Association of Fish Consumption and Mercury Exposure During Pregnancy With Metabolic Health and Inflammatory Biomarkers in Children

Key Points Question Is fish consumption during pregnancy associated with benefits for the metabolic health of children? Findings In this cohort study of 805 mothers and their singleton offspring, moderate fish consumption during pregnancy was associated with the downregulation of inflammation and improvements in the metabolic profile of children; high mercury exposure during pregnancy had the opposite associations. Meaning The results of this study suggest that fish consumption consistent with current recommendations during pregnancy was associated with improvements in the metabolic health of children.


eMethods 1. Measurement of Mercury Levels During Pregnancy
Maternal blood samples were collected in mid-pregnancy for MoBa (18.7 (0.9) weeks) and RHEA (14.1 (3.7) weeks), in late pregnancy for BiB (26.6 (1.4) weeks) and EDEN (26.1 (1.2) weeks), while, for INMA, we collected cord blood samples at delivery. The HELIX project measured whole blood mercury concentration in MoBa, RHEA, BiB, and EDEN cohorts in the same laboratory (ALS Scandinavia, Sweden) using biological samples stored in the cohort biobanks and utilized existing cord blood mercury measurements for INMA performed in the Public Health Laboratory in Alava (Spain). Mercury concentration in whole blood samples was determined using inductively coupled plasma mass spectrometry. Mercury in cord blood was measured using thermal decomposition, amalgamation and atomic absorption spectrometry.
Details of the analytical procedures used for the HELIX project have been described previously. 1

eMethods 2. Measurement of Inflammatory Biomarkers and Adipokines in Childhood
Blood samples were randomized and blocked by cohort prior to measurement to ensure a representation of each cohort in each plate (batch). For protein quantification, an 8point calibration curve per plate was performed with protein standards provided in the Luminex kit and following the procedures described in the standard procedures described by the vendor. Commercial heat inactivated, sterile-filtered plasma from human male AB plasma (Sigma Cat #. H3667) was used as constant controls to control for intra-and inter-plate variability. Four control samples were added per plate. Raw intensities obtained with the xMAP and Luminex system for each sample were converted to pg/mL using the calculated standard curves of each plate and accounting for the dilutions that were made prior measurement. The proportion of coefficients of variation (% CV) for each protein, estimated by plate and then averaged, ranged from 3.42% to 36%. For each protein, the LOD was determined and the lower and upper quantification limits (LOQ1 and LOQ2, respectively) were obtained from the calibration curves. Proteins were not used if 30% of samples were outside of the linear range of quantification. Protein data were log2-transformed to achieve normal distribution. Plate batch effect was then corrected by subtracting for each individual and each protein the difference between the overall protein average minus the plate specific protein average.
Finally, values below LOQ1 and above LOQ2 were imputed using a truncated normal distribution using the truncdist R package.  and parity (primiparous, multiparous) was obtained through interviews and medical records. Information on parental education (cohort-specific definition of low, middle, high), breastfeeding (yes, no), child ethnicity (Caucasian, Asian, Other; motherreported), child sedentary behavior (in minutes/week), and child consumption of fastfood, sugar-sweetened beverages, sweets, and fish intake (in times/week) was collected with interviews or self-administered questionnaires. Moreover, we assessed for estimation with the E-step using the conditional probabilities, and M-step using the expected log-likelihood from the E-step maximized with respect to the parameters. 2 Although this can include the outcome in a supervised clustering approach, in this analysis, we define the clusters from the protein profiles and exposure variables only and then test the association of these clusters to MetS score. For the estimation of the number of latent clusters, we use Bayesian Information Criteria. Abbreviations: MetS, metabolic syndrome. a The MetS score is expressed in standard deviations and was derived using z-scores for waist circumference, systolic and diastolic blood pressure, high-density lipoprotein cholesterol, triglycerides, and insulin, as described in the Methods section. Estimates are beta coefficients (95% CI) calculated by a linear regression model mutually including maternal fish intake and mercury levels and further adjusted for cohort, maternal age, maternal pre-pregnancy BMI, maternal education (for models not stratified by this variable), paternal education, parity, and child ethnicity. b P for interaction calculated by including a cross-product term between the potential effect modifier and maternal fish intake or mercury levels. Abbreviations: MetS, metabolic syndrome a The MetS score is expressed in standard deviations and was derived using z-scores for waist circumference, systolic and diastolic blood pressure, high-density lipoprotein cholesterol, triglycerides, and insulin, as described in the Methods section.

Type of Validation
Estimates are beta coefficients (95% CI) calculated by a linear regression model mutually including maternal fish intake and mercury levels and further adjusted for maternal plasma levels of polychlorinated biphenyls (the sum of 118, 138, 153, 170, and 180 congeners), dichlorodiphenyldichloroethylene and arsenic, maternal age, maternal pre-pregnancy BMI, parental education, parity, child ethnicity, and cohort. Mean variance inflation factor was 2.9, with all variance inflation factors for the predictors in the model being <10. The metabolic syndrome score is expressed in standard deviations and was derived using z-scores for waist circumference, systolic and diastolic blood pressure, high-density lipoprotein cholesterol, triglycerides, and insulin, as described in the Methods section. Effect estimates represent beta coefficients (circles) and 95% CIs (error bars) calculated by linear regression models mutually including maternal fish intake and mercury concentration and further adjusted for maternal age, maternal pre-pregnancy BMI, parental education, parity, child ethnicity, and cohort. For fish intake, the reference category is less than 1 time/week. BiB, Born in Bradford cohort; EDEN, the Étude des Déterminants pré et postnatals du développement et de la santé de l'Enfant study; INMA, INfancia y Medio Ambiente cohort; MoBa, Norwegian Mother, Father and Child Cohort Study; RHEA, Rhea Mother Child Cohort study.