Resonance assignments were executed utilising Chenomx software, and metabolite quantification was accomplished through the automated Bayesil software
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Nuclear Magnetic Resonance (NMR) -"],"publication":["Sex differences modulate the serum metabolome profiles in Parkinson’s disease patients harbouring pathogenic mutations in GBA1, LRRK2, TMEM175, PARK2, PINK1, and PARK7 genes."],"nmr_spectroscopy_protocol":["NMR studies were performed using a Bruker DRX600 MHz spectrometer (Bruker, Karlsruhe, Germany), equipped with a 5 mm triple-resonance z-gradient TXI Probe. We used TOPSPIN version 3.2 (Bruker Biospin, Fällanden, Switzerland) for spectrometer control and data analysis.
"],"submitter_affiliation":["University of Salerno"],"submitter_name":["Carmen Marino"],"organism_part":["blood serum"],"technology_type":["NMR spectroscopy assay"],"disease":[""],"extraction_protocol":["Blood sampling was performed after a 6-h fasting. Whole blood was collected by peripheral venipuncture into clot activator tubes and gently mixed. Sample was stored upright for 30 min at room temperature to allow blood to clot and centrifuged at 2000 ×g for 10 min at room temperature. Serum was aliquoted (0.5 ml) in polypropylene cryotubes and stored at −80 C before usage. Unique anonymized codes have been assigned to the samples for processing and subsequent analysis, maintaining the confidentiality of personal data.
"],"organism":["Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS12753"],"author":["Alessandro Usiello. Ceinge Biotecnologie Avanzate (Italy). usiello@ceinge.unina.it.","Carmen Marino. cmarino@unisa.it."],"data_transformation_protocol":["We examined the concentration matrices derived from NMR peak quantification through a univariate method integrating a T-test and fold change analysis, depicted in a comprehensive volcano plot. We set a fold change threshold of 1 and a p-value threshold of less than 0.05. (Lötsch et al., 2024) The matrices underwent normalisation through sum and Pareto scaling before analysis. A partial least-squares discriminant analysis (PLS-DA) was performed on the normalised metabolomics data using MetaboAnalyst 6.0 (http://www.metaboanalyst.ca/) (Pang et al., 2024). The PLS-DA model's performance was assessed using the Q2 coefficient (with a 10-fold internal cross-validation method) and the R2 coefficient, which represent the variance predicted and explained by the model, respectively (Wu et al., 2024). Both metrics are considered significant if they yield positive values, and the model's accuracy was also assessed.
"],"study_factor":["Genotype","Gender"],"submitter_email":["cmarino@unisa.it"],"sample_collection_protocol":["119 independent and unrelated PD patients were selected from the entire MNI-PD cohort of 804 PD patients. This is the same cohort of genetic patients that we used in our recent HPLC and NMR papers (Yahyavi et al. 2025; Marino et al 2025). This cohort of 119 genetic PD patients includes patients carrying at least one pathogenic mutation in the most frequently mutated PD genes LRRK2, GBA1, TMEM175 and PARK2/PINK1/PARK7 . This cohort was used for NMR untargeted metabolomic analysis and results were compared with HPLC data from our previous study.48 neurological controls were selected from the MNI-HC cohort. The NMR-HC cohort matched for sex and age with NMR-PD cohort and was negative for mutation/variant in PD genes
"],"nmr_assay_protocol":["Acquiring the Carr-Purcell-Meiboom-Gill (CPMG) spectrum was crucial due to the presence of macromolecules, like proteins, in serum that could disrupt signals associated with other components metabolites (Ghini et al., 2019).
CPMG experiments were conducted with a spectral width of 7 kHz and 32,000 data points. Water presaturation lasted for 5 seconds during the relaxation delay, and a spin-echo delay of 0.3 ms was utilised. A weighted Fourier transform was applied to the time-domain data with a line broadening of 0.5 Hz, followed by manual adjustments for phase and baseline in preparation for targeted profiling analysis
"],"omics_type":["Metabolomics"],"study_design":["Parkinson Disease","transmembrane protein 135 homolog (worm)","NMR","parkin protein","untargeted analysis","LRRK2","Bruker 600 MHz spectrometer","blood serum","untargeted metabolites","Homo sapiens","GBA1","experimental sample"],"curator_keywords":["transmembrane protein 135 homolog (worm)","Parkinson Disease","NMR","parkin protein","untargeted analysis","Bruker 600 MHz spectrometer","LRRK2","blood serum","Homo sapiens","untargeted metabolites","GBA1","experimental sample"],"nmr_sample_protocol":["Serum samples were prepared following the established NMR metabolomics standard operating procedures (SOP), as previously outlined reported. (Marino, Grimaldi et al. 2022). Serum samples that had been stored at -80 °c following centrifugation to separate them from plasma were subsequently thawed for the purpose of acquiring NMR spectra. To prevent any manipulation that might alter the metabolomic profile, only dilution of the serum in the acquisition buffer was conducted, while filtering downstream for macromolecular signals as indicated in the guidelines. (Emwas, Zacharias et al. 2025). For NMR sample preparation, 150 μL of phosphate buffer (0.075 M Na2HPO4·7H2O, 4% NaN3, and water) was combined with 150 μL of blood serum and transferred to a 3 mm NMR tube. Trimethylsilyl propionic acid and sodium salt (0.1% TSP in D2O) served as an internal reference for calibrating and measuring NMR signals
"],"metabolite_name":["TMAO","L-Arginine","Taurine","D-Fructose","D-Glucose","L-Phenylalanine","Lactate","2-Oxoglutarate","Creatine","D-Fucose","D-Maltose","L-Ornithine","L-Isoleucine","D-Mannose","L-Asparagine","L-Glycine","3-Hydroxybutyrate","Valerate","L-Threonine","Betaine","L-Alanine","L-Carnitine","D-Galactose","Acetoacetate","L-Leucine","Creatinine","Myo-Inositol","N-Acetylglycine","L-GlutamicAcid","L-Cystine","Pyruvate","Glycerol","Pyroglutamate","L-Proline","L-Glutamine","L-Tyrosine","Glucuronate","2-Hydroxyisovalerate","L-Serine","L-Valine","L-Aspartate","Glycerophosphocholine","2-Oxoisovalerate","2-Hydroxybutyrate"],"additional_accession":[]},"is_claimable":false,"name":"Sex differences modulate the serum metabolome profiles in Parkinson’s disease patients harbouring pathogenic mutations in GBA1, LRRK2, TMEM175, PARK2, PINK1, and PARK7 genes","description":"Background: Sex and genetic background have an impact on Parkinson’s disease (PD) insurgence, but the comprehension of how these factors affect the circulating profile of PD patients is still an aim of study.
Objectives: In this study we aimed to investigate whether genetic background and sex could highlight specific alteration at circulating level in the serum metabolome of PD patients stratified for gender carrying the most common pathogenic mutations in LRRK2, PINK1, PARK2, PARK7, TMEM175 and GBA1.
Methods: We applied an untargeted ¹H NMR-based analysis in serum samples of genetics PD patients (n=119) and sex-age matched controls (n=48). Specifically, we selected 17 LRRK2 carriers (F=11, M=6), 40 PINK1-PARK2-PARK7 carriers (F=18, M=22), 32 TMEM175 carriers (F= 12, M= 20) and 30 GBA1 carriers (F=17, M=13).
Results: Our results disclosed a different metabolomic signature between male and female PD patients, sustaining the hypothesis of a different impact of the disease across the two genders. Serum metabolomic analysis highlighted specific deregulated pathways based on genetic background and sex within PD patients groups. In particular, we demonstrated altered Folate, Glycine, Serine and Nicotinate and nicotamide metabolism in males TMEM175, GBA1, PARK2/PINK1/PARK7 and LRRK2 carriers, respectively. Conversely, across the female groups TMEM175, LRRK2 and PARK2/PIK1/PARK7 carriers showed specific alteration in Spermidine and spermine biosynthesis, Cystein and Ketone body metabolism, respectively. Interestingly, without considering the genetic background, males PD patients shared a higher number of impaired pathways, including amino acids metabolism, bioenergetic processes and antioxidant molecules production, when compared to the female ones whose shared alteration is represented by deregulated lipid pathways.
","dates":{"publication":"2026-06-26","submission":"2025-07-20"},"accession":"MTBLS12753","cross_references":{"MetaboLights":["MTBLC64552","MTBLC30915","MTBLC16530","MTBLC37054","MTBLC13705","MTBLC60645","MTBLC17750","MTBLC16919","MTBLC16737","MTBLC37714","MTBLC28847","MTBLC4139","MTBLC4167","MTBLC4153","MTBLC18147","MTBLC4208","MTBLC17754","MTBLC16870","MTBLC24996","MTBLC16977","MTBLC16467","MTBLC17196","MTBLC17053","MTBLC16347","MTBLC16283","MTBLC16015","MTBLC18050","MTBLC15428","MTBLC17191","MTBLC15603","MTBLC15729","MTBLC17295","MTBLC17203","MTBLC17115","MTBLC16857","MTBLC17895","MTBLC16414","MTBLC17268","MTBLC40410","MTBLC18183","MTBLC15361","MTBLC15891","MTBLC15724","MTBLC31011"],"ChEBI":["CHEBI:64552","CHEBI:30915","CHEBI:16530","CHEBI:37054","CHEBI:13705","CHEBI:60645","CHEBI:17750","CHEBI:16919","CHEBI:16737","CHEBI:37714","CHEBI:28847","CHEBI:4139","CHEBI:4167","CHEBI:4153","CHEBI:18147","CHEBI:4208","CHEBI:17754","CHEBI:16870","CHEBI:24996","CHEBI:16977","CHEBI:16467","CHEBI:17196","CHEBI:17053","CHEBI:16347","CHEBI:16283","CHEBI:16015","CHEBI:18050","CHEBI:15428","CHEBI:17191","CHEBI:15603","CHEBI:15729","CHEBI:17295","CHEBI:17203","CHEBI:17115","CHEBI:16857","CHEBI:17895","CHEBI:16414","CHEBI:17268","CHEBI:40410","CHEBI:18183","CHEBI:15361","CHEBI:15891","CHEBI:15724","CHEBI:31011"]}}