Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic.
Journal
International journal of obesity (2005)
ISSN: 1476-5497
Titre abrégé: Int J Obes (Lond)
Pays: England
ID NLM: 101256108
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
26
08
2022
accepted:
13
02
2023
revised:
07
02
2023
medline:
29
5
2023
pubmed:
25
2
2023
entrez:
24
2
2023
Statut:
ppublish
Résumé
This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile. Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.
Sections du résumé
BACKGROUND/OBJECTIVE
This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools.
SUBJECTS/METHODS
Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression.
RESULTS
The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile.
CONCLUSIONS
Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.
Identifiants
pubmed: 36823293
doi: 10.1038/s41366-023-01281-w
pii: 10.1038/s41366-023-01281-w
pmc: PMC10212764
doi:
Substances chimiques
Cholesterol, LDL
0
Cholesterol
97C5T2UQ7J
Insulin
0
Types de publication
Observational Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
453-462Informations de copyright
© 2023. The Author(s).
Références
Diabetologia. 2017 Jul;60(7):1234-1243
pubmed: 28439641
BMC Med. 2019 Nov 29;17(1):217
pubmed: 31779625
Diabetes. 2022 Aug 1;71(8):1818-1826
pubmed: 35622003
Nucleic Acids Res. 2018 Nov 16;46(20):10546-10562
pubmed: 30295871
Analyst. 2009 Sep;134(9):1781-5
pubmed: 19684899
PLoS Genet. 2008 Nov;4(11):e1000282
pubmed: 19043545
Nat Med. 2021 Jan;27(1):49-57
pubmed: 33398163
Mol Metab. 2021 Oct;52:101304
pubmed: 34274528
J Am Heart Assoc. 2019 Nov 5;8(21):e013479
pubmed: 31630587
PLoS Med. 2011 Jun;8(6):e1000440
pubmed: 21695075
J Clin Endocrinol Metab. 2023 Jan 20;:
pubmed: 36658689
J Am Heart Assoc. 2018 Oct 2;7(19):e010049
pubmed: 30371337
J Pediatr. 2018 Apr;195:190-198.e3
pubmed: 29397160
Diabetes Care. 2011 Jan;34(1):210-5
pubmed: 20937689
BMJ Open. 2017 Aug 21;7(8):e015001
pubmed: 28827236
Diabetes. 2008 Sep;57(9):2480-7
pubmed: 18544706
Lancet Healthy Longev. 2022 May;3(5):e312-e313
pubmed: 36098305
Proc Natl Acad Sci U S A. 2016 Jun 21;113(25):E3470
pubmed: 27303027
Am J Epidemiol. 2017 Nov 1;186(9):1084-1096
pubmed: 29106475
PLoS Med. 2014 Dec 09;11(12):e1001765
pubmed: 25490400
BMC Med. 2018 Feb 6;16(1):17
pubmed: 29402284
Sci Rep. 2022 May 21;12(1):8590
pubmed: 35597771
Cell. 2015 Mar 26;161(1):161-172
pubmed: 25815993
Paediatr Perinat Epidemiol. 1988 Jan;2(1):59-88
pubmed: 2976931
J Intern Med. 2013 Apr;273(4):383-95
pubmed: 23279644
N Engl J Med. 2022 May 19;386(20):1877-1888
pubmed: 35373933
Am J Physiol Regul Integr Comp Physiol. 2011 Sep;301(3):R581-600
pubmed: 21677272
J Proteome Res. 2012 Mar 2;11(3):1782-90
pubmed: 22204613
Diabetes Care. 2020 Jul;43(7):1487-1495
pubmed: 32321731
Am J Physiol. 1998 Feb;274(2):R412-9
pubmed: 9486299
Am J Cardiovasc Drugs. 2011 Aug 1;11(4):227-47
pubmed: 21675801
Int J Epidemiol. 1985 Mar;14(1):32-8
pubmed: 3872850
Cell Metab. 2019 Feb 5;29(2):488-500.e2
pubmed: 30318341
J Am Coll Cardiol. 2016 Dec 27;68(25):2850-2870
pubmed: 28007146
Int J Epidemiol. 2018 Jun 1;47(3):696-696i
pubmed: 29165699
Int J Epidemiol. 2019 Apr 1;48(2):369-374
pubmed: 29947762
Front Endocrinol (Lausanne). 2020 May 14;11:252
pubmed: 32477261
J Clin Endocrinol Metab. 2019 Mar 1;104(3):738-752
pubmed: 30339231
Lancet Diabetes Endocrinol. 2018 May;6(5):361-369
pubmed: 29503172
Aging Cell. 2020 Jan;19(1):e13073
pubmed: 31746094
Eur Heart J. 2017 Aug 21;38(32):2459-2472
pubmed: 28444290
BMC Public Health. 2017 Aug 29;17(1):683
pubmed: 28851330
Mol Syst Biol. 2008;4:167
pubmed: 18277383
World Rev Nutr Diet. 2013;106:127-34
pubmed: 23428691
Nat Commun. 2019 Aug 20;10(1):3346
pubmed: 31431621
Health Psychol. 2010 May;29(3):237-45
pubmed: 20496976
Int J Epidemiol. 2022 Dec 13;51(6):1970-1983
pubmed: 35441226
J Clin Invest. 2019 Oct 1;129(10):3978-3989
pubmed: 31524630
Nat Commun. 2016 Mar 23;7:11122
pubmed: 27005778
Int J Epidemiol. 2008 Dec;37(6):1220-6
pubmed: 18263651
Diabetes Care. 2022 May 1;45(5):1260-1267
pubmed: 35287165
Circulation. 2013 Jan 22;127(3):340-8
pubmed: 23258601