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4/20210826_blank_059.raw/_FUNC003.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210928_S_2_068.raw/_CHRO001.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/HILIC NEG/SET 1/20210923_S_004.raw/_FUNC002.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/RP POS/SET 3/20210802_S_114.raw/_FUNC002.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210902_S_030.raw/_CHRO001.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/RP POS/SET 2/20210729_S_2_068.raw/_CHRO001.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210923_S_2_002.raw/_CHRO001.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/HILIC POS/SET 4/20210920_QC_18.raw/_FUNC003.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/RP POS/SET 1/20210726_Blank_11.raw/_FUNC003.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210920_S_118.raw/_FUNC002.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/HILIC NEG/SET 2/20210928_blank_02.raw/_FUNC002.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/RP POS/SET 3/20210802_Blank_11.raw/_CHRO001.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210920_S_143.raw/_FUNC003.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/HILIC NEG/SET 4/20211005_S_119.raw/_CHRO001.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210818_QC_15.raw/_FUNC003.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210818_S_2_049.raw/_FUNC003.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/HILIC NEG/SET 2/20210928_S_2_057.raw/_FUNC003.DATftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965/FILES/RAW_FILES/20210802_S_093.raw/_CHRO001.DATprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS10965<p>Progenesis MetaScope (Non-linear dynamics, Waters) was used for the metabolite annotation. The metabolite annotation was based on accurate mass values, isotope abundance and MS/MS fragmentation patterns. Metabolite identification was performed using the Human Metabolome Database (HMDB) within a narrow mass error (10 ppm) Those annotations matched with the fragmentation database were labeled as MS level 2, and remaining annotations were labeled as MS Level 3[1-4].</p><p><br></p><p>Refs:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, et al. Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes. Sci Rep [Internet]. 2020;10(1):353. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31941993</p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, et al. Plasma leptin level mirrors metabolome alterations in young adults. Metabolomics [Internet]. 2020;16(8):87. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32772182</p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Anusree VR, Satheesh G, Vijayakumar G, Chandran M, Simon L, et al. Hyperhomocysteinemia-related serum metabolome alterations not normalized by short-term folic acid treatment. Metabolomics [Internet]. 2021 May 9;17(5):47. Available from: https://link.springer.com/10.1007/s11306-021-01798-z</p><p>4.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Salek R, Maguire M. A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol ... [Internet]. 2007;99–108. Available from: http://physiolgenomics.physiology.org/content/29/2/99.short</p>MetaboLightsPublicLiquid Chromatography MS - negative - reverse phaseLiquid Chromatography MS - positive - hilicLiquid Chromatography MS - positive - reverse phaseLiquid Chromatography MS - negative - hilic<p>An ultra-performance liquid chromatography, ACQUITY UPLC System (Waters) coupled to a Quadrupole-Time of Flight (Q-TOF) mass spectrometer (SYNAPT-G2, Waters) was used for the untargeted metabolomics analysis. Reversed-Phase Liquid Chromatography (RPLC) technique was employed with a C18 column (ACQUITY UPLC HSS T3 C18 column, 100 Å, 1.8 μm, 2.1mm x 100mm, Waters). In the RPLC Method, mobile phase consisted of aqueous (A) and organic (B) solvent components, where A was 0.1% formic acid in water and B was 0.1% formic acid in methanol. The gradient was 0 min, 0% B; 1 min, 0% B; 16 min, 100% B; 20 min, 100% B, and 22 min, 0% B. For the wash cycle, weak wash solvent (100%) water and strong wash solvent (100%) methanol were used in RPLC method. The Hydrophilic Interaction Chromatography (HILIC) method was performed with a BEH column (ACQUITY BEH HILIC column, 130 Å, 1.7 μm, 2.1mm x 150mm, Waters). For the HILIC mobile phase, A was 95% Acetonitrile in 10 mM ammonium acetate and B was 50% Acetonitrile in 10 mM ammonium acetate. Both A and B contained 0.1% formic acid and 0.1% acetic acid in positive ion mode and negative ion mode respectively. The HILIC positive ion gradient was 0 min, 1% B; 5 min, 20% B; 10 min, 50% B; 14 min, 95% B; 16 min, 95% B; 17 min, 1% B; and 20 min, 1% B. For HILIC wash cycle, weak wash solvent (100%) acetonitrile and strong solvent (100%) were used. The injection volume was 5 µL with a flow rate of 0.4 mL/min and the column temperature of 40 °C[1-3].</p><p><br></p><p>Refs:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, et al. Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes. Sci Rep [Internet]. 2020;10(1):353. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31941993</p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, et al. Plasma leptin level mirrors metabolome alterations in young adults. Metabolomics [Internet]. 2020;16(8):87. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32772182</p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Anusree VR, Satheesh G, Vijayakumar G, Chandran M, Simon L, et al. Hyperhomocysteinemia-related serum metabolome alterations not normalized by short-term folic acid treatment. Metabolomics [Internet]. 2021 May 9;17(5):47. Available from: https://link.springer.com/10.1007/s11306-021-01798-z</p>EFO: In preparation - Early Metabolic Predictors of Type 2 Diabetes Mellitus in Normoglycemic Adults: Insights from Six-Year Prospective Cohort Study.Rajiv Gandhi Centre for BiotechnologyGOPIKA SATHEESHblood plasmamass spectrometry<p>Blood samples were collected in Sodium fluoride and EDTA tubes, centrifuged at 3000 x g for 20 min at 4 °C and plasma samples were stored at -80 °C until further use. Plasma samples from both the baseline and the follow-up studies were used. Plasma protein precipitation was done using methanol, the plasma samples were centrifuged at 14,000 x g at 4 °C for 20 min, and the resultant supernatant was dried at room temperature using the SpeedVac concentrator. The dried samples were reconstituted in 100 µL of 50% methanol and 0.1% formic acid. The Quality control (QC) samples were prepared by pooling an equal volume of aliquots from all the samples.</p>Homo sapiens<p>Data acquisition and collection were carried out using MassLynx4.1 SCN781 (Waters). Progenesis QI (Non-linear dynamics, Waters) was employed for peak/feature picking and raw data deconvolution. MetaboAnalyst 6.0 facilitated signal correction of the metabolomics data with batch effects corrected using the ComBat algorithm[1-2] and missing data imputed using the k-nearest neighbor (KNN) approach. Metabolites with low repeatability were removed, retaining features present in at least 50% of the QC samples and 20% of the experimental samples. Metabolite features with a relative standard deviation of &lt;40% in the QC samples were used for further analysis.</p><p><br></p><p>Refs:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;Carry PM, Vigers T, Vanderlinden LA, Keeter C, Dong F, Buckner T, et al. Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments. BMC Bioinformatics [Internet]. 2023 Mar 7;24(1):86. Available from: http://www.ncbi.nlm.nih.gov/pubmed/36882691</p><p>2.&nbsp;&nbsp;&nbsp; Radua J, Vieta E, Shinohara R, Kochunov P, Quidé Y, Green MJ, et al. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA. Neuroimage [Internet]. 2020 Sep;218:116956. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32470572</p>Grouphttps://www.ebi.ac.uk/metabolights/MTBLS10965gopikasatheesh@rgcb.res.in<p>The Institutional Human Ethics Committee of both Rajiv Gandhi Centre for Biotechnology and Medical Trust Hospital and Diabetes Care Centre evaluated and sanctioned the study. A cohort of 110 healthy study participants from our baseline study were recruited for the current study. The inclusion at baseline required participants to be men and women aged from 18 to 40 years. Similarly, the exclusion criteria eliminated individuals with known illnesses, including diabetes mellitus or nutritional disorders as well as those attempting to lose weight, having allergies or under any medication. Out of the 110 participants, 94 were recruited for the current study, with 16 participants either migrated or declined to participate. The age range of participants at follow-up was from 24 to 46 years. The participants from the baseline study were contacted, and their informed consent was obtained. The participants were instructed to maintain their habitual diet, avoid unusual activity, and refrain from intentional exercise for three days prior to the study. On the study day morning, a blood sample at fasting was collected from the hand vein of the study participant. Following this, a mixed meal, providing 25% of the daily calorie requirement, was served. Blood samples were collected every 30 min post meal ingestion for 120 min.</p>Metabolomicsultra-performance liquid chromatography-mass spectrometryType 2 Diabetes MellitusInsulin Resistanceuntargeted metabolite profilingultra-performance liquid chromatography-mass spectrometryType 2 Diabetes MellitusInsulin Resistanceuntargeted metabolite profiling<p>The mass spectrometer was operated in resolution mode (MSE) with electrospray ionization (ESI). Data were acquired in both positive and negative modes for two chromatographic methods.&nbsp;The selected mass range was from 50 to 1200 m/z in full scan mode with a scan time of 0.25 s and an inter-scan delay of 0.024 s. Ion source and desolvation gas (nitrogen) temperatures were kept at 112 and 350 °C, respectively. The sampling cone and desolvation gas flow rates were 60 and 875 L/h, respectively. Capillary voltage was set at 2.85 kV. Sampling cone voltage was set at 40 V. Leucine enkephalin (MW = 555.62) performed the lock mass acquisition every 10 s for accurate real-time mass calibration. Initial 10 QC samples run was used for the stabilization of the system. QC samples were acquired after every 5th sample run[1-3].</p><p><br></p><p>Refs:</p><p>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, et al. Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes. Sci Rep [Internet]. 2020;10(1):353. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31941993</p><p>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, et al. Plasma leptin level mirrors metabolome alterations in young adults. Metabolomics [Internet]. 2020;16(8):87. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32772182</p><p>3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kumar AA, Anusree VR, Satheesh G, Vijayakumar G, Chandran M, Simon L, et al. Hyperhomocysteinemia-related serum metabolome alterations not normalized by short-term folic acid treatment. Metabolomics [Internet]. 2021 May 9;17(5):47. Available from: https://link.springer.com/10.1007/s11306-021-01798-z</p>2,3-Octanedione5,6-Dihydrouridine4-Hydroxyproline3alpha,7alpha-Dihydroxycoprostanic acidPC(14:0/20:1(11Z))L-Cysteinylglycine disulfide2-Hexyl-3-phenyl-2-propenalHistidinyl-PhenylalanineIsoleucylproline3-Phenyl-1-propanolPyrrole-2-carboxylic acidLysoPC(22:6(4Z,7Z,10Z,13Z,16Z,19Z))Tranexamic AcidThymolPalmitic acidCreatine3,4-Dihydroxymandelic acid1-Phenyl-1-propanonep-CresolUridine 5'-diphosphateCoumesterol2-Aminoheptanoate1-Methylnicotinamide4-Ethylphenol13S-hydroxyoctadecadienoic acidL-Carnitine3,4-DihydroxybenzaldehydeLysoPC(18:2(9Z,12Z))TrigonellineL-AcetylcarnitinePC(o-16:1(9Z)/18:0)trans-JasmoneLysoPC(16:0)Cyclohexanecarboxylic acid5-Aminopentanoic acidDihydrouracilProline betaineLysoPC(20:4(5Z,8Z,11Z,14Z))LysoPE(20:0/0:0)falseEFO: In preparation - Early Metabolic Predictors of Type 2 Diabetes Mellitus in Normoglycemic Adults: Insights from Six-Year Prospective Cohort Study<p>INTRODUCTION: Type 2 Diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance and hyperglycemia, often preceded by latent metabolic disruptions. Early detection of metabolic alterations can facilitate timely intervention to delay or prevent T2DM onset. Metabolic profiling offers a useful tool to identify novel disease biomarkers and metabolic alterations before clinical manifestations. OBJECTIVE: This study aimed to identify early metabolic alterations and potential biomarkers predictive of T2DM in a cohort of healthy normoglycemic participants over a six-year follow-up. METHODS: A cohort of 94 healthy participants, both men and women aged 18-40, was studied at six-year intervals. Clinical and biochemical parameters were measured, and LC/MS/MS-based untargeted metabolomics analysis of plasma was performed at baseline and follow-up. RESULTS: At follow-up, 9 participants developed T2DM, 51 had prediabetes and 34 remained normoglycemic. Increasing insulin resistance and elevated future T2DM risk were observed in both the Prediabetes and Normal groups. Metabolomics analysis identified Phosphatidylethanolamine (PE) (20:3_18:0), 3beta,7alpha-Dihydroxy-5-cholestenoate and Tridecanoic acid – as having good predictive capacity for future T2DM risk at baseline with alterations in Phosphatidylethanolamine (PE) (20:3_18:0), and Tridecanoic acid persisting at follow-up. CONCLUSION: The study highlights the potential of metabolomics in identifying early metabolic predictors of T2DM, emphasizing the need for early interventions in healthy normoglycemic young adults.</p>2025-08-042025-01-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:89640CHEBI:7563CHEBI:84212CHEBI:46229CHEBI:10319CHEBI:28637CHEBI:89818CHEBI:82968CHEBI:16345CHEBI:24344CHEBI:40410CHEBI:16704CHEBI:82944CHEBI:17895CHEBI:17497CHEBI:75977CHEBI:90059CHEBI:85639CHEBI:15756CHEBI:75454CHEBI:15355CHEBI:16196CHEBI:75145CHEBI:89195CHEBI:16814CHEBI:89173CHEBI:89284CHEBI:18183CHEBI:17489CHEBI:19062CHEBI:71169CHEBI:19289CHEBI:73912CHEBI:18388CHEBI:27905CHEBI:15671CHEBI:20912CHEBI:88773CHEBI:89730CHEBI:88728CHEBI:89935CHEBI:63977CHEBI:28247CHEBI:18332CHEBI:34506CHEBI:83037CHEBI:17687CHEBI:583580CHEBI:4676CHEBI:28832CHEBI:73718CHEBI:84505CHEBI:3210CHEBI:36274CHEBI:73723CHEBI:27471CHEBI:76308CHEBI:16755CHEBI:90452CHEBI:88639CHEBI:145278CHEBI:131692CHEBI:91309CHEBI:64489CHEBI:30813CHEBI:72747CHEBI:59270CHEBI:131924CHEBI:72998CHEBI:81015CHEBI:32368CHEBI:28610CHEBI:30805CHEBI:88829CHEBI:15399CHEBI:174893CHEBI:32111CHEBI:17596CHEBI:30852CHEBI:64486CHEBI:88718CHEBI:88797CHEBI:27226CHEBI:16831CHEBI:50205CHEBI:27637CHEBI:16577CHEBI:27607CHEBI:88585CHEBI:34154CHEBI:425902CHEBI:89053CHEBI:88817CHEBI:17847CHEBI:49584CHEBI:141438CHEBI:89930CHEBI:16797CHEBI:23774CHEBI:18095CHEBI:36751CHEBI:3908CHEBI:17659CHEBI:55365CHEBI:16919CHEBI:89892CHEBI:64517CHEBI:40682CHEBI:73873CHEBI:74344CHEBI:131690CHEBI:28733CHEBI:15887CHEBI:48669CHEBI:18123CHEBI:35280CHEBI:15901CHEBI:57589CHEBI:36096CHEBI:16347CHEBI:74076