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Lipidomic profiles disturbed by the internet gaming disorder in young Korean males
Journal of Chromatography B 1114–1115 (2019) 119–124
Contents lists available at ScienceDirect
Journal of Chromatography B journal homepage: www.elsevier.com/locate/jchromb
Lipidomic profiles disturbed by the internet gaming disorder in young Korean males
T
Chang-Wan Leea, Deokjong Leec,d, Eun Mi Leea, Soo Jin Parka, Dong Yoon Jia, Do Yup Leea, ⁎ Young-Chul Jungb,c, ,1
⁎⁎,1
,
a
The Department of Bio and Fermentation Convergence Technology, BK21 PLUS program, Kookmin University, Seoul 02707, Republic of Korea The Department of Psychiatry, Yonsei University College of Medicine, Seoul 03722, Republic of Korea c Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea d National Health Insurance service Ilsan Hospital, Goyang, Gyunggi 10444, Republic of Korea b
ARTICLE INFO
ABSTRACT
Keywords: Internet gaming disorder (IGD) Lipidomics Lyso-phosphatidylcholine Liquid chromatography Orbitrap mass-spectrometry (LC-Orbitrap MS)
Internet Gaming Disorder (IGD) is characterized by uncontrollable and persistent playing of internet games despite the occurrence of negative consequences. Although there is a worldwide treatment demand, IGD still doesn't have an explicit biomarker. The primary goal of the study is to characterize lipidomic profiles specific to internet gaming disorder (IGD) based on liquid-chromatography Orbitrap mass-spectrometry (LC Orbitrap MS). Primarily, a total of 19 lipids were significantly dys-regulated in the IGD group compared to healthy controls. The lipidomic feature was mainly characterized by various types of phosphatidylcholines (PCs) and lyso-phosphatidylcholines (LysoPCs). Subsequent multivariate statistical model and linear regression model prioritized two LysoPCs (C16:0 and C18:0) for potential biomarker. Receiver operating characteristic (ROC) analysis demonstrated excellent performance of the combined lipid set for discriminating the IGD group from healthy controls (AUC: 0.981, 95% confidence interval: 0.958–1.000). Additional evaluation with potential confounders and clinical parameters suggested robustness and potential applicability of the outcome as biomarkers which may aid diagnosis.
1. Introduction Internet Gaming Disorder (IGD) is characterized by uncontrollable and persistent playing of internet games despite the occurrence of negative consequences. Individuals with IGD prioritize gaming over other daily activities, resulting in significant impairment in personal, family, social, and educational areas. The prevalence of the disorder varies according to countries: 0.3–1.0% in United States [1], 1.16% in Germany [2], and 5.9% in South Korea [3]. IGD commonly begins during the adolescent period. Adolescence is characterized by different development trajectories of the limbic system and prefrontal cortex regions [4]. The protracted development of the prefrontal cortex compared to the limbic system during the adolescent period results in increased vulnerability to addictive behaviors [5], including drinking, smoking and gaming. On the other hand, pathological gaming habits might interfere with the development process, and perpetuate the protracted development of the prefrontal cortex. Recently, the World
Health Organization has included “Gaming Disorder” in the draft 11th Revision of the International Classification of Diseases (ICD-11), which shall facilitate the development of proper diagnostics and prognostics for IGD based on clinical characteristics and other types of information (e.g. biomarkers). However, few investigation has been conducted on biochemical linkage or molecular indicators for IGD, which may provide more objective and robust evaluation associated with clinical examination. Untargeted molecular profiling, particularly, including transcriptomics, proteomics, and metabolomics has shown its diagnostic and prognostic applicability in different types of diseases [6–8]. Particularly, metabolite, analytical target of metabolomics, is most dynamically changed by systemic perturbation [9–12]. We have recently reported metabolic disturbance in the IGD group, which showed distinctive profiles of primary metabolite from healthy controls and ADHD group, and suggested potential extension to lipid metabolism [13]. Moreover, subsequent examination revealed structural and functional
Correspondence to: Y.-C. Jung, The Department of Psychiatry, Yonsei University College of Medicine, Seoul 03722, Republic of Korea. Corresponding author. E-mail addresses: [email protected] (D.Y. Lee), [email protected] (Y.-C. Jung). 1 These authors contributed equally to this work. ⁎
⁎⁎
https://doi.org/10.1016/j.jchromb.2019.03.027 Received 29 October 2018; Received in revised form 19 March 2019; Accepted 21 March 2019 Available online 23 March 2019 1570-0232/ © 2019 Published by Elsevier B.V.
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changes in young males with IGD through magnetic resonance imaging (MRI) [14–17]. Some of these grey matter alteration correlated with the lifetime usage of internet gaming, which implied that long-term excessive gaming during the childhood and adolescence period may affect the structural and functional brain development. Lipids are crucial controllers of brain function [18], and have been progressively implicated in neuropsychiatric disorders, including attention deficit hyperactivity disorder (ADHD) [19,20], schizophrenia [21,22], bipolar disorder [23], major depressive disorder (MDD) [24,25], and addictive disorder [26]. Accordingly, in our current study, broad range of phospholipids were profiled from blood samples of IGD patients based on high-resolution mass spectrometric analysis (LC-Orbitrap MS). Univariate and multivariate statistical analysis showed the significant level of disturbance in lipid metabolism. Linear regression model and subsequent validation based on ROC curve analysis revealed simple combination of two LysoPCs showed excellent predictability of IGD.
for phase separation in which lower phase was collected and concentrated to complete dryness (SCANVAC, Korea). Dried extract was stored at −80 °C until LC-Orbitrap MS analysis. 2.3.2. Un-targeted lipid profiling using UPLC-Orbitrap MS The dried extract was re-constituted with 50 μL of acetonitrile (70%) for MS analysis. The lipid was chromatographically separated with a 150 mm × 2.1 mm UPLC BEH 1.7-μm C18 column (Waters Corporation, Milford, MA, USA) equipped with 5.0 mm × 2.1 mm UPLC BEH 1.7 μm C18 VanGuard Pre-Column (Waters Corporation, Milford, MA, USA) managed by Ultimate-3000 UPLC system (Thermo Fisher Scientific Inc., Waltham, MA, USA). LC flow rate was set to 0.35 mL/ min. The binary solvent system consisted of buffer A (water with 10 mM ammonium formate and 0.2% formic acid) and buffer B (acetonitrile with 0.2% formic acid). The lipid extract was eluted with gradient as follows: equilibration in 10% buffer B for 1 min, 10–75% buffer B gradient over 7.5 min, 75–95% buffer B gradient over 7.6 min, 95% buffer B for 2.8 min, 95–10% buffer B gradient for 0.1 min and reequilibration in 10% buffer B for 5.5 min. Injection volume was set to 10 μL for both MS1 and MS/MS analysis. MS analysis was performed on negative ionization mode using Q-Exactive Plus Orbitrap (Thermo Fisher Scientific Inc., Waltham, MA, USA). MS/MS analysis was conducted with pooled samples (healthy controls and IGD group, respectively) in data-dependent manner as follows [34]: MS/MS targets were set to precursor ions with top 10 intensities, collision energy: 30 eV, resolving power: 17,500 FWHM, automatic gain control (AGC): 1.0e5. All raw data (.raw file) were converted to Analysis Base File (ABF) format using Reifycs Abf Converter (http://www.reifycs.com/ AbfConverter/index.html). The converted files were imported into the MS-DIAL software for identification process against Lipid Blast (MS1 tolerance: 0.005 Da; MS2, 0.0075 Da) [35,36]. Additional identification was done with LipidSearch software (Thermo Fisher Scientific Inc., Waltham, MA, USA) (MS1 tolerance, 10 ppm; MS2, 20 ppm) [37]. And subsequent manual validation was done for the lipids that were shown higher than 5 and 10 ppm for MS1 and MS2 respectively. Pure spectra from standard compounds (LysoPC 16:0, LysoPC 18:0, and LysoPA 18:1) were obtained and compared against the spectra from samples (S1 Fig). A total of 121 putatively identified lipids were processed using TraceFinder (Thermo Fisher Scientific Inc., Waltham, MA, USA), which allows manual inspection of peak quality and quantification based on ion peak areas at specific mass-to-charge (m/z) and retention time. The ICIS peak detection algorithm in the TraceFinder program was used with highest peak detection strategy. A mass tolerance of 5 ppm for precursor ion and retention time tolerance of 0.5 min were applied. Other peak detection settings included: S/N, 3; baseline window, 40; smoothing, 15; minimum peak width, 3; multiplet resolution, 10, area noise factor, 5; peak noise factor, 10; and area tail extension, 5.
2. Materials and methods 2.1. Subject Eighty-nine subjects participated in the study, including 61 young adults with IGD and 28 healthy controls, all male and aged between 19 and 29 years old. Subjects were recruited through online advertisements, flyers and word of mouth. Subjects who scored above 50 points on the Internet Addiction Test (IAT, Young 1998) [27] and reported gaming as their primary purpose of internet use were included in the IGD group. Subsequently, we conducted a clinician-administered interview to confirm the diagnosis of IGD according to the diagnostic criteria of DSM-5. Healthy controls were subjects who scored below 50 points on the IAT and spent < 2 h per day on gaming. All subjects were screened for other comorbid psychiatric symptoms. Beck Depression Inventory (BDI; Beck, Steer & Brown 1996) [28], Beck Anxiety Inventory (BAI; Beck et al. 1988) [29], Wender Utah Rating Scale (WURS; Ward 1993) [30], Barret Impulsiveness Scale (Patton & Stanford 1995) [31] were used to evaluate depression, anxiety, past childhood symptoms of ADHD and impulsivity. Subjects were psychiatric medication-naïve at the time of evaluation. This study was carried out under the guidelines for the use of human participants established by the Institutional Review Board at Yonsei University. The Institutional Review Board of the Yonsei University approved the study (4–2014-0745). Following a complete description of the scope of the study to all participants, written informed consent was obtained. 2.2. Blood sampling The participants were previously asked to abstain from eating for at least 6 h before the blood sampling. Blood was collected using Vacutainer tubes containing the chelating agent ethylenediaminetetraacetic acid (EDTA) and was centrifuged at 1500g at 4 °C in a refrigerated microfuge for 10 min. Plasma was transferred and aliquoted to a fresh RNase/DNase–free 1.5 microfuge tube. Plasma samples were stored at −80 °C. Once all samples were collected, the tubes were marked with a unique number to blind them.
2.4. Statistical analysis Data was normalized based on EigenMS method [38] implemented in NOREVA (http://idrb.zju.edu.cn/noreva/) [39]. Number of neighbors was set to ten. Maximum number of missing data allowed in any row and column was 50% and 80%, respectively. P-value was computed by Student's t-test (Excel, Microsoft Office 2016), and false discovery rates (FDR) were calculated based on Welch approximation implemented in Multi Experimental Viewer (MeV, TIGR) [40]. Boxwhisker plot was generated using Prism 7.00 (Graphpad Software, Inc., La Jolla, CA, USA). Multivariate statistical analysis was conducted by SIMCA 14 (Umetrics AB, Umea, Sweden), which included principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and variable importance into projection (VIP) analysis. Analysis of variance (ANOVA), analysis of covariance (ANCOVA), Mann–Whitney U Test and linear regression analysis with forward selection were performed using IMB SPSS Statistics for Windows, version 23.0 (IBM Corp., Armonk, N.Y., USA). Receiver operating
2.3. Sample preparation 2.3.1. Lipid extraction from blood plasma LysoPCs (C16:0 and C18:0) were purchased from Avanti Polar Lipids, Inc. (Alabaster, AL, USA). Lyso-phosphatidic acid (LysoPA 18:1) was purchased from Sigma-Aldrich Chemie GmbH (Munich, Germany). Extraction was conducted based on the Folch method with minor modification [32,33]. Plasma sample (50 μL) was mixed with 225 μL of MeOH and vortexed for 10 s. Chloroform (450 μL) was added to the mixture and incubated for 1 h. H2O (187.5 μL) was added to the mixture 120
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healthy controls (Table 1).
Table 1 Demographic and clinical characteristics of subjects.
Age (years) Internet addiction severitya Childhood ADHD symptomsb Depressionc Anxietyd Alcohol Usee Impulsivenessf a b c d e f
Healthy controls (n = 28)
IGD group (n = 61)
P-value
23.9 ± 2.5 34.9 ± 12.3 22.0 ± 15.7 9.0 ± 5.7 7.0 ± 6.1 11.9 ± 7.2 43.8 ± 13.0
23.9 ± 2.1 64.2 ± 10.2 40.6 ± 21.8 14.5 ± 7.7 13.2 ± 9.4 11.4 ± 8.6 49.8 ± 13.5
0.935 < 0.001 0.001 < 0.001 0.002 0.791 0.050
3.2. Disturbance in lipid profile of blood plasma by IGD Un-targeted lipid profiling was performed on a total of 89 plasma samples using UHPLC-Orbitrap MS analysis. A total of 121 lipids were annotated and further applied for quantitative evaluation (see the material and method for details). The major lipid classes were fatty acid ester of hydroxyl fatty acids (FAHFAs), lyso-phosphatidic acids (LysoPAs), lyso-phophatidylcholines (LysoPCs), lyso-phosphatidylethanolamines (LysoPEs), and phosphatidylcholines (PCs). The raw and normalized data are available at the website (https://lms2.kookmin.ac. kr:446/index.php?hCode=PAPER_LIST&publication_name=inter_ paper). Nineteen lipids were differentially regulated between healthy controls and IGD group (Student t-test, p < .05). Among them were 47% of lyso-forms of phospholipids that were significantly different (Table 2). Fifteen lipids were present at significantly higher levels in the IGD group in which LysoPCs (C16:0 and C18:0) and LysoPE (22:6) showed the highest levels of increased contents. On contrary, PC (33:1), PSs (C41:6 and C46:8), and LysoPC (22:1) were the lipids with the significant down-regulation. LysoPCs, the products of phospholipase A2 (PLA2) catalyzed-reaction from PCs, have previously been implicated in numerous processes, including cellular growth [41]. The lipid species have been reported in the association with inflammatory processes in neurodegenerative disease such as Alzheimer's disease (AD) [42] and Parkinson's disease (PD) [43]. Particularly, the significant elevation in LysoPCs was reported in the psychiatric disease, major depressive disorder (MDD), which may cause hyperactivity of pro-inflammatory cytokine interleukin (IL)-6 [44]. Moreover, our result showed the moderate levels of increases in LysoPAs (C18:1 and C20:4) (S3 Fig). LysoPA, produced from LysoPC by lysophospholipase D, has been suggested for its putative role in astrocytes associated with the permeability of the blood–brain barrier (BBB) under physiological or pathological conditions [45]. Likewise, branched fatty acid esters of hydroxy fatty acids (FAHFAs) was found the elevated level in the IGD group. The overproduction of the lipid may play a pivotal role like arachidonic acid that is metabolized to both pro-inflammatory and anti-inflammatory eicosanoids [46].
Young's internet addiction test (YIAT) (≥50). Wender-Utah rating scale. Beck Depression Inventory. Beck Anxiety inventory. Alcohol Use Disorder Identification Test. Barret Impulsiveness scale.
characteristic (ROC) analysis was done by MedCalc for Windows, version 12.7.0.0 (MedCalc Software, Ostend, Belgium). 2.5. Quality control for MS analysis All analytical procedures were conducted in randomized order, which included extraction, reconstitution, and MS analysis to minimize potential systematic error. In addition, analytical stability of mass spectrometry was evaluated based on un-supervised multivariate statistics. Score scatter plot showed no significant signal drift with T1 (upper panel) and T2 (lower panel) using PCA (S2 Fig). 3. Results and discussion 3.1. Clinical characteristics of subjects Participants in this study were all males and their demographic and clinical characteristics are indicated in Table 1. There was no significant difference between IGD subjects and healthy control in age and education. IGD subjects scored significantly higher on tests of childhood ADHD symptoms, depression, anxiety and impulsiveness compared to
Table 2 The list of metabolites that are present at significantly different levels in the IGD group. Statistical evaluation is performed based on p-value (Student's t-test) and false discovery rate (FDR) against healthy controls. Healthy controls vs. IGD group Lipids
RT (min)
Observed m/z
Theoretical m/z
Error (ppm)
p-value
Change
FDR
t-stat
FAHFA 27:4 LysoPC 16:0 LysoPC 18:0 LysoPC 18:2 LysoPC 22:1 LysoPE 18:3 LysoPE 20:5 LysoPE 22:6 LysoPS 19:1 LysoPS 19:2 PC 32:2 PC 33:1 PC 34:1 PC 37:6 PC 42:10 PS 41:6 PS 46:8 SM d33:1 Cer d24:2
8.93 9.50 10.25 9.13 11.19 8.56 8.39 9.10 9.93 9.27 10.75 11.49 10.73 10.31 11.33 11.88 11.98 9.91 11.49
431.3162 540.3311 568.3631 564.3304 622.4093 474.2625 498.2634 524.2783 536.2993 534.2834 774.5282 790.5621 804.5763 836.5459 898.5574 848.5438 914.5914 733.5493 394.3318
431.3167 540.3301 568.3614 564.3301 622.4084 474.2621 498.2621 524.2777 536.2994 534.2837 774.5285 790.5598 804.5755 836.5442 898.5598 848.5447 914.5917 733.5501 394.3327
1.16 1.85 2.99 0.53 1.45 0.84 2.61 1.14 0.19 0.56 0.39 2.91 0.99 2.03 2.67 1.06 0.33 1.09 2.28
0.005 < 0.001 < 0.001 0.032 0.010 < 0.001 0.004 < 0.001 0.017 0.030 0.003 0.045 < 0.001 0.011 0.047 0.021 0.032 0.010 0.011
Increase ↑ Increase ↑ Increase ↑ Increase ↑ Decrease ↓ Increase ↑ Increase ↑ Increase ↑ Increase ↑ Increase ↑ Increase ↑ Decrease ↓ Increase ↑ Increase ↑ Increase ↑ Decrease ↓ Decrease ↓ Increase ↑ Increase ↑
0.082 0.001 < 0.001 0.228 0.108 0.006 0.067 < 0.001 0.154 0.228 0.067 0.293 0.001 0.108 0.300 0.187 0.233 0.109 0.108
2.851 4.612 8.938 2.180 −2.623 3.802 2.967 7.107 2.443 2.210 3.016 −2.032 4.505 2.609 2.016 −2.345 −2.185 2.640 2.614
Cer, ceramide; FAHFA, fatty acid esters of hydroxyl fatty acid; LysoPC, lyso-phosphatidylcholine; LysoPE, lyso-phosphatidylethanolamine; LysoPS, lyso-phosphatidylserine; PC, phophatidylcholine; PS, phosphatidylserine; SM, sphingomyelin. 121
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Fig. 1. The differential lipidomic profiles of the IGD group. (A) The score plot based on multivariate statistical model by OPLS-DA. (B) Box-Whisker plots of the lipids that show VIP scores > 1.5. Box indicates mean value and standard deviation, and bar presents minimum and maximum values.
3.3. Lipid biomarker exploration and validation for IGD
including major depressive disorder [50], psychotic disorder [51], and schizophrenia [52]. The combined set with the two LysoPCs demonstrated improved linear linkage to IGD score (R = 0.677, p = 3.02 × 10–13). The performance of the plasma lipids as medical diagnostic factors was evaluated using ROC curve analysis [53,54]. The individual lipid showed excellent discrimination power against IGD score. The AUCs of LysoPC (16:0) and LysoPC (18:0) were 0.859 (95% confidence interval: 0.771–0.948) and 0.943 (95% confidence interval: 0.873–1.000), respectively (Fig. 3). As expected, the combined panel of the predictive biomarkers showed the improvement in the performance (AUC: 0.981, 95% confidence interval: 0.958–1.000).
We examined if the integrative lipidomic profiles can be discriminated between healthy controls and the IGD group. Subsequently, OPLS-DA was applied to explore potential biomarker. The multivariate statistical analysis effectively manages significant amounts of collinearity [47], hence it can delineate major contributors from high dimension of data matrix [48]. The OPLS-DA model with eight-fold cross validation revealed the high levels of an explained variance (R2Y) of 0.994 and predictability (Q2) of 0.842 (Fig. 1A). Based on the model, 7 lipids were prioritized by VIP analysis (VIP score > 1.5). The list included FAHFA, LysoPCs, LysoPEs, and PC, which were all found significantly higher abundances in IGD group (Fig. 1B). In addition, linear regression analysis with forward selection was performed to characterize the linear relation between IGD score and lipids, which in turn allows more straightforward mathematical model [49]. The model suggested the highest linearity of LysoPC (16:0) and LysoPC (18:0) with IGD score. The lipids were found the most significant different between healthy controls and IGD group (Fig. 2), and they also showed the high VIP score in OPLS-DA model. Of particular interest, the significant alteration in the biomarkers, LysoPC (16:0) and LysoPC (18:0) were coordinately linked to our previous findings, in which the corresponding free fatty acids (palmitic acid and stearic acid) were shown decreased contents [13]. The abnormal contents in two molecular species implied potential disturbance in LysoPCs metabolism in the IGD group, which has been associated with psychiatric disorders
3.4. Evaluation of confounding effects associated with IGD levels Potential confounders were first examined based on Mann-Whitney U test [55,56], which included age, ADHD score, depression, anxiety, alcohol use, and impulsiveness. Among them were ADHD score, depression, anxiety, and impulsiveness that showed the significant differences between healthy controls and the IGD group (p < .05, S1 Table). The clinical characteristics, particularly, anxiety, depression, and ADHD have been deliberated for the association with IGD. For instance, Lee et al. and Ho et al. demonstrated that comorbidity of depression, anxiety, attention deficit hyperactivity disorder (ADHD) affect the various stages of IGD [57,58]. A recent meta-analysis reported that the prevalence of ADHD among internet addiction patients were 21.7% [58]. 122
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Fig. 2. Scatter plots and Pearson's correlation coefficients between IGD score and (A) LysoPC 16:0, (B) LysoPC 18:0, and (C) linear regression model of LysoPC 16:0 and LysoPC 18:0.
coefficient was altered when depression was included in the model (S3 Table). Our previous report showed that depression was a unique component for linear regression model in IGD associated with blood primary metabolites [13], suggesting that depression may be an interactive factor rather than a confounder. Subsequently, we tested covariance between the lipids and the clinical parameter using MANCOVA in which two lipids were set to dependent variables, and depression was set to covariates. The result showed that significant level of covariance relation was only observed between the lipid biomarkers and depression (p = .047). The result implied strong comorbidity between IGD and depression relative to other clinical characteristics under debate. However, the linear regression models including depression did not show the statistical significance based on AUC values by ROC analysis (p = .693) (S3 Table). 4. Conclusion To the best of our knowledge, the current study is the first report on lipidomic profiles of IGD. Although the sample size of our study is small and confirmative conclusion requires further expansion of observation and additional examination under controlled circumstances, our current findings imply the cumulative long-term effect of pathologic gaming on the brain development in young adults. The current exploratory investigation on the lipid biomarker cluster proves the clinical applicability, which may improve the pathological definition for IGD and increase the accuracy of the diagnosis. In addition, the identified metabolic features, the relatedness with clinical parameters, and the putative biochemical linkage will hopefully aid future pathological studies in IGD.
Fig. 3. Validation of lipidomic markers based on Receiver Operating Characteristic (ROC) analysis. ROC curves for LysoPC 16:0 (blue), LysoPC 18:0 (green), linear regression model in combination with LysoPC 16:0 and LysoPC 18:0 (red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Competing interests The authors have declared that no competing interests exist. Acknowledgments
After adjustment of the four confounders, 17 out of 19 lipids were still present at significantly different levels in the IGD group compared to healthy controls. The list included two lipid biomarkers (LysoPC 16:0 and LysoPC 18:0) (p < .05, S2 Table). Likewise, the confounding effect on linear relation was evaluated, which may affect the linear regression model for the biomarker panel. The statistical significance was identified in ADHD score and depression using ANCOVA (p < .05, S1 Table). The significant covariance was identified in ADHD score and depression against IGD score. Subsequently, we examined whether regression of coefficient (variables vs IGD score) is significantly altered by including ADHD score and depression to the regression model. The result showed that < 10% of regression of coefficient was changed with ADHD score, which indicated that no significant effect of ADHD score on the linear regression model (S3 Table). On contrary, > 15% of regression of
This study was supported by a grant of the Korean Mental Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HM15C2578) to Y.C.J., and by the Brain Research Program and the Bio & Medical Technology Development Program through the National Research Foundation funded by the Ministry of Science and ICT (NRF-2016R1C1B2007982 and NRF-2016M3A9B6902062) to D.Y.L. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jchromb.2019.03.027. 123
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Journal of Chromatography B journal homepage: www.elsevier.com/locate/jchromb
Lipidomic profiles disturbed by the internet gaming disorder in young Korean males
T
Chang-Wan Leea, Deokjong Leec,d, Eun Mi Leea, Soo Jin Parka, Dong Yoon Jia, Do Yup Leea, ⁎ Young-Chul Jungb,c, ,1
⁎⁎,1
,
a
The Department of Bio and Fermentation Convergence Technology, BK21 PLUS program, Kookmin University, Seoul 02707, Republic of Korea The Department of Psychiatry, Yonsei University College of Medicine, Seoul 03722, Republic of Korea c Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea d National Health Insurance service Ilsan Hospital, Goyang, Gyunggi 10444, Republic of Korea b
ARTICLE INFO
ABSTRACT
Keywords: Internet gaming disorder (IGD) Lipidomics Lyso-phosphatidylcholine Liquid chromatography Orbitrap mass-spectrometry (LC-Orbitrap MS)
Internet Gaming Disorder (IGD) is characterized by uncontrollable and persistent playing of internet games despite the occurrence of negative consequences. Although there is a worldwide treatment demand, IGD still doesn't have an explicit biomarker. The primary goal of the study is to characterize lipidomic profiles specific to internet gaming disorder (IGD) based on liquid-chromatography Orbitrap mass-spectrometry (LC Orbitrap MS). Primarily, a total of 19 lipids were significantly dys-regulated in the IGD group compared to healthy controls. The lipidomic feature was mainly characterized by various types of phosphatidylcholines (PCs) and lyso-phosphatidylcholines (LysoPCs). Subsequent multivariate statistical model and linear regression model prioritized two LysoPCs (C16:0 and C18:0) for potential biomarker. Receiver operating characteristic (ROC) analysis demonstrated excellent performance of the combined lipid set for discriminating the IGD group from healthy controls (AUC: 0.981, 95% confidence interval: 0.958–1.000). Additional evaluation with potential confounders and clinical parameters suggested robustness and potential applicability of the outcome as biomarkers which may aid diagnosis.
1. Introduction Internet Gaming Disorder (IGD) is characterized by uncontrollable and persistent playing of internet games despite the occurrence of negative consequences. Individuals with IGD prioritize gaming over other daily activities, resulting in significant impairment in personal, family, social, and educational areas. The prevalence of the disorder varies according to countries: 0.3–1.0% in United States [1], 1.16% in Germany [2], and 5.9% in South Korea [3]. IGD commonly begins during the adolescent period. Adolescence is characterized by different development trajectories of the limbic system and prefrontal cortex regions [4]. The protracted development of the prefrontal cortex compared to the limbic system during the adolescent period results in increased vulnerability to addictive behaviors [5], including drinking, smoking and gaming. On the other hand, pathological gaming habits might interfere with the development process, and perpetuate the protracted development of the prefrontal cortex. Recently, the World
Health Organization has included “Gaming Disorder” in the draft 11th Revision of the International Classification of Diseases (ICD-11), which shall facilitate the development of proper diagnostics and prognostics for IGD based on clinical characteristics and other types of information (e.g. biomarkers). However, few investigation has been conducted on biochemical linkage or molecular indicators for IGD, which may provide more objective and robust evaluation associated with clinical examination. Untargeted molecular profiling, particularly, including transcriptomics, proteomics, and metabolomics has shown its diagnostic and prognostic applicability in different types of diseases [6–8]. Particularly, metabolite, analytical target of metabolomics, is most dynamically changed by systemic perturbation [9–12]. We have recently reported metabolic disturbance in the IGD group, which showed distinctive profiles of primary metabolite from healthy controls and ADHD group, and suggested potential extension to lipid metabolism [13]. Moreover, subsequent examination revealed structural and functional
Correspondence to: Y.-C. Jung, The Department of Psychiatry, Yonsei University College of Medicine, Seoul 03722, Republic of Korea. Corresponding author. E-mail addresses: [email protected] (D.Y. Lee), [email protected] (Y.-C. Jung). 1 These authors contributed equally to this work. ⁎
⁎⁎
https://doi.org/10.1016/j.jchromb.2019.03.027 Received 29 October 2018; Received in revised form 19 March 2019; Accepted 21 March 2019 Available online 23 March 2019 1570-0232/ © 2019 Published by Elsevier B.V.
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changes in young males with IGD through magnetic resonance imaging (MRI) [14–17]. Some of these grey matter alteration correlated with the lifetime usage of internet gaming, which implied that long-term excessive gaming during the childhood and adolescence period may affect the structural and functional brain development. Lipids are crucial controllers of brain function [18], and have been progressively implicated in neuropsychiatric disorders, including attention deficit hyperactivity disorder (ADHD) [19,20], schizophrenia [21,22], bipolar disorder [23], major depressive disorder (MDD) [24,25], and addictive disorder [26]. Accordingly, in our current study, broad range of phospholipids were profiled from blood samples of IGD patients based on high-resolution mass spectrometric analysis (LC-Orbitrap MS). Univariate and multivariate statistical analysis showed the significant level of disturbance in lipid metabolism. Linear regression model and subsequent validation based on ROC curve analysis revealed simple combination of two LysoPCs showed excellent predictability of IGD.
for phase separation in which lower phase was collected and concentrated to complete dryness (SCANVAC, Korea). Dried extract was stored at −80 °C until LC-Orbitrap MS analysis. 2.3.2. Un-targeted lipid profiling using UPLC-Orbitrap MS The dried extract was re-constituted with 50 μL of acetonitrile (70%) for MS analysis. The lipid was chromatographically separated with a 150 mm × 2.1 mm UPLC BEH 1.7-μm C18 column (Waters Corporation, Milford, MA, USA) equipped with 5.0 mm × 2.1 mm UPLC BEH 1.7 μm C18 VanGuard Pre-Column (Waters Corporation, Milford, MA, USA) managed by Ultimate-3000 UPLC system (Thermo Fisher Scientific Inc., Waltham, MA, USA). LC flow rate was set to 0.35 mL/ min. The binary solvent system consisted of buffer A (water with 10 mM ammonium formate and 0.2% formic acid) and buffer B (acetonitrile with 0.2% formic acid). The lipid extract was eluted with gradient as follows: equilibration in 10% buffer B for 1 min, 10–75% buffer B gradient over 7.5 min, 75–95% buffer B gradient over 7.6 min, 95% buffer B for 2.8 min, 95–10% buffer B gradient for 0.1 min and reequilibration in 10% buffer B for 5.5 min. Injection volume was set to 10 μL for both MS1 and MS/MS analysis. MS analysis was performed on negative ionization mode using Q-Exactive Plus Orbitrap (Thermo Fisher Scientific Inc., Waltham, MA, USA). MS/MS analysis was conducted with pooled samples (healthy controls and IGD group, respectively) in data-dependent manner as follows [34]: MS/MS targets were set to precursor ions with top 10 intensities, collision energy: 30 eV, resolving power: 17,500 FWHM, automatic gain control (AGC): 1.0e5. All raw data (.raw file) were converted to Analysis Base File (ABF) format using Reifycs Abf Converter (http://www.reifycs.com/ AbfConverter/index.html). The converted files were imported into the MS-DIAL software for identification process against Lipid Blast (MS1 tolerance: 0.005 Da; MS2, 0.0075 Da) [35,36]. Additional identification was done with LipidSearch software (Thermo Fisher Scientific Inc., Waltham, MA, USA) (MS1 tolerance, 10 ppm; MS2, 20 ppm) [37]. And subsequent manual validation was done for the lipids that were shown higher than 5 and 10 ppm for MS1 and MS2 respectively. Pure spectra from standard compounds (LysoPC 16:0, LysoPC 18:0, and LysoPA 18:1) were obtained and compared against the spectra from samples (S1 Fig). A total of 121 putatively identified lipids were processed using TraceFinder (Thermo Fisher Scientific Inc., Waltham, MA, USA), which allows manual inspection of peak quality and quantification based on ion peak areas at specific mass-to-charge (m/z) and retention time. The ICIS peak detection algorithm in the TraceFinder program was used with highest peak detection strategy. A mass tolerance of 5 ppm for precursor ion and retention time tolerance of 0.5 min were applied. Other peak detection settings included: S/N, 3; baseline window, 40; smoothing, 15; minimum peak width, 3; multiplet resolution, 10, area noise factor, 5; peak noise factor, 10; and area tail extension, 5.
2. Materials and methods 2.1. Subject Eighty-nine subjects participated in the study, including 61 young adults with IGD and 28 healthy controls, all male and aged between 19 and 29 years old. Subjects were recruited through online advertisements, flyers and word of mouth. Subjects who scored above 50 points on the Internet Addiction Test (IAT, Young 1998) [27] and reported gaming as their primary purpose of internet use were included in the IGD group. Subsequently, we conducted a clinician-administered interview to confirm the diagnosis of IGD according to the diagnostic criteria of DSM-5. Healthy controls were subjects who scored below 50 points on the IAT and spent < 2 h per day on gaming. All subjects were screened for other comorbid psychiatric symptoms. Beck Depression Inventory (BDI; Beck, Steer & Brown 1996) [28], Beck Anxiety Inventory (BAI; Beck et al. 1988) [29], Wender Utah Rating Scale (WURS; Ward 1993) [30], Barret Impulsiveness Scale (Patton & Stanford 1995) [31] were used to evaluate depression, anxiety, past childhood symptoms of ADHD and impulsivity. Subjects were psychiatric medication-naïve at the time of evaluation. This study was carried out under the guidelines for the use of human participants established by the Institutional Review Board at Yonsei University. The Institutional Review Board of the Yonsei University approved the study (4–2014-0745). Following a complete description of the scope of the study to all participants, written informed consent was obtained. 2.2. Blood sampling The participants were previously asked to abstain from eating for at least 6 h before the blood sampling. Blood was collected using Vacutainer tubes containing the chelating agent ethylenediaminetetraacetic acid (EDTA) and was centrifuged at 1500g at 4 °C in a refrigerated microfuge for 10 min. Plasma was transferred and aliquoted to a fresh RNase/DNase–free 1.5 microfuge tube. Plasma samples were stored at −80 °C. Once all samples were collected, the tubes were marked with a unique number to blind them.
2.4. Statistical analysis Data was normalized based on EigenMS method [38] implemented in NOREVA (http://idrb.zju.edu.cn/noreva/) [39]. Number of neighbors was set to ten. Maximum number of missing data allowed in any row and column was 50% and 80%, respectively. P-value was computed by Student's t-test (Excel, Microsoft Office 2016), and false discovery rates (FDR) were calculated based on Welch approximation implemented in Multi Experimental Viewer (MeV, TIGR) [40]. Boxwhisker plot was generated using Prism 7.00 (Graphpad Software, Inc., La Jolla, CA, USA). Multivariate statistical analysis was conducted by SIMCA 14 (Umetrics AB, Umea, Sweden), which included principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and variable importance into projection (VIP) analysis. Analysis of variance (ANOVA), analysis of covariance (ANCOVA), Mann–Whitney U Test and linear regression analysis with forward selection were performed using IMB SPSS Statistics for Windows, version 23.0 (IBM Corp., Armonk, N.Y., USA). Receiver operating
2.3. Sample preparation 2.3.1. Lipid extraction from blood plasma LysoPCs (C16:0 and C18:0) were purchased from Avanti Polar Lipids, Inc. (Alabaster, AL, USA). Lyso-phosphatidic acid (LysoPA 18:1) was purchased from Sigma-Aldrich Chemie GmbH (Munich, Germany). Extraction was conducted based on the Folch method with minor modification [32,33]. Plasma sample (50 μL) was mixed with 225 μL of MeOH and vortexed for 10 s. Chloroform (450 μL) was added to the mixture and incubated for 1 h. H2O (187.5 μL) was added to the mixture 120
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healthy controls (Table 1).
Table 1 Demographic and clinical characteristics of subjects.
Age (years) Internet addiction severitya Childhood ADHD symptomsb Depressionc Anxietyd Alcohol Usee Impulsivenessf a b c d e f
Healthy controls (n = 28)
IGD group (n = 61)
P-value
23.9 ± 2.5 34.9 ± 12.3 22.0 ± 15.7 9.0 ± 5.7 7.0 ± 6.1 11.9 ± 7.2 43.8 ± 13.0
23.9 ± 2.1 64.2 ± 10.2 40.6 ± 21.8 14.5 ± 7.7 13.2 ± 9.4 11.4 ± 8.6 49.8 ± 13.5
0.935 < 0.001 0.001 < 0.001 0.002 0.791 0.050
3.2. Disturbance in lipid profile of blood plasma by IGD Un-targeted lipid profiling was performed on a total of 89 plasma samples using UHPLC-Orbitrap MS analysis. A total of 121 lipids were annotated and further applied for quantitative evaluation (see the material and method for details). The major lipid classes were fatty acid ester of hydroxyl fatty acids (FAHFAs), lyso-phosphatidic acids (LysoPAs), lyso-phophatidylcholines (LysoPCs), lyso-phosphatidylethanolamines (LysoPEs), and phosphatidylcholines (PCs). The raw and normalized data are available at the website (https://lms2.kookmin.ac. kr:446/index.php?hCode=PAPER_LIST&publication_name=inter_ paper). Nineteen lipids were differentially regulated between healthy controls and IGD group (Student t-test, p < .05). Among them were 47% of lyso-forms of phospholipids that were significantly different (Table 2). Fifteen lipids were present at significantly higher levels in the IGD group in which LysoPCs (C16:0 and C18:0) and LysoPE (22:6) showed the highest levels of increased contents. On contrary, PC (33:1), PSs (C41:6 and C46:8), and LysoPC (22:1) were the lipids with the significant down-regulation. LysoPCs, the products of phospholipase A2 (PLA2) catalyzed-reaction from PCs, have previously been implicated in numerous processes, including cellular growth [41]. The lipid species have been reported in the association with inflammatory processes in neurodegenerative disease such as Alzheimer's disease (AD) [42] and Parkinson's disease (PD) [43]. Particularly, the significant elevation in LysoPCs was reported in the psychiatric disease, major depressive disorder (MDD), which may cause hyperactivity of pro-inflammatory cytokine interleukin (IL)-6 [44]. Moreover, our result showed the moderate levels of increases in LysoPAs (C18:1 and C20:4) (S3 Fig). LysoPA, produced from LysoPC by lysophospholipase D, has been suggested for its putative role in astrocytes associated with the permeability of the blood–brain barrier (BBB) under physiological or pathological conditions [45]. Likewise, branched fatty acid esters of hydroxy fatty acids (FAHFAs) was found the elevated level in the IGD group. The overproduction of the lipid may play a pivotal role like arachidonic acid that is metabolized to both pro-inflammatory and anti-inflammatory eicosanoids [46].
Young's internet addiction test (YIAT) (≥50). Wender-Utah rating scale. Beck Depression Inventory. Beck Anxiety inventory. Alcohol Use Disorder Identification Test. Barret Impulsiveness scale.
characteristic (ROC) analysis was done by MedCalc for Windows, version 12.7.0.0 (MedCalc Software, Ostend, Belgium). 2.5. Quality control for MS analysis All analytical procedures were conducted in randomized order, which included extraction, reconstitution, and MS analysis to minimize potential systematic error. In addition, analytical stability of mass spectrometry was evaluated based on un-supervised multivariate statistics. Score scatter plot showed no significant signal drift with T1 (upper panel) and T2 (lower panel) using PCA (S2 Fig). 3. Results and discussion 3.1. Clinical characteristics of subjects Participants in this study were all males and their demographic and clinical characteristics are indicated in Table 1. There was no significant difference between IGD subjects and healthy control in age and education. IGD subjects scored significantly higher on tests of childhood ADHD symptoms, depression, anxiety and impulsiveness compared to
Table 2 The list of metabolites that are present at significantly different levels in the IGD group. Statistical evaluation is performed based on p-value (Student's t-test) and false discovery rate (FDR) against healthy controls. Healthy controls vs. IGD group Lipids
RT (min)
Observed m/z
Theoretical m/z
Error (ppm)
p-value
Change
FDR
t-stat
FAHFA 27:4 LysoPC 16:0 LysoPC 18:0 LysoPC 18:2 LysoPC 22:1 LysoPE 18:3 LysoPE 20:5 LysoPE 22:6 LysoPS 19:1 LysoPS 19:2 PC 32:2 PC 33:1 PC 34:1 PC 37:6 PC 42:10 PS 41:6 PS 46:8 SM d33:1 Cer d24:2
8.93 9.50 10.25 9.13 11.19 8.56 8.39 9.10 9.93 9.27 10.75 11.49 10.73 10.31 11.33 11.88 11.98 9.91 11.49
431.3162 540.3311 568.3631 564.3304 622.4093 474.2625 498.2634 524.2783 536.2993 534.2834 774.5282 790.5621 804.5763 836.5459 898.5574 848.5438 914.5914 733.5493 394.3318
431.3167 540.3301 568.3614 564.3301 622.4084 474.2621 498.2621 524.2777 536.2994 534.2837 774.5285 790.5598 804.5755 836.5442 898.5598 848.5447 914.5917 733.5501 394.3327
1.16 1.85 2.99 0.53 1.45 0.84 2.61 1.14 0.19 0.56 0.39 2.91 0.99 2.03 2.67 1.06 0.33 1.09 2.28
0.005 < 0.001 < 0.001 0.032 0.010 < 0.001 0.004 < 0.001 0.017 0.030 0.003 0.045 < 0.001 0.011 0.047 0.021 0.032 0.010 0.011
Increase ↑ Increase ↑ Increase ↑ Increase ↑ Decrease ↓ Increase ↑ Increase ↑ Increase ↑ Increase ↑ Increase ↑ Increase ↑ Decrease ↓ Increase ↑ Increase ↑ Increase ↑ Decrease ↓ Decrease ↓ Increase ↑ Increase ↑
0.082 0.001 < 0.001 0.228 0.108 0.006 0.067 < 0.001 0.154 0.228 0.067 0.293 0.001 0.108 0.300 0.187 0.233 0.109 0.108
2.851 4.612 8.938 2.180 −2.623 3.802 2.967 7.107 2.443 2.210 3.016 −2.032 4.505 2.609 2.016 −2.345 −2.185 2.640 2.614
Cer, ceramide; FAHFA, fatty acid esters of hydroxyl fatty acid; LysoPC, lyso-phosphatidylcholine; LysoPE, lyso-phosphatidylethanolamine; LysoPS, lyso-phosphatidylserine; PC, phophatidylcholine; PS, phosphatidylserine; SM, sphingomyelin. 121
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Fig. 1. The differential lipidomic profiles of the IGD group. (A) The score plot based on multivariate statistical model by OPLS-DA. (B) Box-Whisker plots of the lipids that show VIP scores > 1.5. Box indicates mean value and standard deviation, and bar presents minimum and maximum values.
3.3. Lipid biomarker exploration and validation for IGD
including major depressive disorder [50], psychotic disorder [51], and schizophrenia [52]. The combined set with the two LysoPCs demonstrated improved linear linkage to IGD score (R = 0.677, p = 3.02 × 10–13). The performance of the plasma lipids as medical diagnostic factors was evaluated using ROC curve analysis [53,54]. The individual lipid showed excellent discrimination power against IGD score. The AUCs of LysoPC (16:0) and LysoPC (18:0) were 0.859 (95% confidence interval: 0.771–0.948) and 0.943 (95% confidence interval: 0.873–1.000), respectively (Fig. 3). As expected, the combined panel of the predictive biomarkers showed the improvement in the performance (AUC: 0.981, 95% confidence interval: 0.958–1.000).
We examined if the integrative lipidomic profiles can be discriminated between healthy controls and the IGD group. Subsequently, OPLS-DA was applied to explore potential biomarker. The multivariate statistical analysis effectively manages significant amounts of collinearity [47], hence it can delineate major contributors from high dimension of data matrix [48]. The OPLS-DA model with eight-fold cross validation revealed the high levels of an explained variance (R2Y) of 0.994 and predictability (Q2) of 0.842 (Fig. 1A). Based on the model, 7 lipids were prioritized by VIP analysis (VIP score > 1.5). The list included FAHFA, LysoPCs, LysoPEs, and PC, which were all found significantly higher abundances in IGD group (Fig. 1B). In addition, linear regression analysis with forward selection was performed to characterize the linear relation between IGD score and lipids, which in turn allows more straightforward mathematical model [49]. The model suggested the highest linearity of LysoPC (16:0) and LysoPC (18:0) with IGD score. The lipids were found the most significant different between healthy controls and IGD group (Fig. 2), and they also showed the high VIP score in OPLS-DA model. Of particular interest, the significant alteration in the biomarkers, LysoPC (16:0) and LysoPC (18:0) were coordinately linked to our previous findings, in which the corresponding free fatty acids (palmitic acid and stearic acid) were shown decreased contents [13]. The abnormal contents in two molecular species implied potential disturbance in LysoPCs metabolism in the IGD group, which has been associated with psychiatric disorders
3.4. Evaluation of confounding effects associated with IGD levels Potential confounders were first examined based on Mann-Whitney U test [55,56], which included age, ADHD score, depression, anxiety, alcohol use, and impulsiveness. Among them were ADHD score, depression, anxiety, and impulsiveness that showed the significant differences between healthy controls and the IGD group (p < .05, S1 Table). The clinical characteristics, particularly, anxiety, depression, and ADHD have been deliberated for the association with IGD. For instance, Lee et al. and Ho et al. demonstrated that comorbidity of depression, anxiety, attention deficit hyperactivity disorder (ADHD) affect the various stages of IGD [57,58]. A recent meta-analysis reported that the prevalence of ADHD among internet addiction patients were 21.7% [58]. 122
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Fig. 2. Scatter plots and Pearson's correlation coefficients between IGD score and (A) LysoPC 16:0, (B) LysoPC 18:0, and (C) linear regression model of LysoPC 16:0 and LysoPC 18:0.
coefficient was altered when depression was included in the model (S3 Table). Our previous report showed that depression was a unique component for linear regression model in IGD associated with blood primary metabolites [13], suggesting that depression may be an interactive factor rather than a confounder. Subsequently, we tested covariance between the lipids and the clinical parameter using MANCOVA in which two lipids were set to dependent variables, and depression was set to covariates. The result showed that significant level of covariance relation was only observed between the lipid biomarkers and depression (p = .047). The result implied strong comorbidity between IGD and depression relative to other clinical characteristics under debate. However, the linear regression models including depression did not show the statistical significance based on AUC values by ROC analysis (p = .693) (S3 Table). 4. Conclusion To the best of our knowledge, the current study is the first report on lipidomic profiles of IGD. Although the sample size of our study is small and confirmative conclusion requires further expansion of observation and additional examination under controlled circumstances, our current findings imply the cumulative long-term effect of pathologic gaming on the brain development in young adults. The current exploratory investigation on the lipid biomarker cluster proves the clinical applicability, which may improve the pathological definition for IGD and increase the accuracy of the diagnosis. In addition, the identified metabolic features, the relatedness with clinical parameters, and the putative biochemical linkage will hopefully aid future pathological studies in IGD.
Fig. 3. Validation of lipidomic markers based on Receiver Operating Characteristic (ROC) analysis. ROC curves for LysoPC 16:0 (blue), LysoPC 18:0 (green), linear regression model in combination with LysoPC 16:0 and LysoPC 18:0 (red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Competing interests The authors have declared that no competing interests exist. Acknowledgments
After adjustment of the four confounders, 17 out of 19 lipids were still present at significantly different levels in the IGD group compared to healthy controls. The list included two lipid biomarkers (LysoPC 16:0 and LysoPC 18:0) (p < .05, S2 Table). Likewise, the confounding effect on linear relation was evaluated, which may affect the linear regression model for the biomarker panel. The statistical significance was identified in ADHD score and depression using ANCOVA (p < .05, S1 Table). The significant covariance was identified in ADHD score and depression against IGD score. Subsequently, we examined whether regression of coefficient (variables vs IGD score) is significantly altered by including ADHD score and depression to the regression model. The result showed that < 10% of regression of coefficient was changed with ADHD score, which indicated that no significant effect of ADHD score on the linear regression model (S3 Table). On contrary, > 15% of regression of
This study was supported by a grant of the Korean Mental Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HM15C2578) to Y.C.J., and by the Brain Research Program and the Bio & Medical Technology Development Program through the National Research Foundation funded by the Ministry of Science and ICT (NRF-2016R1C1B2007982 and NRF-2016M3A9B6902062) to D.Y.L. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jchromb.2019.03.027. 123
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