Urine metabolomic profiles of autism and autistic traits – a twin study Urine metabolomic profiles of autism and autistic traits – a twin study 2024-444-1 https://doi.org/10.48723/6821-pn89 Swedish National Data Service Svensk nationell datatjänst Landing page Urine metabolomic profiles of autism and autistic traits – a twin study Urine metabolomic profiles of autism and autistic traits – a twin study 2024-444-1 https://doi.org/10.48723/6821-pn89 oai:gup.ub.gu.se/200442 10.1017/thg.2014.12 Arora, Abishek Arora, Abishek Mastropasqua, Francesca Mastropasqua, Francesca Bölte, Sven Bölte, Sven Tammimies, Kristiina Tammimies, Kristiina 2019-01303_VR 2019-01303_VR Swedish National Data Service Svensk nationell datatjänst Landing page Pervasive developmental disorders Genomgripande utvecklingsstörningar Autism Spectrum Disorder Autismspektrumstörning Specific diseases, disorders and medical conditions Specifika sjukdomar, störningar och medicinska tillstånd The dataset consists of two excel files. One (Full_Data_Arora...) contains data for all 105 individuals in the study described below. The other (Data_Arora...) is a subset of the data containing the data for the 48 complete twins that were part of the study. In this study, individuals (N=105) and 48 complete twin pairs were selected from the RATSS cohort , which is a neurodevelopmental condition (NDC) enriched twin sample recruited from the general Swedish population between June 2011 and December 2015, for untargeted mass spectrometry-based urine metabolomics. Detailed inclusion and exclusion criteria for RATSS have been previously published. Among the recruited twins, we selected participants for this study based on the autism diagnosis status, whether the twin pair was concordant (both with autism diagnosis) or discordant (only one with autism diagnosis) and if they had available urine samples. Furthermore, we age- and sex-matched the non-autistic twin pairs. The study was approved by the Swedish Ethical Review Authority (2016/1452-31). Written informed consent was obtained from all participants or their caregivers, based on their age. During a 2.5-day study visit, a team of clinical professionals conducted a diagnostic evaluation of the participants in line with the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) guidelines. The evaluation utilised a combination of diagnostic interviews, a review of medical history documents, and the use of established diagnostic measures [19]. This included behavioural assessment tools such as the Autism Diagnostic Interview-Revised (ADI-R), the Autism Diagnostic Observation Schedule 2nd Edition (ADOS-2). Furthermore, additional tools were used to establish the diagnosis of other NDCs, if any, such the Diagnostic Interview for ADHD in Adults (DIVA-2), the Structured Clinical Interview for DSM-IV (SCID-IV), and the Adaptive Behavior Assessment System (ABAS) – more detailed information about the same is available in the publication by Bölte and colleagues (PMID: 24735654). Autistic traits were evaluated with the parent-report version of the Social Responsiveness Scale 2nd Edition (SRS-2), consisting of 65 items. Intelligence quotient (IQ) was measured by using the Wechsler Intelligence Scale for Children or Adults - IV General Ability Index (GAI). Additionally, the participants were asked for a list of their current, regularly used medications during the study visit. As there was a collection of different medications including antidepressants and ADHD medication, all were grouped together and adjusted for in our analyses. No subgrouping was possible for the drugs due to lack of power to detect metabolomic effects of specific drugs. Urine collection and metabolite extraction The urine samples were collected at the last day of the visit from the study participants. First, the participants were informed about the urine sample collection into a special urine cup. The urine cup was then given to a research nurse who transferred 10 mL of the collected urine to a sterile vacutainer tube with no additives. The sample was then directly transported, aliquoted and stored in the Karolinska Institutet Biobank at -80 °C. The collected samples were further transported for analysis to the Proteomics and Metabolomics Facility, University of Tuscia, Italy. All samples were handled as per the same stated protocol. Before the metabolomic analysis, the urinary specific gravity was measured following centrifugation at 13,000 g for 10 minutes. Urine aliquots (200 μl) were mixed with 200 μl of methanol:acetonitrile:water (50:30:20), vortexed for 30 minutes, maximum speed at 4 °C and then centrifuged at 16,000 g for 15 minutes at 4 °C. Supernatants were collected for metabolomic analysis. Ultra-High Performance Liquid Chromatography (UHPLC) For the experiments, 20 µL of samples were injected into a UPLC system (Ultimate 3000, Thermo Scientific) and were analysed on positive mode: samples were loaded onto a Reprosil C18 column (2.0 mm × 150 mm, 2.5 μm - Dr Maisch, Germany) for metabolite separation. Chromatographic separations were achieved at a column temperature of 30 °C and flow rate of 0.2 mL/min. A linear gradient (0–100%) of solvent A (ddH2O, 0.1% formic acid) to B (acetonitrile, 0.1% formic acid) was employed over 20 minutes, returning to 100% solvent A in 2 minutes and a 6-minute post-time solvent A hold. Acetonitrile, formic acid, and HPLC-grade water were purchased from Sigma Aldrich. High Resolution Mass Spectrometry (HRMS) The UPLC system was coupled online with a mass spectrometer, Q Exactive (Thermo Scientific), scanning in full MS mode (2 μ scans) at a resolution of 70,000 in the 67 to 1000 m/z range, target of 1 × 106 ions and a maximum ion injection time (IT) of 35 ms, 3.8 kV spray voltage, 40 sheath gas, and 25 auxiliary gas, operated in negative and then positive ion mode. Source ionization parameters were: spray voltage, 3.8 kV; capillary temperature, 300 °C; and S-Lens level, 45. Calibration was performed before each analysis against positive or negative ion mode calibration mixes (Piercenet, Thermo Fisher, Rockford, IL) to ensure sub-ppm error of the intact mass. Metabolite quantification Data were normalized by urinary specific gravity because creatinine excretion may be abnormally reduced in autistic children [31]. Replicates were exported as mzXML files and processed through MAVEN [32]. Mass spectrometry chromatograms were elaborated for peak alignment, matching and comparison of parent and fragment ions, and tentative metabolite identification (within a 10-ppm mass deviation range between observed and expected results against the imported Kyoto Encyclopaedia of Genes and Genomes (KEGG) database . Datasetet består av två excel filer. En av dem (Full_Data_Arora...) innehåller data för 105 individer från studien beskriven nedan. Den andra filen (Data_Arora...) är en delmängd av datasetet som innehåller data för de 48 kompletta tvillingpar från studien. I denna studie valdes individer (N=105) och 48 kompletta tvillingpar från RATSS-kohorten, som är ett tvillingurval berikat med neuropsykiatriska funktionsnedsättningar (NPF) rekryterade från den allmänna svenska befolkningen mellan juni 2011 och december 2015, för icke-riktad masspektrometribaserad metabolomik av urinprover. Detaljerade inklusions- och exklusionskriterier för RATSS har tidigare publicerats. Bland de rekryterade tvillingarna valde vi deltagare för denna studie baserat på autismdiagnosstatus, om tvillingparet var konkordant (båda med autismdiagnos) eller diskordant (endast en med autismdiagnos) och om de hade tillgängliga urinprover. Dessutom matchade vi de icke-autistiska tvillingparen efter ålder och kön. Studien godkändes av den svenska Etikprövningsmyndigheten (2016/1452-31). Skriftligt informerat samtycke erhölls från alla deltagare eller deras vårdnadshavare, beroende på deras ålder. Under ett 2,5-dagars studiebesök genomförde ett team av kliniska experter en diagnostisk utvärdering av deltagarna i enlighet med riktlinjerna i Diagnostic and Statistical Manual of Mental Disorders, 5 (DSM-5). Utvärderingen använde en kombination av diagnostiska intervjuer, granskning av medicinska historiedokument och användning av etablerade diagnostiska verktyg. Detta inkluderade beteendebedömningsverktyg som Autism Diagnostic Interview-Revised (ADI-R) och Autism Diagnostic Observation Schedule 2nd Edition (ADOS-2). Dessutom användes ytterligare verktyg för att fastställa diagnoser av andra NPF, såsom Diagnostic Interview for ADHD in Adults (DIVA-2), Structured Clinical Interview for DSM-IV (SCID-IV) och Adaptive Behavior Assessment System (ABAS) – mer detaljerad information finns i publikationen av Bölte och kollegor (PMID: 24735654). Autistiska drag utvärderades med föräldrarapporterade versionen av Social Responsiveness Scale 2nd Edition (SRS-2), bestående av 65 punkter. Intelligenskvot (IQ) mättes med Wechsler Intelligence Scale for Children eller Adults - IV General Ability Index (GAI). Dessutom tillfrågades deltagarna om en lista över deras aktuella, regelbundet använda mediciner under studiebesöket. Eftersom det fanns en samling av olika mediciner inklusive antidepressiva och ADHD-mediciner, grupperades alla tillsammans och justerades för i våra analyser. Ingen undergruppering var möjlig för läkemedlen på grund av brist på kraft att upptäcka metabolomiska effekter av specifika läkemedel. Urin uppsamling och metabolomik: Urinproverna samlades in den sista dagen av besöket från studiedeltagarna. Först informerades deltagarna om urinprovsamlingen i en särskild urinbägare. Urinbägaren gavs sedan till en forskningssjuksköterska som överförde 10 ml av den insamlade urinen till ett sterilt vakuumrör utan tillsatser. Provet transporterades sedan direkt, delades upp och lagrades i Karolinska Institutets biobank vid -80 °C. De insamlade proverna transporterades vidare för analys till Proteomics and Metabolomics Facility, University of Tuscia, Italien. Alla prover hanterades enligt samma angivna protokoll. Före metabolomanalysen mättes urinens specifika gravitation efter centrifugering vid 13 000 g i 10 minuter. Urinalikvoter (200 μl) blandades med 200 μl metanol:acetonitril (50:30:20), vortexades i 30 minuter, vid maximal hastighet vid 4 °C och centrifugerades sedan vid 16 000 g i 15 minuter vid 4 °C. Supernatanten samlades in för metabolomanalys. Ultra-High Performance Liquid Chromatography (UHPLC) För experimenten injicerades 20 µL av proverna i ett UPLC-system (Ultimate 3000, Thermo Scientific) och analyserades i positivt läge: prover laddades på en Reprosil C18-kolonn (2,0 mm × 150 mm, 2,5 μm - Dr Maisch, Tyskland) för separation av metaboliter. Kromatografiska separationer uppnåddes vid en kolonntemperatur av 30 °C och ett flödeshastighet på 0,2 mL/min. En linjär gradient (0–100%) av lösningsmedel A (ddH2O, 0,1% myrsyra) till B (acetonitril, 0,1% myrsyra) användes över 20 minuter, återgår till 100% lösningsmedel A på 2 minuter och en 6-minuters eftertid med lösningsmedel A. Acetonitril, myrsyra och HPLC-klass vatten köptes från Sigma Aldrich. Högupplösande masspektrometri (HRMS) UPLC-systemet kopplades online med en masspektrometer, Q Exactive (Thermo Scientific), som skannade i full MS-läge (2 μ-skanningar) med en upplösning av 70 000 i intervallet 67 till 1000 m/z, mål på 1 × 106 joner och en maximal joninjektionstid (IT) på 35 ms, 3,8 kV sprutspänning, 40 skyddsgas och 25 hjälpgas, som användes i negativt och sedan positivt jonläge. Källjoniseringsparametrar var: sprutspänning, 3,8 kV; kapillär temperatur, 300 °C; och S-Lens nivå, 45. Kalibrering utfördes före varje analys mot positiva eller negativa jonlägeskalibreringsmixar (Piercenet, Thermo Fisher, Rockford, IL) för att säkerställa sub-ppm-fel av den intakta massan. Metabolitmätning: Data normaliserades efter urinens specifika gravitation eftersom kreatininutsöndring kan vara onormalt reducerad hos autistiska barn [31]. Replikat exporterades som mzXML-filer och bearbetades genom MAVEN [32]. Masspektrometrikromatogram utarbetades för toppjustering, matchning och jämförelse av föräldra- och fragmentjoner, och tentativ metabolitidentifiering (inom ett 10-ppm massavvikelseintervall mellan observerade och förväntade resultat mot den importerade Kyoto Encyclopedia of Genes and Genomes (KEGG) databasen). Sweden Sverige IndividualIndividual IndividIndivid The study includes individuals (N=105) from the RATSS cohort, including 48 complete twin pairs. The RATSS cohort is a twin study focusing on neuropsychiatric conditions, recruited from the general Swedish population between June 2011 and December 2023. Studien omfattar individer (N=105) från the Roots of Autism and ADHD study in Sweden (RATSS-)kohorten, inklusive 48 kompletta tvillingpar. RATSS-kohorten är en tvillingstudie som fokuserar på neuropsykiatriska tillstånd och rekryterades från den allmänna svenska populationen mellan juni 2011 och december 2023. Numeric Text Cross-sectionCross-section TvärsnittTvärsnitt The sample includes individuals from the RATSS cohort, recruited between June 2011 and December 2015. It includes twins who are concordant or discordant for autism and had available urine samples. Participants were selected based on their autism diagnosis status and the availability of urine samples. Participants underwent diagnostic evaluations according to DSM-5 guidelines.The sample includes individuals from the RATSS cohort, recruited between June 2011 and December 2015. It includes twins who are concordant or discordant for autism and had available urine samples. Participants were selected based on their autism diagnosis status and the availability of urine samples. Participants underwent diagnostic evaluations according to DSM-5 guidelines. Urvalet omfattar individer från RATSS-kohorten, rekryterade mellan juni 2011 och december 2015. Det inkluderar tvillingar som är konforma eller diskordanta för autism och som hade tillgängliga urinprover. Deltagarna valdes ut baserat på deras autismdiagnosstatus och tillgänglighet av urinprover. Deltagarna genomgick diagnostiska utvärderingar enligt DSM-5 riktlinjer.Urvalet omfattar individer från RATSS-kohorten, rekryterade mellan juni 2011 och december 2015. Det inkluderar tvillingar som är konforma eller diskordanta för autism och som hade tillgängliga urinprover. Deltagarna valdes ut baserat på deras autismdiagnosstatus och tillgänglighet av urinprover. Deltagarna genomgick diagnostiska utvärderingar enligt DSM-5 riktlinjer. Mixed probability and non-probabilityMixed probability and non-probability Blandat sannolikhets- och icke-sannolikhetsurvalBlandat sannolikhets- och icke-sannolikhetsurval 1) Phenotypic Data Collection Method: Phenotypic data were collected during a 2.5-day study visit where a team of clinical professionals conducted diagnostic evaluations of participants according to the guidelines in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). Evaluations included diagnostic interviews, review of medical records, and the use of established diagnostic tools. Diagnostic Tools: Autism Diagnostic Interview-Revised (ADI-R) Autism Diagnostic Observation Schedule 2nd Edition (ADOS-2) Diagnostic Interview for ADHD in Adults (DIVA-2) Structured Clinical Interview for DSM-IV (SCID-IV) Adaptive Behavior Assessment System (ABAS) Additional Assessments: Social Responsiveness Scale 2nd Edition (SRS-2) to evaluate autistic traits Wechsler Intelligence Scale for Children or Adults - IV General Ability Index (GAI) to measure intelligence quotient (IQ) 2) Urine Samples Collection Method: Urine samples were collected from participants on the last day of the study visit. Participants were instructed on how to collect urine in a special urine cup. The urine was then transferred by a research nurse to a sterile vacutainer tube without additives and directly transported to be stored at -80 °C in the Karolinska Institutet Biobank. Processing and Analysis: Urine samples were transported to the Proteomics and Metabolomics Facility at the University of Tuscia, Italy for analysis. Prior to metabolomics analysis, urinary specific gravity was measured, and samples were centrifuged. Urine aliquots were mixed with methanol:acetonitrile , vortexed, and centrifuged before the supernatant was collected for metabolomics analysis. 3) Metabolomics Data Collection Method: Metabolomics data were collected using Ultra-High Performance Liquid Chromatography (UHPLC) coupled with High Resolution Mass Spectrometry (HRMS). Urine samples were analyzed to identify metabolites with high reliability. Processing and Analysis: Data were normalized by urinary specific gravity. Replicates were exported as mzXML files and processed through MAVEN for peak alignment, matching, and comparison of parent and fragment ions. Metabolite identification was performed using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. Reference: "UHPLC was coupled with HRMS for the untargeted analysis of metabolites... Data were normalized by urinary specific gravity... processed through MAVEN" (Urine Collection and Metabolite Extraction). Analysis Methods 1. Ultra-High Performance Liquid Chromatography (UHPLC) Description: UHPLC was used to separate the metabolites present in the urine samples. A Reprosil C18 column was employed for the separation. The chromatographic separations were achieved at a column temperature of 30 °C and a flow rate of 0.2 mL/min with a linear gradient of solvent A (water with 0.1% formic acid) to solvent B (acetonitrile with 0.1% formic acid). Reference: "For the experiments 20 µL of samples were injected into a UPLC system (Ultimate 3000 Thermo . 2. High Resolution Mass Spectrometry (HRMS) Description: The UHPLC system was coupled online with a mass spectrometer (Q Exactive, Thermo Scientific) to detect and quantify metabolites. The mass spectrometer operated in both positive and negative ion modes, with source ionization parameters set to ensure optimal detection. 3. Metabolite Quantification and Identification Description: Data from the mass spectrometry were normalized by urinary specific gravity. The chromatograms were processed using MAVEN software for peak alignment and metabolite identification, matched against the KEGG database within a 10-ppm mass deviation range.1) Phenotypic Data Collection Method: Phenotypic data were collected during a 2.5-day study visit where a team of clinical professionals conducted diagnostic evaluations of participants according to the guidelines in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). Evaluations included diagnostic interviews, review of medical records, and the use of established diagnostic tools. Diagnostic Tools: Autism Diagnostic Interview-Revised (ADI-R) Autism Diagnostic Observation Schedule 2nd Edition (ADOS-2) Diagnostic Interview for ADHD in Adults (DIVA-2) Structured Clinical Interview for DSM-IV (SCID-IV) Adaptive Behavior Assessment System (ABAS) Additional Assessments: Social Responsiveness Scale 2nd Edition (SRS-2) to evaluate autistic traits Wechsler Intelligence Scale for Children or Adults - IV General Ability Index (GAI) to measure intelligence quotient (IQ) 2) Urine Samples Collection Method: Urine samples were collected from participants on the last day of the study visit. Participants were instructed on how to collect urine in a special urine cup. The urine was then transferred by a research nurse to a sterile vacutainer tube without additives and directly transported to be stored at -80 °C in the Karolinska Institutet Biobank. Processing and Analysis: Urine samples were transported to the Proteomics and Metabolomics Facility at the University of Tuscia, Italy for analysis. Prior to metabolomics analysis, urinary specific gravity was measured, and samples were centrifuged. Urine aliquots were mixed with methanol:acetonitrile , vortexed, and centrifuged before the supernatant was collected for metabolomics analysis. 3) Metabolomics Data Collection Method: Metabolomics data were collected using Ultra-High Performance Liquid Chromatography (UHPLC) coupled with High Resolution Mass Spectrometry (HRMS). Urine samples were analyzed to identify metabolites with high reliability. Processing and Analysis: Data were normalized by urinary specific gravity. Replicates were exported as mzXML files and processed through MAVEN for peak alignment, matching, and comparison of parent and fragment ions. Metabolite identification was performed using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. Reference: "UHPLC was coupled with HRMS for the untargeted analysis of metabolites... Data were normalized by urinary specific gravity... processed through MAVEN" (Urine Collection and Metabolite Extraction). Analysis Methods 1. Ultra-High Performance Liquid Chromatography (UHPLC) Description: UHPLC was used to separate the metabolites present in the urine samples. A Reprosil C18 column was employed for the separation. The chromatographic separations were achieved at a column temperature of 30 °C and a flow rate of 0.2 mL/min with a linear gradient of solvent A (water with 0.1% formic acid) to solvent B (acetonitrile with 0.1% formic acid). Reference: "For the experiments 20 µL of samples were injected into a UPLC system (Ultimate 3000 Thermo . 2. High Resolution Mass Spectrometry (HRMS) Description: The UHPLC system was coupled online with a mass spectrometer (Q Exactive, Thermo Scientific) to detect and quantify metabolites. The mass spectrometer operated in both positive and negative ion modes, with source ionization parameters set to ensure optimal detection. 3. Metabolite Quantification and Identification Description: Data from the mass spectrometry were normalized by urinary specific gravity. The chromatograms were processed using MAVEN software for peak alignment and metabolite identification, matched against the KEGG database within a 10-ppm mass deviation range. 1) Fenotypdata Insamlingsmetod: Fenotypdata samlades in under en 2,5-dagars studievisit där ett team av kliniska experter utförde diagnostiska utvärderingar av deltagarna enligt riktlinjerna i Diagnostic and Statistical Manual of Mental Disorders, 5 upplagan (DSM-5). Utvärderingarna inkluderade diagnostiska intervjuer, genomgång av medicinska journaler och användning av etablerade diagnostiska verktyg. Diagnostiska Verktyg: Autism Diagnostic Interview-Revised (ADI-R) Autism Diagnostic Observation Schedule 2nd Edition (ADOS-2) Diagnostic Interview for ADHD in Adults (DIVA-2) Structured Clinical Interview for DSM-IV (SCID-IV) Adaptive Behavior Assessment System (ABAS) Ytterligare Bedömningar: Social Responsiveness Scale 2nd Edition (SRS-2) för att utvärdera autistiska drag Wechsler Intelligence Scale for Children or Adults - IV General Ability Index (GAI) för att mäta intelligenskvot (IQ) 2) Urinprover Insamlingsmetod: Urinprover samlades in från deltagarna under sista dagen av studievisiten. Deltagarna informerades om hur urinen skulle samlas in i en speciell urinbägare. Urinen överfördes sedan av en forskningssjuksköterska till en steril vakuumrör utan tillsatser och transporterades direkt för att lagras vid -80 °C i Karolinska Institutets biobank. Bearbetning och Analys: Urinproverna transporterades till Proteomics and Metabolomics Facility vid University of Tuscia, Italien för analys. Före metabolomikanalysen mättes urinens specifika vikt och proverna centrifugerades. Urinaliquoter blandades med metanol:acetonitril, vortexades och centrifugerades innan supernatanten samlades in för metabolomikanalys. 3) Metabolomikdata Insamlingsmetod: Metabolomikdata samlades in genom Ultra-High Performance Liquid Chromatography (UHPLC) kopplat till High Resolution Mass Spectrometry (HRMS). Urinproverna analyserades för att identifiera metaboliter med hög tillförlitlighet. Bearbetning och Analys: Data normaliserades baserat på urinens specifika vikt. Replikat exporterades som mzXML-filer och bearbetades med MAVEN för toppjustering, matchning och jämförelse av föräldra- och fragmentjoner. Metabolitidentifiering utfördes med Kyoto Encyclopaedia of Genes and Genomes (KEGG) databasen.1) Fenotypdata Insamlingsmetod: Fenotypdata samlades in under en 2,5-dagars studievisit där ett team av kliniska experter utförde diagnostiska utvärderingar av deltagarna enligt riktlinjerna i Diagnostic and Statistical Manual of Mental Disorders, 5 upplagan (DSM-5). Utvärderingarna inkluderade diagnostiska intervjuer, genomgång av medicinska journaler och användning av etablerade diagnostiska verktyg. Diagnostiska Verktyg: Autism Diagnostic Interview-Revised (ADI-R) Autism Diagnostic Observation Schedule 2nd Edition (ADOS-2) Diagnostic Interview for ADHD in Adults (DIVA-2) Structured Clinical Interview for DSM-IV (SCID-IV) Adaptive Behavior Assessment System (ABAS) Ytterligare Bedömningar: Social Responsiveness Scale 2nd Edition (SRS-2) för att utvärdera autistiska drag Wechsler Intelligence Scale for Children or Adults - IV General Ability Index (GAI) för att mäta intelligenskvot (IQ) 2) Urinprover Insamlingsmetod: Urinprover samlades in från deltagarna under sista dagen av studievisiten. Deltagarna informerades om hur urinen skulle samlas in i en speciell urinbägare. Urinen överfördes sedan av en forskningssjuksköterska till en steril vakuumrör utan tillsatser och transporterades direkt för att lagras vid -80 °C i Karolinska Institutets biobank. Bearbetning och Analys: Urinproverna transporterades till Proteomics and Metabolomics Facility vid University of Tuscia, Italien för analys. Före metabolomikanalysen mättes urinens specifika vikt och proverna centrifugerades. Urinaliquoter blandades med metanol:acetonitril, vortexades och centrifugerades innan supernatanten samlades in för metabolomikanalys. 3) Metabolomikdata Insamlingsmetod: Metabolomikdata samlades in genom Ultra-High Performance Liquid Chromatography (UHPLC) kopplat till High Resolution Mass Spectrometry (HRMS). Urinproverna analyserades för att identifiera metaboliter med hög tillförlitlighet. Bearbetning och Analys: Data normaliserades baserat på urinens specifika vikt. Replikat exporterades som mzXML-filer och bearbetades med MAVEN för toppjustering, matchning och jämförelse av föräldra- och fragmentjoner. Metabolitidentifiering utfördes med Kyoto Encyclopaedia of Genes and Genomes (KEGG) databasen. Measurements and testsMeasurements and tests Mätningar och testerMätningar och tester Access to data through SND. Access to data is restricted. Åtkomst till data via SND. Tillgång till data är begränsad. restrictedAccess