An Approach to Early Detection of Metabolic Syndrome through Non-Invasive Methods in Obese Children.
anthropometry
child
early diagnosis
metabolic syndrome
obesity
Journal
Children (Basel, Switzerland)
ISSN: 2227-9067
Titre abrégé: Children (Basel)
Pays: Switzerland
ID NLM: 101648936
Informations de publication
Date de publication:
17 Dec 2020
17 Dec 2020
Historique:
received:
26
10
2020
revised:
06
12
2020
accepted:
15
12
2020
entrez:
22
12
2020
pubmed:
23
12
2020
medline:
23
12
2020
Statut:
epublish
Résumé
Metabolic Syndrome (MetS) has a high prevalence in children, and its presence increases in those with a high BMI. This fact confirms the need for early detection to avoid the development of other comorbidities. Non-invasive variables are presented as a cost-effective and easy to apply alternative in any clinical setting. To propose a non-invasive method for the early diagnosis of MetS in overweight and obese Chilean children. We conducted a cross-sectional study on 221 children aged 6 to 11 years. We carried out multivariate logistic regressions, receiver operating characteristic curves, and discriminant analysis to determine the predictive capacity of non-invasive variables. The proposed new method for early detection of MetS is based on clinical decision trees. The prevalence of MetS was 26.7%. The area under the curve for the BMI and waist circumference was 0.827 and 0.808, respectively. Two decision trees were calculated: the first included blood pressure (≥104.5/69 mmHg), BMI (≥23.5 Kg/m Early detection of MetS is possible through non-invasive methods in overweight and obese children. Two models (Clinical decision trees) based on anthropometric (non-invasive) variables with acceptable validity indexes have been presented. Clinical decision trees can be applied in different clinical and non-clinical settings, adapting to the tools available, being an economical and easy to measurement option. These methods reduce the use of blood tests to those patients who require confirmation.
Sections du résumé
BACKGROUND
BACKGROUND
Metabolic Syndrome (MetS) has a high prevalence in children, and its presence increases in those with a high BMI. This fact confirms the need for early detection to avoid the development of other comorbidities. Non-invasive variables are presented as a cost-effective and easy to apply alternative in any clinical setting.
AIM
OBJECTIVE
To propose a non-invasive method for the early diagnosis of MetS in overweight and obese Chilean children.
METHODS
METHODS
We conducted a cross-sectional study on 221 children aged 6 to 11 years. We carried out multivariate logistic regressions, receiver operating characteristic curves, and discriminant analysis to determine the predictive capacity of non-invasive variables. The proposed new method for early detection of MetS is based on clinical decision trees.
RESULTS
RESULTS
The prevalence of MetS was 26.7%. The area under the curve for the BMI and waist circumference was 0.827 and 0.808, respectively. Two decision trees were calculated: the first included blood pressure (≥104.5/69 mmHg), BMI (≥23.5 Kg/m
CONCLUSIONS
CONCLUSIONS
Early detection of MetS is possible through non-invasive methods in overweight and obese children. Two models (Clinical decision trees) based on anthropometric (non-invasive) variables with acceptable validity indexes have been presented. Clinical decision trees can be applied in different clinical and non-clinical settings, adapting to the tools available, being an economical and easy to measurement option. These methods reduce the use of blood tests to those patients who require confirmation.
Identifiants
pubmed: 33348633
pii: children7120304
doi: 10.3390/children7120304
pmc: PMC7767015
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : INNOVA CORFO
ID : 07CN131SM-19
Références
Lancet Child Adolesc Health. 2017 Oct;1(2):86-88
pubmed: 30169210
Arch Endocrinol Metab. 2019 Feb;63(1):30-39
pubmed: 30864629
J Pediatr Endocrinol Metab. 2018 Aug 28;31(8):847-854
pubmed: 29883323
J Paediatr Child Health. 2013 Apr;49(4):E281-7
pubmed: 23521181
Biomed Res Int. 2017;2017:8728017
pubmed: 29457038
Circulation. 2009 Oct 20;120(16):1640-5
pubmed: 19805654
Eur J Cardiovasc Nurs. 2016 Dec;15(7):549-558
pubmed: 26743264
J Pediatr. 2004 Oct;145(4):439-44
pubmed: 15480363
Front Endocrinol (Lausanne). 2019 Aug 16;10:568
pubmed: 31474943
Arch Pediatr Adolesc Med. 2003 Aug;157(8):821-7
pubmed: 12912790
Nutr Hosp. 2019 Mar 7;36(1):96-102
pubmed: 30834755
Transl Pediatr. 2017 Oct;6(4):397-407
pubmed: 29184820
BMC Med. 2011 May 05;9:48
pubmed: 21542944
Nutr Hosp. 2013 Nov 01;28(6):1999-2005
pubmed: 24506380
Rev Med Chil. 2011 Jun;139(6):732-8
pubmed: 22051753
Rev Med Chil. 2015 Sep;143(9):1136-43
pubmed: 26530196
Int J Obes (Lond). 2015 Jul;39(7):1070-8
pubmed: 25869598
Pediatr Obes. 2018 Jul;13(7):421-432
pubmed: 29700992
J Am Heart Assoc. 2017 Aug 16;6(8):
pubmed: 28862940
Rev Med Chil. 2018 Sep;146(9):978-986
pubmed: 30725017
Diabetol Metab Syndr. 2018 Sep 29;10:72
pubmed: 30288175
Metab Syndr Relat Disord. 2013 Apr;11(2):71-80
pubmed: 23249214
Visc Med. 2016 Oct;32(5):357-362
pubmed: 27921049
Heart Asia. 2011 Jan 01;3(1):2-7
pubmed: 27325971
PLoS One. 2018 Mar 22;13(3):e0194490
pubmed: 29566051
Int J Cardiol. 2018 May 15;259:216-219
pubmed: 29472026
Cardiovasc Diabetol. 2016 Oct 28;15(1):149
pubmed: 27793156
Pediatr Int. 2010 Jun;52(3):402-9
pubmed: 19807877
Eur J Clin Invest. 2019 Mar;49(3):e13060
pubmed: 30549264
Eur Heart J. 2018 Sep 1;39(33):3021-3104
pubmed: 30165516
Prev Chronic Dis. 2017 Oct 12;14:E93
pubmed: 29023232
BMJ Open. 2018 Oct 21;8(10):e020476
pubmed: 30344164
Nutr Metab Cardiovasc Dis. 2019 Nov;29(11):1189-1196
pubmed: 31378631
J Pediatr Endocrinol Metab. 2019 Jan 28;32(1):49-55
pubmed: 30530900
Diabetes Care. 2019 Jan;42(Suppl 1):S13-S28
pubmed: 30559228
BMC Pediatr. 2018 Feb 7;18(1):33
pubmed: 29415673
JAMA. 2016 Jun 7;315(21):2292-9
pubmed: 27272581
Obes Rev. 2016 Dec;17(12):1258-1275
pubmed: 27452904
Int J Obes (Lond). 2014 Sep;38 Suppl 2:S4-14
pubmed: 25376220
Metab Syndr Relat Disord. 2019 May;17(4):210-216
pubmed: 30741590