (3), 245. (34) Underwood, J. L.; DeLuca, H. F. Vitamin D is not directly necessary for bone growth and mineralization. Am. J. Physiol.: Endocrinol. Metab. 1984, 246 (6), E493−E498. (35) Weinstein, R. S.; Underwood, J. L.; Hutson, M. S.; DeLuca, H. F. Bone histomorphometry in vitamin D-deficient rats infused with calcium and phosphorus. Am. J. Physiol.: Endocrinol. Metab. 1984, 246 (6), E499−E505. (36) Hochberg, Z.; Tiosano, D.; Even, L. Calcium therapy for calcitriolresistant rickets. J. Pediatr. 1992, 121 (5), 803−808. (37) Balsan, S.; Garabedian, M.; Larchet, M.; Gorski, A.; Cournot, G.; Tau, C.; Bourdeau, A.; Silve, C.; Ricour, C. Long-term nocturnal calcium infusions can cure rickets and promote normal mineralization in hereditary resistance to 1, 25-dihydroxyvitamin D. J. Clin. Invest. 1986, 77 (5), 1661. (38) Pettifor, J. M.; Ross, P.; Wang, J.; Moodley, G.; Couper-Smith, J. Rickets in children of rural origin in South Africa: is low dietary calcium a factor? J. Pediatr. 1978, 92 (2), 320−324. (39) Pettifor, J. M.; Ross, P.; Moodley, G.; Shuenyane, E. Calcium deficiency in rural black children in South Africa–a comparison between rural and urban communities. Am. J. Clin. Nutr. 1979, 32 (12), 2477− 2483. (40) Graff, M.; Thacher, T. D.; Fischer, P. R.; Stadler, D.; Pam, S. D.; Pettifor, J. M.; Isichei, C. O.; Abrams, S. A. Calcium absorption in Nigerian children with rickets. Am. J. Clin. Nutr. 2004, 80 (5), 1415− 1421. (41) Legius, E.; Proesmans, W.; Eggermont, E.; VandammeLombaerts, R.; Bouillon, R.; Smet, M. Rickets due to dietary calcium deficiency. Eur. J. Pediatr. 1989, 148 (8), 784−785. (42) Balasubramanian, K.; Rajeswari, J.; Govil, Y.; Agarwal, A.; Kumar, A.; Bhatia, V. Varying role of vitamin D deficiency in the etiology of 4141



Article

rickets in young children vs. adolescents in northern India. J. Trop. Pediatr. 2003, 49 (4), 201−206. (43) Clements, M. The problem of rickets in UK Asians. J. Hum. Nutr. Diet. 1989, 2 (2), 105−116. (44) Marie, P. J.; Pettifor, J. M.; Ross, F. P.; Glorieux, F. H. Histological osteomalacia due to dietary calcium deficiency in children. N. Engl. J. Med. 1982, 307 (10), 584−588. (45) Thacher, T. D.; Fischer, P. R.; Isichei, C. O.; Zoakah, A. I.; Pettifor, J. M. Prevention of nutritional rickets in Nigerian children with dietary calcium supplementation. Bone 2012, 50 (5), 1074−1080.

4142


Biomarkers Identified by Urinary Metabonomics for ... - ACS Publications

Recommend Documents