Summary of Study ST002993
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001863. The data can be accessed directly via it's Project DOI: 10.21228/M8WX4S This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
Study ID | ST002993 |
Study Title | Identifying subgroups of childhood obesity by using multiplatform metabotyping |
Study Summary | Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios. |
Institute | Universidad CEU San Pablo |
Laboratory | CEMBIO |
Last Name | Chamoso-Sánchez |
First Name | David |
Address | Urb. Montepríncipe. 28925 Alcorcón, Madrid (España) |
david.chamososanchez@usp.ceu.es | |
Phone | (+34)913724769 |
Submit Date | 2023-11-07 |
Num Groups | 2 |
Total Subjects | 110 |
Num Males | 53 |
Num Females | 57 |
Raw Data Available | Yes |
Raw Data File Type(s) | mzXML |
Analysis Type Detail | GC/LC-MS |
Release Date | 2023-12-04 |
Release Version | 1 |
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Sample Preparation:
Sampleprep ID: | SP003112 |
Sampleprep Summary: | For LC-MS analysis, 40 µL of serum was mixed with 800 µL of a cold mixture (-20ºC) of methanol:MTBE:Chloroform (1.33:1:1, v/v/v) with Sphinganine (D17:0) and palmitic acid-d31 as internal standards. Samples were vortexed for 30 s and shaken for 20 min at maximum speed at room temperature. Next, samples were centrifuged (13,200 rpm, room temperature, 5 min). After centrifugation, supernatant was directly injected into the system. For GC-MS analysis, protein precipitation was achieved by mixing 1 volume of serum with 3 volumes of cold (-20ºC) acetonitrile with 25 ppm of palmitic acid-d31 as internal standard, followed by methoximation with O-methoxyamine hydrochloride (15 mg/mL) in pyridine, and sylation with BSTFA: TMCS (99:1). Finally, 20 ppm of tricosane in heptane was added as second internal standard. For CE-MS analysis, 100 µL of serum was mixed with 100 µL of 0.2 M formic acid containing 5% acetonitrile and 0.4 mM methionine sulfone, 2 mM paracetamol and 0.5 mM 4-Morpholineethanesulfonic acid, 2-(N-Morpholino) ethanesulfonic acid (MES) as internal standards. The sample was transferred to an ultracentrifugation device (Millipore Ireland Ltd., Carrigtohill, Ireland) with a 30 kDa protein cutoff for deproteinization through centrifugation (2000 × g, 4 °C, 90 min). |