Prenatal Metal Concentrations and Childhood Cardiometabolic Risk Using Bayesian Kernel Machine Regression to Assess Mixture and Interaction Effects.
Adiponectin
/ blood
Adipose Tissue
Adolescent
Adult
Bayes Theorem
Blood Pressure
Body Mass Index
Cardiovascular Diseases
/ epidemiology
Child
Child, Preschool
Cholesterol
/ blood
Female
Glycated Hemoglobin
/ analysis
Humans
Leptin
/ blood
Metals
/ blood
Mexico
/ epidemiology
Pregnancy
Pregnancy Trimester, Second
/ blood
Prospective Studies
Risk Factors
Triglycerides
/ blood
Young Adult
Journal
Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644
Informations de publication
Date de publication:
03 2019
03 2019
Historique:
entrez:
6
2
2019
pubmed:
6
2
2019
medline:
21
5
2019
Statut:
ppublish
Résumé
Trace metal concentrations may affect cardiometabolic risk, but the role of prenatal exposure is unclear. We examined (1) the relation between blood metal concentrations during pregnancy and child cardiometabolic risk factors; (2) overall effects of metals mixture (essential vs. nonessential); and (3) interactions between metals. We measured 11 metals in maternal second-trimester whole blood in a prospective birth cohort in Mexico City. In children 4-6 years old, we measured body mass index (BMI), percent body fat, and blood pressure (N = 609); and plasma hemoglobin A1C (HbA1c), non-high-density lipoprotein (HDL) cholesterol, triglycerides, leptin, and adiponectin (N = 411). We constructed cardiometabolic component scores using age- and sex-adjusted z scores and averaged five scores to create a global risk score. We estimated linear associations of each metal with individual z scores and used Bayesian Kernel Machine Regression to assess metal mixtures and interactions. Higher total metals were associated with lower HbA1c, leptin, and systolic blood pressure, and with higher adiponectin and non-HDL cholesterol. We observed no interactions between metals. Higher selenium was associated with lower triglycerides in linear (β = -1.01 z score units per 1 unit ln(Se), 95% CI = -1.84, -0.18) and Bayesian Kernel Machine Regression models. Manganese was associated with decreased HbA1c in linear models (β = -0.32 and 95% CI = -0.61, -0.03). Antimony and arsenic were associated with lower leptin in Bayesian Kernel Machine Regression models. Essential metals were more strongly associated with cardiometabolic risk than were nonessential metals. Low essential metals during pregnancy were associated with increased cardiometabolic risk factors in childhood.
Sections du résumé
BACKGROUND
Trace metal concentrations may affect cardiometabolic risk, but the role of prenatal exposure is unclear. We examined (1) the relation between blood metal concentrations during pregnancy and child cardiometabolic risk factors; (2) overall effects of metals mixture (essential vs. nonessential); and (3) interactions between metals.
METHODS
We measured 11 metals in maternal second-trimester whole blood in a prospective birth cohort in Mexico City. In children 4-6 years old, we measured body mass index (BMI), percent body fat, and blood pressure (N = 609); and plasma hemoglobin A1C (HbA1c), non-high-density lipoprotein (HDL) cholesterol, triglycerides, leptin, and adiponectin (N = 411). We constructed cardiometabolic component scores using age- and sex-adjusted z scores and averaged five scores to create a global risk score. We estimated linear associations of each metal with individual z scores and used Bayesian Kernel Machine Regression to assess metal mixtures and interactions.
RESULTS
Higher total metals were associated with lower HbA1c, leptin, and systolic blood pressure, and with higher adiponectin and non-HDL cholesterol. We observed no interactions between metals. Higher selenium was associated with lower triglycerides in linear (β = -1.01 z score units per 1 unit ln(Se), 95% CI = -1.84, -0.18) and Bayesian Kernel Machine Regression models. Manganese was associated with decreased HbA1c in linear models (β = -0.32 and 95% CI = -0.61, -0.03). Antimony and arsenic were associated with lower leptin in Bayesian Kernel Machine Regression models. Essential metals were more strongly associated with cardiometabolic risk than were nonessential metals.
CONCLUSIONS
Low essential metals during pregnancy were associated with increased cardiometabolic risk factors in childhood.
Identifiants
pubmed: 30720588
doi: 10.1097/EDE.0000000000000962
pii: 00001648-201903000-00016
pmc: PMC6402346
mid: NIHMS1008121
doi:
Substances chimiques
Adiponectin
0
Glycated Hemoglobin A
0
Leptin
0
Metals
0
Triglycerides
0
hemoglobin A1c protein, human
0
Cholesterol
97C5T2UQ7J
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
263-273Subventions
Organisme : NIEHS NIH HHS
ID : R01 ES026033
Pays : United States
Organisme : NIEHS NIH HHS
ID : K99 ES027508
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES021357
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES013744
Pays : United States
Organisme : NIEHS NIH HHS
ID : R24 ES028522
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES023515
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK092924
Pays : United States
Organisme : NIEHS NIH HHS
ID : R00 ES027508
Pays : United States
Organisme : NIEHS NIH HHS
ID : R00 ES023450
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES009089
Pays : United States
Références
Lancet. 2004 May 15;363(9421):1642-5
pubmed: 15145640
Environ Health Perspect. 2005 Feb;113(2):164-9
pubmed: 15687053
Pediatrics. 2006 Jul;118(1):201-6
pubmed: 16818566
Diabetologia. 2008 Jan;51(1):29-38
pubmed: 17851649
Environ Health Perspect. 2008 Mar;116(3):355-61
pubmed: 18335103
BMC Med Genet. 2008 Apr 21;9:30
pubmed: 18423055
Pediatrics. 2008 Jul;122(1):198-208
pubmed: 18596007
J Nutr. 2010 Mar;140(3):437-45
pubmed: 20071652
Atherosclerosis. 2010 Jun;210(2):643-8
pubmed: 20102763
Epidemiology. 2010 Jul;21 Suppl 4:S51-7
pubmed: 20220524
Int J Environ Res Public Health. 2010 Sep;7(9):3332-47
pubmed: 20948927
Annu Rev Public Health. 2011;32:237-62
pubmed: 21219171
Free Radic Biol Med. 2012 Apr 15;52(8):1335-42
pubmed: 22342560
Environ Health Perspect. 2012 Dec;120(12):1658-70
pubmed: 22889723
Am J Epidemiol. 2013 Jun 15;177(12):1356-67
pubmed: 23676282
Epigenomics. 2013 Jun;5(3):271-81
pubmed: 23750643
Diabetologia. 2014 May;57(5):940-9
pubmed: 24463933
BMC Endocr Disord. 2014 Mar 08;14:24
pubmed: 24606630
Curr Probl Pediatr Adolesc Health Care. 2014 Mar;44(3):54-72
pubmed: 24607261
Environ Health. 2014 Jun 10;13(1):50
pubmed: 24916609
Diabetes. 2014 Nov;63(11):3699-710
pubmed: 24947366
J Dev Orig Health Dis. 2014 Aug;5(4):281-7
pubmed: 24965134
Int J Womens Health. 2014 Jul 11;6:647-56
pubmed: 25050077
PLoS One. 2014 Aug 08;9(8):e104273
pubmed: 25105421
Ann Glob Health. 2014 Jul-Aug;80(4):269-77
pubmed: 25459328
Environ Res. 2015 Jan;136:27-34
pubmed: 25460617
Environ Res. 2015 Jan;136:180-6
pubmed: 25460635
Biostatistics. 2015 Jul;16(3):493-508
pubmed: 25532525
PLoS One. 2015 Apr 13;10(4):e0123742
pubmed: 25874871
Curr Diabetes Rev. 2016;12(3):252-8
pubmed: 26264451
Curr Opin Endocrinol Diabetes Obes. 2015 Oct;22(5):353-9
pubmed: 26313897
Diabetes. 2016 Jan;65(1):164-71
pubmed: 26542316
Nutrients. 2016 Feb 06;8(2):80
pubmed: 26861388
Environ Res. 2017 Jan;152:226-232
pubmed: 27810680
Environ Health Perspect. 2016 Dec 1;124(12):A227-A229
pubmed: 27905274
Nutrients. 2017 Feb 03;9(2):null
pubmed: 28165362
Pediatr Obes. 2018 May;13(5):292-300
pubmed: 28493362
N Engl J Med. 2017 Jul 6;377(1):13-27
pubmed: 28604169
Environ Health Perspect. 2017 Jun 26;125(6):067015
pubmed: 28669934
Pediatrics. 2017 Sep;140(3):null
pubmed: 28827377