An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. Methods We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to...

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Published inJournal of translational medicine Vol. 21; no. 1; pp. 229 - 13
Main Authors Mir, Fayaz Ahmad, Mall, Raghvendra, Ullah, Ehsan, Iskandarani, Ahmad, Cyprian, Farhan, Samra, Tareq A., Alkasem, Meis, Abdalhakam, Ibrahem, Farooq, Faisal, Taheri, Shahrad, Abou-Samra, Abdul-Badi
Format Journal Article
LanguageEnglish
Published London BioMed Central 29.03.2023
BioMed Central Ltd
BMC
Subjects
Analysis
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Body fat
Body mass index
Cardiovascular disease
Case-Control Studies
Complications and side effects
Diabetes
Fasting
Genotype & phenotype
Humans
Ions
Laboratories
Mass spectroscopy
Medicine/Public Health
Metabolic diseases
Metabolic pathways
Metabolic syndrome
Metabolic Syndrome - complications
Metabolic Syndrome - genetics
Metabolites
Metabolomics
MicroRNA
MicroRNAs
MicroRNAs - genetics
miRNA
Multiomics
Nutrition & metabolism
Obesity
Obesity - complications
Obesity - genetics
Prevention
Quality control
Risk factors
Software
Triglycerides
Vein & artery diseases
Womens health
United Arab Emirates
United States > US
Qatar
Online AccessGet full text
ISSN1479-5876
1479-5876
DOI10.1186/s12967-023-04074-x

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Abstract Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. Methods We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. Results We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. Conclusions The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
AbstractList To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways.OBJECTIVESTo examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways.We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications.METHODSWe analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications.We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome.RESULTSWe identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome.The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.CONCLUSIONSThe data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. Methods We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. Results We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. Conclusions The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
Abstract Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. Methods We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. Results We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. Conclusions The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
ObjectivesTo examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways.MethodsWe analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications.ResultsWe identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome.ConclusionsThe data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. Methods We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. Results We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. Conclusions The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.
ArticleNumber 229
Audience Academic
Author Mall, Raghvendra
Cyprian, Farhan
Abdalhakam, Ibrahem
Ullah, Ehsan
Alkasem, Meis
Taheri, Shahrad
Abou-Samra, Abdul-Badi
Samra, Tareq A.
Mir, Fayaz Ahmad
Farooq, Faisal
Iskandarani, Ahmad
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/36991398$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_37155_2972_449X_0102_7
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Cites_doi 10.1210/endrev/bnaa004
10.3390/ijms23179821
10.1038/s41598-017-19120-0
10.1038/nature03076
10.1161/CIRCULATIONAHA.116.021654
10.1172/JCI106989
10.1093/nar/gkv007
10.1037/h0071325
10.1172/JCI10761
10.1093/ajcn/nqz133
10.1038/s41598-019-43793-4
10.1212/NXG.0000000000000564
10.1001/archinte.167.7.642
10.1186/1471-2121-11-33
10.1001/archinte.168.15.1609
10.1016/j.ecl.2016.04.004
10.1210/jc.2003-030087
10.1093/bioinformatics/btq431
10.1093/nar/gkx1144
10.1038/nature05488
10.1128/MCB.02085-07
10.1111/obr.12198
10.1016/j.jacc.2017.07.763
10.1093/eurheartj/ehu123
10.1371/journal.pone.0017429
10.1038/s41574-021-00512-2
10.1042/bj1420327
10.2337/dc12-1654
10.1186/s12199-017-0642-7
10.1210/jc.2006-0594
10.1016/S2213-8587(18)30137-2
10.1186/s12967-022-03654-7
10.3390/metabo12111044
10.1093/bib/bbab168
10.1161/CIRCRESAHA.117.311002
10.1161/ATVBAHA.113.301714
10.3389/fendo.2022.937089
10.1016/j.jacc.2013.11.035
10.1111/dme.12646
10.1161/CIRCULATIONAHA.117.031139
10.1038/s41598-022-11970-7
10.1080/14786440109462720
10.1038/hr.2009.178
10.1007/s12035-016-0078-x
10.1530/EJE-15-0449
10.1093/ajcn/4.1.20
10.1093/bioinformatics/btg405
10.1186/s12872-020-01690-z
10.1016/j.ymgme.2020.07.010
10.1074/jbc.M116.723379
10.1186/s12967-016-0893-x
10.1186/1472-6823-14-9
10.1038/srep20032
10.1038/sj.ijo.0802711
10.1186/1471-2105-14-7
10.1016/j.cmet.2017.07.008
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References MN Li (4074_CR48) 2020; 20
K Pearson (4074_CR27) 1901; 2
J Leandro (4074_CR63) 2020; 131
F Patin (4074_CR64) 2017; 54
L Zhang (4074_CR47) 2018; 137
S Sherif (4074_CR35) 2022; 20
R Caleyachetty (4074_CR10) 2017; 70
S Hanzelmann (4074_CR33) 2013; 14
N Mononen (4074_CR41) 2019; 9
S Bellary (4074_CR54) 2021; 17
4074_CR34
4074_CR30
N Eckel (4074_CR14) 2018; 6
R Wei (4074_CR26) 2018; 8
GM Hinnouho (4074_CR12) 2015; 36
MN Poy (4074_CR44) 2004; 432
RH Eckel (4074_CR15) 2016; 133
PN Brandao-Lima (4074_CR37) 2022; 12
GF Svingen (4074_CR58) 2013; 33
M Bluher (4074_CR9) 2020; 41
LM Browning (4074_CR46) 2004; 28
JP Rey-Lopez (4074_CR6) 2014; 15
B Schegg (4074_CR51) 2009; 29
T McLaughlin (4074_CR4) 2007; 167
4074_CR29
JB Meigs (4074_CR5) 2006; 91
JV van Vliet-Ostaptchouk (4074_CR8) 2014; 14
F Xu (4074_CR62) 2010; 33
4074_CR25
GM Reaven (4074_CR43) 2003; 88
4074_CR28
4074_CR23
B Morkedal (4074_CR13) 2014; 63
4074_CR24
MK Cavaghan (4074_CR42) 2000; 106
4074_CR20
RW McGarrah (4074_CR40) 2018; 122
LF Gulyaeva (4074_CR39) 2016; 14
N Stefan (4074_CR18) 2008; 168
N Stefan (4074_CR19) 2017; 26
C Wang (4074_CR45) 2016; 6
J Vague (4074_CR17) 1956; 4
EA Shirdel (4074_CR31) 2011; 6
JM Liefhebber (4074_CR52) 2010; 11
4074_CR59
FA Mir (4074_CR32) 2022; 13
L Gautier (4074_CR21) 2004; 20
MWA Teunissen (4074_CR53) 2021; 7
JP Despres (4074_CR2) 2006; 444
M Hamer (4074_CR16) 2015; 173
S Hosseinkhani (4074_CR56) 2022; 12
BS Carvalho (4074_CR22) 2010; 26
GM Hinnouho (4074_CR11) 2013; 36
FA Mir (4074_CR38) 2022; 23
Y Heianza (4074_CR3) 2015; 32
NW Cornell (4074_CR60) 1974; 142
LL Zhang (4074_CR61) 2020; 24
F Magkos (4074_CR7) 2019; 110
I Birlouez-Aragon (4074_CR49) 1993; 123
TK Kyle (4074_CR1) 2016; 45
S Baumann (4074_CR50) 2016; 291
ME Ritchie (4074_CR36) 2015; 43
N Yamaguchi (4074_CR57) 2017; 22
J Wahren (4074_CR55) 1972; 51
References_xml – volume: 41
  start-page: 89
  year: 2020
  ident: 4074_CR9
  publication-title: Endocr Rev
  doi: 10.1210/endrev/bnaa004
– volume: 23
  start-page: 9
  year: 2022
  ident: 4074_CR38
  publication-title: Int J Mol Sci
  doi: 10.3390/ijms23179821
– volume: 8
  start-page: 663
  year: 2018
  ident: 4074_CR26
  publication-title: Sci Rep
  doi: 10.1038/s41598-017-19120-0
– volume: 432
  start-page: 226
  year: 2004
  ident: 4074_CR44
  publication-title: Nature
  doi: 10.1038/nature03076
– volume: 133
  start-page: 1051
  year: 2016
  ident: 4074_CR15
  publication-title: Circulation
  doi: 10.1161/CIRCULATIONAHA.116.021654
– volume: 51
  start-page: 1870
  year: 1972
  ident: 4074_CR55
  publication-title: J Clin Invest
  doi: 10.1172/JCI106989
– volume: 43
  year: 2015
  ident: 4074_CR36
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkv007
– ident: 4074_CR28
  doi: 10.1037/h0071325
– volume: 106
  start-page: 329
  year: 2000
  ident: 4074_CR42
  publication-title: J Clin Invest
  doi: 10.1172/JCI10761
– volume: 110
  start-page: 533
  year: 2019
  ident: 4074_CR7
  publication-title: Am J Clin Nutr
  doi: 10.1093/ajcn/nqz133
– ident: 4074_CR29
– volume: 9
  start-page: 8887
  year: 2019
  ident: 4074_CR41
  publication-title: Sci Rep
  doi: 10.1038/s41598-019-43793-4
– volume: 7
  year: 2021
  ident: 4074_CR53
  publication-title: Neurol Genet
  doi: 10.1212/NXG.0000000000000564
– ident: 4074_CR25
– volume: 167
  start-page: 642
  year: 2007
  ident: 4074_CR4
  publication-title: Arch Intern Med
  doi: 10.1001/archinte.167.7.642
– volume: 11
  start-page: 33
  year: 2010
  ident: 4074_CR52
  publication-title: BMC Cell Biol
  doi: 10.1186/1471-2121-11-33
– volume: 168
  start-page: 1609
  year: 2008
  ident: 4074_CR18
  publication-title: Arch Intern Med
  doi: 10.1001/archinte.168.15.1609
– volume: 45
  start-page: 511
  year: 2016
  ident: 4074_CR1
  publication-title: Endocrinol Metab Clin North Am
  doi: 10.1016/j.ecl.2016.04.004
– volume: 88
  start-page: 2399
  year: 2003
  ident: 4074_CR43
  publication-title: J Clin Endocrinol Metab
  doi: 10.1210/jc.2003-030087
– volume: 26
  start-page: 2363
  year: 2010
  ident: 4074_CR22
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btq431
– ident: 4074_CR59
– ident: 4074_CR30
  doi: 10.1093/nar/gkx1144
– volume: 444
  start-page: 881
  year: 2006
  ident: 4074_CR2
  publication-title: Nature
  doi: 10.1038/nature05488
– volume: 29
  start-page: 943
  year: 2009
  ident: 4074_CR51
  publication-title: Mol Cell Biol
  doi: 10.1128/MCB.02085-07
– volume: 15
  start-page: 781
  year: 2014
  ident: 4074_CR6
  publication-title: Obes Rev
  doi: 10.1111/obr.12198
– volume: 70
  start-page: 1429
  year: 2017
  ident: 4074_CR10
  publication-title: J Am Coll Cardiol
  doi: 10.1016/j.jacc.2017.07.763
– volume: 36
  start-page: 551
  year: 2015
  ident: 4074_CR12
  publication-title: Eur Heart J
  doi: 10.1093/eurheartj/ehu123
– ident: 4074_CR20
– volume: 6
  year: 2011
  ident: 4074_CR31
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0017429
– volume: 17
  start-page: 534
  year: 2021
  ident: 4074_CR54
  publication-title: Nat Rev Endocrinol
  doi: 10.1038/s41574-021-00512-2
– volume: 142
  start-page: 327
  year: 1974
  ident: 4074_CR60
  publication-title: Biochem J
  doi: 10.1042/bj1420327
– ident: 4074_CR24
– volume: 36
  start-page: 2294
  year: 2013
  ident: 4074_CR11
  publication-title: Diabetes Care
  doi: 10.2337/dc12-1654
– volume: 22
  start-page: 35
  year: 2017
  ident: 4074_CR57
  publication-title: Environ Health Prev Med
  doi: 10.1186/s12199-017-0642-7
– volume: 91
  start-page: 2906
  year: 2006
  ident: 4074_CR5
  publication-title: J Clin Endocrinol Metab
  doi: 10.1210/jc.2006-0594
– volume: 6
  start-page: 714
  year: 2018
  ident: 4074_CR14
  publication-title: Lancet Diabetes Endocrinol
  doi: 10.1016/S2213-8587(18)30137-2
– volume: 20
  start-page: 442
  year: 2022
  ident: 4074_CR35
  publication-title: J Transl Med
  doi: 10.1186/s12967-022-03654-7
– volume: 12
  start-page: 89
  year: 2022
  ident: 4074_CR37
  publication-title: Metabolites
  doi: 10.3390/metabo12111044
– ident: 4074_CR34
  doi: 10.1093/bib/bbab168
– volume: 122
  start-page: 1238
  year: 2018
  ident: 4074_CR40
  publication-title: Circ Res
  doi: 10.1161/CIRCRESAHA.117.311002
– volume: 33
  start-page: 2041
  year: 2013
  ident: 4074_CR58
  publication-title: Arterioscler Thromb Vasc Biol
  doi: 10.1161/ATVBAHA.113.301714
– volume: 13
  year: 2022
  ident: 4074_CR32
  publication-title: Front Endocrinol (Lausanne)
  doi: 10.3389/fendo.2022.937089
– ident: 4074_CR23
– volume: 63
  start-page: 1071
  year: 2014
  ident: 4074_CR13
  publication-title: J Am Coll Cardiol
  doi: 10.1016/j.jacc.2013.11.035
– volume: 32
  start-page: 665
  year: 2015
  ident: 4074_CR3
  publication-title: Diabet Med
  doi: 10.1111/dme.12646
– volume: 137
  start-page: 1374
  year: 2018
  ident: 4074_CR47
  publication-title: Circulation
  doi: 10.1161/CIRCULATIONAHA.117.031139
– volume: 12
  start-page: 8418
  year: 2022
  ident: 4074_CR56
  publication-title: Sci Rep
  doi: 10.1038/s41598-022-11970-7
– volume: 2
  start-page: 559
  year: 1901
  ident: 4074_CR27
  publication-title: The London Edinburgh Dublin Philosop Magazine J Sci
  doi: 10.1080/14786440109462720
– volume: 33
  start-page: 49
  year: 2010
  ident: 4074_CR62
  publication-title: Hypertens Res
  doi: 10.1038/hr.2009.178
– volume: 54
  start-page: 5361
  year: 2017
  ident: 4074_CR64
  publication-title: Mol Neurobiol
  doi: 10.1007/s12035-016-0078-x
– volume: 173
  start-page: 703
  year: 2015
  ident: 4074_CR16
  publication-title: Eur J Endocrinol
  doi: 10.1530/EJE-15-0449
– volume: 4
  start-page: 20
  year: 1956
  ident: 4074_CR17
  publication-title: Am J Clin Nutr
  doi: 10.1093/ajcn/4.1.20
– volume: 20
  start-page: 307
  year: 2004
  ident: 4074_CR21
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btg405
– volume: 20
  start-page: 404
  year: 2020
  ident: 4074_CR48
  publication-title: BMC Cardiovasc Disord
  doi: 10.1186/s12872-020-01690-z
– volume: 131
  start-page: 14
  year: 2020
  ident: 4074_CR63
  publication-title: Mol Genet Metab
  doi: 10.1016/j.ymgme.2020.07.010
– volume: 291
  start-page: 18514
  year: 2016
  ident: 4074_CR50
  publication-title: J Biol Chem
  doi: 10.1074/jbc.M116.723379
– volume: 14
  start-page: 143
  year: 2016
  ident: 4074_CR39
  publication-title: J Transl Med
  doi: 10.1186/s12967-016-0893-x
– volume: 14
  start-page: 9
  year: 2014
  ident: 4074_CR8
  publication-title: BMC Endocr Disord
  doi: 10.1186/1472-6823-14-9
– volume: 6
  start-page: 20032
  year: 2016
  ident: 4074_CR45
  publication-title: Sci Rep
  doi: 10.1038/srep20032
– volume: 123
  start-page: 1370
  year: 1993
  ident: 4074_CR49
  publication-title: J Nutr
– volume: 28
  start-page: 1004
  year: 2004
  ident: 4074_CR46
  publication-title: Int J Obes Relat Metab Disord
  doi: 10.1038/sj.ijo.0802711
– volume: 14
  start-page: 7
  year: 2013
  ident: 4074_CR33
  publication-title: BMC Bioinformatics
  doi: 10.1186/1471-2105-14-7
– volume: 24
  start-page: 12437
  year: 2020
  ident: 4074_CR61
  publication-title: Eur Rev Med Pharmacol Sci
– volume: 26
  start-page: 292
  year: 2017
  ident: 4074_CR19
  publication-title: Cell Metab
  doi: 10.1016/j.cmet.2017.07.008
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Snippet Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures...
To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and...
Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures...
ObjectivesTo examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures...
Abstract Objectives To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular...
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SubjectTerms Analysis
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Body fat
Body mass index
Cardiovascular disease
Case-Control Studies
Complications and side effects
Diabetes
Fasting
Genotype & phenotype
Humans
Ions
Laboratories
Mass spectroscopy
Medicine/Public Health
Metabolic diseases
Metabolic pathways
Metabolic syndrome
Metabolic Syndrome - complications
Metabolic Syndrome - genetics
Metabolites
Metabolomics
MicroRNA
MicroRNAs
MicroRNAs - genetics
miRNA
Multiomics
Nutrition & metabolism
Obesity
Obesity - complications
Obesity - genetics
Prevention
Quality control
Risk factors
Software
Triglycerides
Vein & artery diseases
Womens health
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Title An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study
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