Targeted metabolomics to understand the association between arsenic metabolism and diabetes-related outcomes: Preliminary evidence from the Strong Heart Family Study.


Journal

Environmental research
ISSN: 1096-0953
Titre abrégé: Environ Res
Pays: Netherlands
ID NLM: 0147621

Informations de publication

Date de publication:
01 2019
Historique:
received: 06 06 2018
revised: 02 09 2018
accepted: 25 09 2018
pubmed: 14 10 2018
medline: 1 10 2019
entrez: 14 10 2018
Statut: ppublish

Résumé

Inorganic arsenic exposure is ubiquitous and both exposure and inter-individual differences in its metabolism have been associated with cardiometabolic risk. A more efficient arsenic metabolism profile (lower MMA%, higher DMA%) has been associated with reduced risk for arsenic-related health outcomes. This profile, however, has also been associated with increased risk for diabetes-related outcomes. The mechanism behind these conflicting associations is unclear; we hypothesized the one-carbon metabolism (OCM) pathway may play a role. We evaluated the influence of OCM on the relationship between arsenic metabolism and diabetes-related outcomes (HOMA2-IR, waist circumference, fasting plasma glucose) using metabolomic data from an OCM-specific and P180 metabolite panel measured in plasma, arsenic metabolism measured in urine, and HOMA2-IR and FPG measured in fasting plasma. Samples were drawn from baseline visits (2001-2003) in 59 participants from the Strong Heart Family Study, a family-based cohort study of American Indians aged ≥14 years from Arizona, Oklahoma, and North/South Dakota. In unadjusted analyses, a 5% increase in DMA% was associated with higher HOMA2-IR (geometric mean ratio (GMR)= 1.13 (95% CI: 1.03, 1.25)) and waist circumference (mean difference=3.66 (0.95, 6.38). MMA% was significantly associated with lower HOMA2-IR and waist circumference. After adjustment for OCM-related metabolites (SAM, SAH, cysteine, glutamate, lysophosphatidylcholine 18.2, and three phosphatidlycholines), associations were attenuated and no longer significant. These preliminary results indicate that the association of lower MMA% and higher DMA% with diabetes-related outcomes may be influenced by OCM status, either through confounding, reverse causality, or mediation.

Sections du résumé

BACKGROUND
Inorganic arsenic exposure is ubiquitous and both exposure and inter-individual differences in its metabolism have been associated with cardiometabolic risk. A more efficient arsenic metabolism profile (lower MMA%, higher DMA%) has been associated with reduced risk for arsenic-related health outcomes. This profile, however, has also been associated with increased risk for diabetes-related outcomes.
OBJECTIVES
The mechanism behind these conflicting associations is unclear; we hypothesized the one-carbon metabolism (OCM) pathway may play a role.
METHODS
We evaluated the influence of OCM on the relationship between arsenic metabolism and diabetes-related outcomes (HOMA2-IR, waist circumference, fasting plasma glucose) using metabolomic data from an OCM-specific and P180 metabolite panel measured in plasma, arsenic metabolism measured in urine, and HOMA2-IR and FPG measured in fasting plasma. Samples were drawn from baseline visits (2001-2003) in 59 participants from the Strong Heart Family Study, a family-based cohort study of American Indians aged ≥14 years from Arizona, Oklahoma, and North/South Dakota.
RESULTS
In unadjusted analyses, a 5% increase in DMA% was associated with higher HOMA2-IR (geometric mean ratio (GMR)= 1.13 (95% CI: 1.03, 1.25)) and waist circumference (mean difference=3.66 (0.95, 6.38). MMA% was significantly associated with lower HOMA2-IR and waist circumference. After adjustment for OCM-related metabolites (SAM, SAH, cysteine, glutamate, lysophosphatidylcholine 18.2, and three phosphatidlycholines), associations were attenuated and no longer significant.
CONCLUSIONS
These preliminary results indicate that the association of lower MMA% and higher DMA% with diabetes-related outcomes may be influenced by OCM status, either through confounding, reverse causality, or mediation.

Identifiants

pubmed: 30316100
pii: S0013-9351(18)30520-6
doi: 10.1016/j.envres.2018.09.034
pmc: PMC6298442
mid: NIHMS1509386
pii:
doi:

Substances chimiques

Arsenic N712M78A8G

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

146-157

Subventions

Organisme : NHLBI NIH HHS
ID : U01 HL041642
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL041654
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK091369
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES025216
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL090863
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL041652
Pays : United States
Organisme : NIEHS NIH HHS
ID : P42 ES010349
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL109319
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL109284
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL065521
Pays : United States
Organisme : NIEHS NIH HHS
ID : T32 ES007141
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL109282
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES009089
Pays : United States
Organisme : NIEHS NIH HHS
ID : F31 ES027796
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL109315
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK107532
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL109301
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL065520
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES021367
Pays : United States

Informations de copyright

Copyright © 2018 Elsevier Inc. All rights reserved.

Auteurs

Miranda J Spratlen (MJ)

Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA; Department of Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: mjs2376@cumc.columbia.edu.

Maria Grau-Perez (M)

Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA; Fundación Investigación Clínico de Valencia-INCLIVA, Area of Cardiometabolic and Renal Risk, Valencia, Valencia, Spain; University of Valencia, Department of Statistics and Operational Research, Valencia, Valencia, Spain.

Jason G Umans (JG)

MedStar Health Research Institute, Hyattsville, MD, USA; Department of Medicine, Georgetown University School of Medicine, Washington, DC, USA.

Joseph Yracheta (J)

Missouri Breaks Industries Research, Inc., Eagle Butte, SD, USA.

Lyle G Best (LG)

Missouri Breaks Industries Research, Inc., Eagle Butte, SD, USA.

Kevin Francesconi (K)

Institute of Chemistry - Analytical Chemistry, University of Graz, Austria.

Walter Goessler (W)

Institute of Chemistry - Analytical Chemistry, University of Graz, Austria.

Teodoro Bottiglieri (T)

Baylor Scott & White Research Institute, Dallas, TX, USA.

Mary V Gamble (MV)

Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.

Shelley A Cole (SA)

Texas Biomedical Research Institute, San Antonio, TX, USA.

Jinying Zhao (J)

College of Public Health and Health Professions and the College of Medicine at the University of Florida, Gainesville, FL, USA.

Ana Navas-Acien (A)

Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA; Department of Environmental Health & Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: an2737@cumc.columbia.edu.

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Classifications MeSH