Urine metabolomic profiles of autism and autistic traits – a twin study
SND-ID: 2024-444. Version: 1. DOI: https://doi.org/10.48723/6821-pn89
Associated documentation
Citation
Creator/Principal investigator(s)
Abishek Arora
- Karolinska Institutet, Department of Women's and Children's Health
Francesca Mastropasqua - Karolinska Institutet, Department of Women's and Children's Health
Sven Bölte
- Karolinska Institutet, Department of Women's and Children's Health
Kristiina Tammimies
- Karolinska Institutet, Department of Women's and Children's Health
Research principal
Karolinska Institutet
- Department of Women's and Children's Health
Description
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 pa
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 . Show less..
Data contains personal data
Yes
Sensitive personal data
Yes
Type of personal data
pseudonymized data, health data
Code key exists
Yes
Language
Unit of analysis
Population
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.
Time Method
Study design
Preclinical study
Sampling procedure
Biobank is connected to the study
The study has collected samples/material which are stored in a scientific collection or biobank
Scientific collection or biobank name: KI biobank
Type(s) of sample: urine
Variables
208
Number of individuals/objects
105
Response rate/participation rate
100%
Geographic spread
Geographic location: Sweden
Highest geographic unit
National area (NUTS2)
Responsible department/unit
Department of Women's and Children's Health
Contributor(s)
Abishek Arora - Karolinska Institutet
Francesca Mastropasqua - Karolinska Institutet, Department of Women's and Children's Health
Ethics Review
Stockholm - Ref. 2016/1452-31
Research area
Medical and health sciences (Standard för svensk indelning av forskningsämnen 2011)
Psychiatry (Standard för svensk indelning av forskningsämnen 2011)
Specific diseases, disorders and medical conditions (CESSDA Topic Classification)
Bolte, S., Willfors, C., Berggren, S., Norberg, J., Poltrago, L., Mevel, K., Coco, C., Fransson, P., Borg, J., Sitnikov, R., Toro, R., Tammimies, K., Anderlid, B. M., Nordgren, A., Falk, A., Meyer, U., Kere, J., Landén, M., Dalman, C., … Lichtenstein, P. (n.d.). The Roots of Autism and ADHD Twin Study in Sweden (RATSS). In Twin Research and Human Genetics (Vol. 17, Issue 3, pp. 164–176). https://doi.org/10.1017/thg.2014.12
DOI:
https://doi.org/10.1017/thg.2014.12
SwePub:
oai:gup.ub.gu.se/200442
If you have published anything based on these data, please notify us with a reference to your publication(s). If you are responsible for the catalogue entry, you can update the metadata/data description in DORIS.
