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

Highlights

• One carbon metabolism, arsenic and diabetes appear to be interconnected

• Arsenic metabolism was associated with diabetes outcomes

• Adjustment for one carbon metabolism metabolites removed this association

• Glutamate and phosphatidylcholines may be of particular importance

• Findings need confirmation in larger prospective cohorts

Abstract

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.

Introduction

Inorganic arsenic is a known human carcinogen and chronic exposure has been associated with increased risk for numerous health outcomes including metabolic effects, such as type 2 diabetes and the metabolic syndrome (Moon et al., 2012; Naujokas et al., 2013; Kuo et al., 2015; Tyler and Allan 2014; Wu et al., 2014; Council 2001). After ingestion, inorganic arsenic is metabolized through multiple oxidative methylation and reduction reactions ultimately converting inorganic arsenic (As^III and As^V) (iAs) to the methylated metabolites monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA), which are excreted in the urine together with inorganic arsenic (Chen et al., 2011; Hall et al., 2009; Hall and Gamble 2012). Typically, arsenic metabolism is evaluated in epidemiological studies by computing relative percentages of inorganic arsenic, MMA, and DMA over their sum (iAs%, MMA%, DMA%) (Vahter, 2000; Hernandez and Marcos 2008; Steinmaus et al., 2005; Huang et al., 2009).

Inter-individual differences in methylation capacity have been associated with risk for subsequent health outcomes. Several studies have reported higher percentages of MMA (MMA%) and lower percentages of DMA (DMA%) in the urine to be associated with greater risk for many arsenic-induced health effects including skin lesions (Ahsan et al., 2007; Chen et al., 2009; Valenzuela et al., 2009), cancers of the skin, bladder, lung (Steinmaus et al., 2006; Agusa et al., 2010; Chen et al., 2003; Yu et al., 2000; Huang et al., 2008; Steinmaus et al., 2010) and cardiovascular disease, (Huang et al., 2007, Huang et al., 2009, Chen et al., 2013) even after controlling for arsenic exposure levels. However, for metabolic-related health outcomes, including diabetes and metabolic syndrome, higher MMA% and lower DMA% is associated with a lower risk (Kuo et al., 2015; Nizam et al., 2013; Mendez et al., 2016; Del Razo et al., 2011). The reasons for the contrasting association between arsenic metabolism and metabolic outcomes versus other arsenic-associated health outcomes are not clear. Some studies have suggested that one-carbon metabolism (OCM) may play a role given its strong association with both metabolic outcomes (Mahabir et al., 2008; Braun et al., 2015; Bird et al., 2015; Bian et al., 2013; Mazidi et al., 2017; Roe et al., 2017; Gunanti et al., 2014) and arsenic metabolism (Hall et al., 2009; Hall and Gamble 2012; Spratlen et al., 2017; Heck et al., 2007; Hall et al., 2007; Gamble et al., 2006; Gamble et al., 2005; Gamble et al., 2005; Peters et al., 2015). OCM, a biochemical pathway that is dependent on folate, facilitates the generation of S-adenosylmethionine (SAM), a central metabolite, which serves as the methyl donor for numerous methylation reactions including the methylation of arsenic (Ralph Carmel, 2001).

In this study, we explored potential mechanistic pathways that may explain the associations between arsenic metabolism and metabolic outcomes through the targeted evaluation of specific metabolites. The association between arsenic metabolism and metabolic profiles has been relatively unexplored. However, metabolic profiling has already identified metabolites, including those related to OCM, that can predict risk for diabetes beyond traditional diabetes risk factors. (Zhao et al., 2015; Rhee et al., 2011; Wang et al., 2011; Drogan et al., 2015; Floegel et al., 2013). Our analyses expanded on these findings by evaluating associations between OCM- and diabetes-associated metabolites, arsenic metabolism biomarkers, and diabetes-related outcomes including waist circumference, fasting plasma glucose and HOMA2-IR. Our selection of these outcomes was informed by previous work conducted in our study population that have reported associations between arsenic metabolism and diabetes (Kuo et al., 2015), a homeostasis model assessment index (HOMA2-IR), (Grau-Perez et al., 2017) body mass index (BMI) (Gribble et al., 2013), waist circumference (Spratlen et al., 2018) and metabolic syndrome (Spratlen et al., 2018); all of these outcomes are linked to insulin resistance. Previous evaluation of the association between arsenic metabolism and other metabolic outcomes in this study population, including triglycerides and high density lipoprotein cholesterol, yielded null results and were therefore not evaluated in this analysis.

Using data from the Strong Heart Family Study (SHFS), a family-based cohort study comprised of American Indian tribal members aged 14 years and older from Arizona, Oklahoma, and North and South Dakota, we conducted a pilot study including 59 participants. We analyzed nine OCM-specific metabolites in addition to the Biocrates P180 metabolite panel (188 endogenous metabolites including amino acids, lipids, and carbohydrates, many of them related to OCM) in baseline plasma samples collected in 2001–2003. For our analyses, of the 188 metabolites, we a priori selected only metabolites that are involved in OCM or have been prospectively associated with incident diabetes in previous studies (n=33 metabolites) (Supplemental Table 1) (Zhao et al., 2015; Rhee et al., 2011; Wang et al., 2011; Drogan et al., 2015; Floegel et al., 2013; Wang-Sattler et al., 2012; Wang et al., 2013; Walford et al., 2016; Menni et al., 2013; Peddinti et al., 2017; Ferrannini et al., 2013). Given the small sample size, our analyses are considered exploratory and aim to describe the relationship of OCM-specific metabolites and previously identified incident diabetes-associated metabolites with arsenic metabolism and metabolic outcomes including fasting glucose levels, insulin resistance and waist circumference.