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.

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Study IDST002993
Study TitleIdentifying subgroups of childhood obesity by using multiplatform metabotyping
Study SummaryObesity 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
LaboratoryCEMBIO
Last NameChamoso-Sánchez
First NameDavid
AddressUrb. Montepríncipe. 28925 Alcorcón, Madrid (España)
Emaildavid.chamososanchez@usp.ceu.es
Phone(+34)913724769
Submit Date2023-11-07
Num Groups2
Total Subjects110
Num Males53
Num Females57
Raw Data AvailableYes
Raw Data File Type(s)mzXML
Analysis Type DetailGC/LC-MS
Release Date2023-12-04
Release Version1
David Chamoso-Sánchez David Chamoso-Sánchez
https://dx.doi.org/10.21228/M8WX4S
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Factor
SA325834C624Idiopathic obesity
SA325835C615Idiopathic obesity
SA325836C628Idiopathic obesity
SA325837C636Idiopathic obesity
SA325838C668Idiopathic obesity
SA325839C578Idiopathic obesity
SA325840C1133Idiopathic obesity
SA325841C2312Idiopathic obesity
SA325842C2310Idiopathic obesity
SA325843C2318Idiopathic obesity
SA325844C2319Idiopathic obesity
SA325845C756Idiopathic obesity
SA325846C572Idiopathic obesity
SA325847C1171Idiopathic obesity
SA325848C1222Idiopathic obesity
SA325849C1204Idiopathic obesity
SA325850C1229Idiopathic obesity
SA325851C1238Idiopathic obesity
SA325852C842Idiopathic obesity
SA325853C947Idiopathic obesity
SA325854C904Idiopathic obesity
SA325855C791Idiopathic obesity
SA325856C2296Idiopathic obesity
SA325857C820Idiopathic obesity
SA325858C841Idiopathic obesity
SA325859C877Idiopathic obesity
SA325860C772Idiopathic obesity
SA325861C726Idiopathic obesity
SA325862C1539Idiopathic obesity
SA325863C1538Idiopathic obesity
SA325864C1548Idiopathic obesity
SA325865C1030Idiopathic obesity
SA325866C1564Idiopathic obesity
SA325867C1556Idiopathic obesity
SA325868C1520Idiopathic obesity
SA325869C1453Idiopathic obesity
SA325870C2289Idiopathic obesity
SA325871C1306Idiopathic obesity
SA325872C1331Idiopathic obesity
SA325873C1336Idiopathic obesity
SA325874C1366Idiopathic obesity
SA325875C1768Idiopathic obesity
SA325876C1317Idiopathic obesity
SA325877C2249Idiopathic obesity
SA325878C1131Idiopathic obesity
SA325879C2259Idiopathic obesity
SA325880C2275Idiopathic obesity
SA325881C1782Idiopathic obesity
SA325882C2226Idiopathic obesity
SA325883C2284Idiopathic obesity
SA325884C1937Idiopathic obesity
SA325885C2218Idiopathic obesity
SA325886C2002Idiopathic obesity
SA325887C2158Idiopathic obesity
SA325888C2188Idiopathic obesity
SA325889M393Monogenic obesity
SA325890M37Monogenic obesity
SA325891M538Monogenic obesity
SA325892M2126Monogenic obesity
SA325893M516Monogenic obesity
SA325894M489Monogenic obesity
SA325895M2220Monogenic obesity
SA325896M2178Monogenic obesity
SA325897M539Monogenic obesity
SA325898M2195Monogenic obesity
SA325899M1104Monogenic obesity
SA325900M2253Monogenic obesity
SA325901M2258Monogenic obesity
SA325902M913Monogenic obesity
SA325903M1145Monogenic obesity
SA325904M1120Monogenic obesity
SA325905M1147Monogenic obesity
SA325906M1176Monogenic obesity
SA325907M2105Monogenic obesity
SA325908M996Monogenic obesity
SA325909M962Monogenic obesity
SA325910M1116Monogenic obesity
SA325911M759Monogenic obesity
SA325912M803Monogenic obesity
SA325913M881Monogenic obesity
SA325914M908Monogenic obesity
SA325915M637Monogenic obesity
SA325916M796Monogenic obesity
SA325917M1361Monogenic obesity
SA325918M1344Monogenic obesity
SA325919M1380Monogenic obesity
SA325920M1044Monogenic obesity
SA325921M1518Monogenic obesity
SA325922M1322Monogenic obesity
SA325923M1309Monogenic obesity
SA325924M1223Monogenic obesity
SA325925M1202Monogenic obesity
SA325926M1239Monogenic obesity
SA325927M1272Monogenic obesity
SA325928M1294Monogenic obesity
SA325929M1558Monogenic obesity
SA325930M1605Monogenic obesity
SA325931M1884Monogenic obesity
SA325932M1078Monogenic obesity
SA325933M1986Monogenic obesity
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