Abstract

Background

   Limited studies have evaluated the joint influence of redox-related
   metals and genetic variation on metabolic pathways. We analyzed the
   association of 11 metals with metabolic patterns, and the interacting
   role of candidate genetic variants, in 1145 participants from the
   Hortega Study, a population-based sample from Spain.

Methods

   Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr),
   cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu),
   selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS,
   respectively. We summarized 54 plasma metabolites, measured with
   targeted NMR, by estimating metabolic principal components (mPC).
   Redox-related SNPs (N = 291) were measured by oligo-ligation assay.

Results

   In our study, the association with metabolic principal component (mPC)
   1 (reflecting non-essential and essential amino acids, including
   branched chain, and bacterial co-metabolism versus fatty acids and VLDL
   subclasses) was positive for Se and Zn, but inverse for Cu,
   arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2
   (reflecting essential amino acids, including aromatic, and bacterial
   co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3
   (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse
   for Co. The association for mPC4 (reflecting HDL subclasses) was
   positive for Sb, but inverse for plasma Zn. These associations were
   mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and
   Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were
   NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of
   interactions with genetic variants associated to enriched endocrine,
   cardiovascular and neurological pathways.

Conclusions

   Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several
   metabolic patterns involved in chronic disease. Carriers of
   redox-related variants may have differential susceptibility to
   metabolic alterations associated to excessive exposure to metals.

   Keywords: Metals, Metabolomics, Oxidative stress, Candidate genes,
   Gene-environment interaction

   Abbreviations: AAA, aromatic amino acids; AAS, atomic absorption
   spectrometry; AsB, arsenobetaine; BCAA, branched-chain amino acids;
   BKMR, Bayesian Kernel Machine Regression; CVD, cardiovascular disease;
   HDL, high-density lipoprotein; HWE, Hardy Weinberg equilibrium; ICPMS,
   inductively coupled-plasma mass spectrometry; LDL, low-density
   lipoprotein; LOD, limit of detection; MAF, minor allele frequency; mPC,
   metabolic principal component; NMR, Nuclear Magnetic Resonance; PCA,
   principal component analysis; ROS, reactive oxygen species; SNP, single
   nucleotide polymorphisms; SOD, superoxide dismutase; VLDL, very
   low-density lipoprotein

Graphical abstract

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Highlights

     * •
       In a population-based sample, cobalt, copper, selenium, zinc,
       arsenic, cadmium and antimony exposures were related to some
       metabolic patterns.
     * •
       Carriers of redox-related variants displayed differential
       susceptibility to metabolic alterations associated to excessive
       metal exposures.
     * •
       Cobalt and zinc showed a number of statistical interactions with
       variants from genes sharing biological pathways with a role in
       chronic diseases.
     * •
       The metabolic impact of metals combined with variation in
       redox-related genes might be large in the population, given metals
       widespread exposure.

1. Introduction

   Mounting evidence supports that exposure to trace elements -mostly
   metals and metalloids, hereinafter referred to as “metals” - can
   substantially influence human health [[59][1], [60][2], [61][3],
   [62][4], [63][5]]. In the human body, essential metals, such as copper
   (Cu), selenium (Se) and zinc (Zn), are key enzymatic components playing
   a fundamental role on several cellular processes, including redox
   balance maintenance [[64]6,[65]7]. Essential metals are also needed for
   human microbiota metabolic activities because they have a role as
   cofactors in bacterial enzymatic pathways [[66]8]. While low
   concentrations of essential metals can be damaging for the optimal
   function of human and bacterial metabolism [[67]9], excessive
   concentrations may have not only adverse metabolic outcomes, but also
   induce alteration of enzymes involved in structural functions
   [[68]10,[69]11]. In addition, non-essential metals, such as cadmium
   (Cd) or arsenic, with no physiological function, but well-established
   toxic effects, can act as competitors for enzymatic binding sites and
   interfere with several metabolic processes [[70]7]. For instance,
   divalent toxic metals bind to sulfhydryl groups, which not only
   counteracts the antioxidant properties of glutathione and
   metallothioneins [[71]12], but also interferes with glucose capture
   into cells [[72]7], signal transduction pathways [[73]6], and the
   one-carbon and citric acid metabolism [[74]13].

   Some studies have evaluated individual metals, mainly essential, in
   relation to specific metabolites subclasses, especially lipoproteins
   and fatty acids [[75][14], [76][15], [77][16], [78][17], [79][18]], but
   also inflammation markers and products of microbiota and microbiota
   composition [[80][19], [81][20], [82][21], [83][22], [84][23]].
   However, limited epidemiologic studies have considered the joint
   influence of metal biomarkers on an extended panel of metabolites. A
   small pilot study in the Strong Heart Study participants (N = 145)
   found correlations between several urinary metals (including molybdenum
   [Mo], Se, Zn, inorganic arsenic and antimony [Sb]) and amino acids,
   fatty acids and lipid metabolism [[85]24]. Larger epidemiologic studies
   are needed to confirm these findings.

   The main objective of the current analysis was to evaluate the
   cross-sectional association of essential (urine cobalt [Co] and Mo, and
   plasma Cu, Se and Zn) and non-essential (urine barium [Ba], Cd,
   chromium [Cr], Sb, vanadium [V] and arsenic corrected for arsenobetaine
   [As]) metal exposure biomarkers with NMR-measured plasma metabolites
   (including amino acids, fatty acids, fluid balance, energy, bacterial
   co-metabolism, and lipoprotein subclasses) in the Hortega Study, a
   population-based sample of a general population from Spain. Omics
   technologies can give an expanded view of metabolites unbalance
   potentially exerted by metals. In this way, metabolomics coupled to
   advanced statistical methods, can provide a holistic view of how
   biological pathways are inter-related, and help to understand the
   overall impact of metals in cellular metabolism and health. In this
   study, we summarized correlated metabolites using variable-reduction
   methods based on principal components (PC). We subsequently evaluated
   the individual and joint influences of metals on metabolic profiles
   using traditional and Bayesian Kernel Machine Regression (BKMR)
   methods, which allow a flexible view of the highly dimensional
   non-linear inter-relationships among metals and the metabolic patterns
   represented by the estimated metabolic PCs (mPCs).

   Finally, since altered redox metabolism has been postulated to be one
   of the main mechanisms for the detrimental health effects of metals
   [[86]6,[87]7,[88][25], [89][26], [90][27]] -indeed several metals were
   associated to oxidative stress biomarkers in our study population
   [[91]26,[92]28]-, we also explored candidate gene-environment
   interactions (i.e. whether carriers of genetic variants in
   redox-related genes show a differential association of metals with
   metabolic patterns) and conducted subsequent biological pathway
   analysis of genes annotated to relevant interacting genetic variants.

2. Materials and methods

2.1. Study population

   The Hortega Cohort is a representative sample from beneficiaries of the
   Hospital Universitario Rio Hortega's catchment area. The examination
   phase was conducted in 2001–2003 resulting in 1502 adults recruited
   with ages between 15 and 85 years. The sampling scheme and methodology
   have been previously reported [[93]29]. We excluded 299 participants
   missing metabolites, 55 participants missing metals, and 3 participants
   missing other variables of interest. A total of 1145 participants were
   included in the final analyses.

   The Ethics Committee of the Hospital Universitario Rio Hortega approved
   the research protocol, and every participant provided informed consent.

2.2. Metals measures

   Urine and plasma samples were collected at the 2001–2003 examination
   visit and kept frozen at <80° in the Hortega Study biorepository. In
   2010, plasma Cu, Se and Zn, which are considered as biomarkers of
   essential metal status, were evaluated by atomic absorption
   spectrometry (AAS) with graphite furnace at Cerba international
   Laboratories Ltd. The limit of detection (LOD) (and corresponding
   coefficient variation [CV]) was 0.63 μg/dL (7.2%) for Cu, 29.9 μg/L
   (5.6%) for Se and 0.65 μg/dL (4.2%) for Zn; no individual had levels
   below the LOD for plasma metals. For plasma determinations, Scharlau
   Standard Solutions were used as reference material for accuracy. In
   2013, total urine metals (arsenic, Ba, Cd, Cr, Co, Mo, Sb and V), which
   are considered as biomarkers of short-term exposure to most
   non-essential metals, and arsenobetaine (AsB), which was needed to
   distinguish organic arsenic from seafood, were measured using
   inductively coupled-plasma mass spectrometry (ICP-MS) and anion
   exchange high performance liquid chromatography (HPLC) coupled to
   ICP-MS, respectively, on a 7500 CE spectrometer with octapole reaction
   cell (Agilent Technologies, Tokyo, Japan). For urine metals, the LOD
   (and corresponding CV) were 0.024 μg/L (6.5%) for total arsenic,
   0.0005 μg/L (1.9%) for Ba, 0.005 μg/L (5.2%) for Cd, 0.038 μg/L (4.3%)
   for Cr, 0.001 μg/L (3.0%) for Co, 0.01 μg/L (1.8%) for Mo, 0.003 μg/L
   (5.3%) for Sb and 0.008 μg/L (3.6%) for V. The percentage of
   individuals below the LOD was 0.07% for Cd, 0.14% for Co and 1.81% for
   Sb. For other urine metals there were no individuals with undetectable
   values. The corresponding LOD (CV) for urine AsB was 0.056 μg/L (9.7%),
   leaving 4.7% of participants with undetectable AsB values. ClinCheck
   Urine Control for AsB and for total trace elements at Level I and II
   (RECIPE) were used for accuracy. Reference materials for all metal
   determinations were traceable to the corresponding National Institute
   of Standards and Technology references. Quality control was