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Research article

Urinary arsenic species and birth outcomes in Tacna, Peru, 2019: a prospective cohort study

Authors
  • Diego Fano-Sizgorich orcid logo (Universidad Peruana Cayetano Heredia)
  • Matthew O. Gribble orcid logo (University of California, San Francisco)
  • Cinthya Vásquez-Velásquez orcid logo (Universidad Peruana Cayetano Heredia)
  • Claudio Ramírez-Atencio orcid logo (Universidad Nacional Jorge Basadre Grohmann)
  • Julio Aguilar orcid logo (Universidad Nacional Jorge Basadre Grohmann)
  • Jeffrey K. Wickliffe orcid logo (University of Alabama at Birmingham)
  • Maureen Y. Lichtveld orcid logo (University of Pittsburgh)
  • Dana B. Barr orcid logo (Emory University)
  • Gustavo F. Gonzales orcid logo (Universidad Peruana Cayetano Heredia)

Abstract

Arsenic exposure during pregnancy might affect foetal development. Arsenic metabolism may modulate the potential damage to the fetus. Tacna has the highest arsenic exposure levels in Peru. However, this region also has the highest birth weight in Peru. It is not known if arsenic exposure is affecting maternal–perinatal health in Tacna. This study aimed to evaluate the association between urinary arsenic metabolism and birth outcomes, specifically birth weight and gestational age at birth in Tacna, Peru. A prospective cohort study was conducted, involving 158 pregnant women in Tacna, Peru, during January–November 2019. Participants were enrolled in their second trimester and followed-up until birth. Urine samples were collected in the second and third trimesters. Urine samples were analysed for total arsenic concentration and its species. Generalised estimating equations analysis was used to evaluate the association of interest. Inter-differences in arsenic toxicokinetics, calculated with principal component analysis was included as an interaction term. Analysis was stratified by pregnancy trimester. The median total urinary arsenic concentration was 33.34 μg/L. Inorganic arsenic and dimethylarsinic acid were higher in the second trimester. Dimethylarsinic acid was the predominant component (84.78% of total urinary arsenic). No significant association was found between urinary arsenic exposure and birth weight or gestational age at birth. The association was not affected by arsenic metabolism. Stratified analyses by pregnancy trimester also showed no significant associations. Urinary arsenic was not associated with birth weight, and this null relationship remained unaffected by arsenic toxicokinetic differences reflected in urine.

Keywords: birth weight, foetal development, gestational age, toxicity, pregnant women, Latin America

How to Cite:

Fano-Sizgorich, D., Gribble, M. O., Vásquez-Velásquez, C., Ramírez-Atencio, C., Aguilar, J., Wickliffe, J. K., Lichtveld, M. Y., Barr, D. B. & Gonzales, G. F., (2024) “Urinary arsenic species and birth outcomes in Tacna, Peru, 2019: a prospective cohort study”, UCL Open Environment 6(1). doi: https://doi.org/10.14324/111.444/ucloe.3146

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Funding

  • Fogarty International Center (grant D43 TW011502)
  • Fogarty International Center (FIC) (grant 5U01TW010107)
  • Fogarty International Center (FIC) (grant 5U2RTW010114)
  • National Institute of Environmental Health Sciences (NIEHS) (grant P30 ES019776)

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Published on
06 Dec 2024
Peer Reviewed
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Creative Commons Attribution 4.0

Introduction

Arsenic is a naturally occurring element found in the Earth’s crust, soil, water and air. It is a toxic substance and a known carcinogen, causing skin, lung, bladder and kidney cancers [1]. Arsenic is also known to have adverse effects on foetal and infant health [2]. Pregnant women who are exposed to high levels of arsenic are at an increased risk of adverse birth outcomes, including stillbirth, preterm birth (<37 weeks of gestational age), low birth weight (<2500 g at term) and congenital abnormalities [3]. In recent years, there has been growing concern about the impact of arsenic exposure on maternal and child health.

The ingestion of water containing high concentration of arsenic is one of the most common routes of exposure. It is estimated that 107 countries around the world are affected by high levels of arsenic in water [4], with groundwater being the most common source, although high levels are also found in surface water [5]. Arsenic concentration in water can be very heterogeneous even in a same country, such as Bangladesh, with arsenic levels ranging from 90 to 4730 μg/L in tube-well water [6]. In Chile, at Bahía de Camarones, which is located near the city of Arica (border with Peru), drinking water inorganic arsenic (iAs) levels of 48.7–1252 μg/L have been found, composed particularly of arsenate (AsV) [7]. A study from our group has determined that around two-thirds of the Tacna (a province in southern Peru) population of pregnant women are exposed to iAs levels higher than 10 μg/L in tap water, of which 50% were exposed to >50 μ/L [8]. However, Tacna, despite the arsenic exposure context, has shown the highest birth weight in Peru [9,10], as well as the lowest small-for-gestational age prevalence [10].

Urinary arsenic and its metabolites are commonly used as biomarkers of arsenic exposure in epidemiological studies [11]. Arsenic and its metabolites are excreted primarily in urine, and urinary arsenic levels have been shown to correlate with the internal dose of arsenic exposure [11]. Several studies have reported a significant association between maternal urinary arsenic levels and adverse birth outcomes, although the findings have been inconsistent across studies [3,12]. It is important to note that individuals have varying proficiencies in metabolising arsenic, and this could modulate the potential damage to the fetus [13].

Given the potential health risks associated with arsenic exposure during pregnancy, there is a need for further research to better understand the impact of arsenic on maternal and child health. This study aims to evaluate the association between urinary arsenic metabolism and birth outcomes, specifically birth weight and gestational age at birth.

Materials and methods

Study design and study area

We conducted a longitudinal cohort study during January–November 2019, in which a total of 158 pregnant women living in the province of Tacna, in their second trimester of pregnancy who attended their antenatal care-controls were enrolled and followed-up until birth. The province of Tacna is in southern Peru, with a total area of 8170 km2, and it is characterised for its desertic geography.

Enrolment of participants and follow-up

The recruitment of the pregnant women is described elsewhere [8]. In brief, a total of 16 health establishments within the five most populated districts in the province of Tacna were selected for the enrolment to take place. We were granted authorisation to consult the prenatal health care record, which included information about the date of last antenatal care consultation, gestational age by the time of consultation, age, address and telephone number.

To be considered as a potential participant for the study, the women were 18–40 years old, had lived in Tacna for at least 5 years, and were pregnant for <24 weeks by the time of the recruitment. Eligible women were recruited via a telephone call. Those invited to participate in the study were then visited in their homes or in the health establishment a total of two times for urine sampling. A final visit was scheduled after birth, in which data from their baby was collected, such as birth weight and gestational age at birth.

Urine sampling and arsenic quantification

One urine sample was taken in the second and third trimester of pregnancy. During the recruitment the women were given two sterile plastic flasks for urine specimen collection. They were asked to avoid consuming fish or seafood for the last three days prior the sampling. They were instructed in how to do the self-collection of the sample, indicating that they should eliminate the first few millilitres of the morning void. Once the sample was collected, participants were asked to store it in the freezer until the research personnel were able to collect them. The samples were transported at 4°C to the laboratory for storage. Samples were homogenised and then aliquoted in cryovials of 2 mL, and stored at −20°C. For arsenic quantification and speciation, the samples were delivered on dry ice to the LEADER laboratory at Emory University in Atlanta, GA, USA. The procedure is described elsewhere [14].

Statistical analysis

Descriptive statistics were used to display median with interquartile range for non-normal distributed data. Categorical variables are presented as absolute and relative frequencies. Arsenic species concentrations and their relative per cent (%) are presented.

Relative percent of the species were calculated as follows:

%iAs=[AsIII]+[AsV][AsIII]+[AsV]+[MMA]+[DMA],

%MMA=[MMA][AsIII]+[AsV]+[MMA]+[DMA],

%DMA=[DMA][AsIII]+[AsV]+[MMA]+[DMA],

where

[iAs]: inorganic arsenic concentration in urine

[AsIII]: arsenate concentration in urine

[AsV]: arsenate concentration in urine

[MMA]: monomethylarsonic acid concentration in urine

[DMA]: dimethylarsinic acid concentration in urine

To compare total urinary arsenic (tAs) and arsenic species concentration between the second and third trimester of pregnancy, we used Wilcoxon’s signed-rank test. We used Student’s t-test for paired observations to compare if %iAs, %MMA and %DMA were different between pregnancy trimesters, after the normal distribution evaluation of the differences. We performed a principal component analysis (PCA) to characterise the main sources of variability in the urinary arsenic data and its species (arsenic toxicokinetics differences between pregnant women). The PCA was conducted on the concentration of urinary iAs, MMA and DMA. The principal components correlations and eigenvectors can be found in Fig. A1.

Arsenic exposure was considered as the residuals of the following model to remove the influence of organic arsenic from seafood on urinary total arsenic [15,16]:

tAs=β1∗Asb+β2∗Asb2+constant,

where

tAs: total urinary arsenic (μg/L)

Asb: arsenobetaine (μg/L)

Generalised estimating equations (GEE) with Gaussian family analysis was employed to evaluate the association between arsenic and birth weight, and whether this association was affected by arsenic toxicokinetic differences between pregnant women. This same approach was applied to examine the association with gestational age at birth, but as coefficients were small, the variable arsenic exposure was scaled by dividing it by 1000 for better interpretation. GEE analysis was then stratified by newborn sex. An analysis stratified by pregnancy trimester was performed using linear regression. Regression models were adjusted for mother’s age, pregestational body mass index (BMI) and mother’s education level (as a proxy for socioeconomic status). All statistical analyses were conducted using STATA 17.0 software with a significance level of p < 0.05.

Results

The study sample of pregnant women had a mean age of 28.15 years at the time of recruitment, and mean BMI of 26.73 kg/m2 before pregnancy. Only five women (3.13%) declared to be smokers during pregnancy and 13 consumed alcohol (8.16%). Thirty-six of the women (22.50) were single mothers, and the sample had a high proportion of women with higher education (38.13%). In Table 1 we present the distribution of urinary arsenic species concentrations as median and interquartile range (IQR). Median total tAs was 33.34 μg/L and ranged between 2.50 and 167.48 μg/L. We observed variation in tAs across visits, being lower in visit 2. DMA was the most present arsenic component (84.78%). Water arsenic concentration distribution in the second and third trimester can be found in Table A1, indicating that for the third trimester, pregnant women were mostly exposed to levels ≤10 μg/L (51.83% vs. 29.56% in the second trimester), and there was a positive significant correlation between water arsenic and urinary DMA concentration in both trimesters.

Table 1.

Urinary arsenic species concentration and relative content across pregnancy

Arsenic species (μg/L) Total Second trimester Third trimester p-valuea
Median IQR Median IQR Median IQR
tAs 33.34 30.58 41.57 33.95 28.32 20.67 <0.001
AsIII 1.57 1.57 2.08 1.90 1.24 1.03 <0.001
AsV 1.36 1.30 1.36 1.36 1.36 1.21 0.553
iAs 2.99 2.80 3.54 2.99 2.68 2.03 0.001
MMA 2.10 1.79 2.17 2.07 2.07 1.35 0.165
DMA 28.36 26.86 35.55 29.06 23.36 16.75 <0.001
Asb 2.37 2.55 2.64 3.05 2.09 2.24 0.002
%iAsb 8.85 2.72 8.30 2.59 9.49 2.73 <0.001
%MMAb 6.37 2.21 5.41 1.87 7.47 2.06 <0.001
%DMAb 84.78 4.05 86.28 3.56 83.03 3.89 <0.001

  • aWilcoxon’s signed-rank for total arsenic and arsenic species concentration; and paired Student’s t-test for arsenic species relative content (%).

  • bValues are shown as mean and standard deviation.

Mean birth weight was 3618 ± 477.38 g. As seen in Table 2, there was no significant association between urinary arsenic and birth weight [adjusted β = 0.16, 95% confidence interval (CI) −1.07; 1.39, p = 0.800]. The interaction between urinary arsenic and arsenic toxicokinetics difference between women (PCA Score 1) showed a reduction in birth weight; nonetheless, this was non-significant (adjusted β = −0.05, 95% CI −0.76; 0.65, p = 0.882).

Table 2.

Association between urinary arsenic and interaction with arsenic metabolism with birth weight

Variable Unadjusted 95% CI Adjusted 95% CI
Urinary arsenic 0.04 −1.27; 1.36 0.16 −1.07; 1.39
Score 1a 0.62 −16.09; 17.33 1.27 −14.11; 16.65
Urinary arsenica Score 1 −0.10 −0.89; 0.69 −0.05 −0.76; 0.65
Mother’s age 4.40 −7.81; 16.60 3.63 −7.97; 15.23
Pregestational BMI 23.76 9.59; 37.92 20.65 6.94; 34.35
Education
 Elementary Ref. Ref.
 Secondary 305.65 −1.68; 612.97 371.28 72.67; 669.93
 Tertiary 212.99 −101.37; 527.36 312.73 8.70; 616.77

  • Residuals were calculated from the model tAs~β1 (Asb) + β2 (Asb)2.

  • Models were adjusted for mother’s age, mother’s education level, pregestational BMI.

  • aScore 1 (arsenic toxicokinetics difference between women), obtained from principal components analysis, is higher when %DMA is lower, meaning a reduced metabolic capability.

  • Bold letters indicate a p < 0.05.

Regarding gestational age at birth, as seen in Table 3, we found a non-significant increase of 0.02 weeks (95% CI −2.37; 2.40, p = 0.989), while the interaction term presented a decrease, although not significant, in gestational age at birth (β = −0.17, 95% CI −1.53; 1.19, p = 0.802).

Table 3.

Association between urinary arsenic and interaction with arsenic metabolism with gestational age at birth

Variable Adjusted 95% CI Adjusted 95% CI
Urinary arsenic −0.08 −2.48; 2.32 0.02 −2.37; 2.40
Score 1a 0.01 −0.02; 0.04 0.01 −0.02; 0.04
Urinary arsenica Score 1 −0.19 −1.63; 1.24 −0.17 −1.53; 1.19
Mother’s age −0.03 −0.06; −0.004 −0.03 −0.06; −0.001
Pregestational BMI −0.04 −0.07; −0.01 −0.03 −0.07; 0.003
Education
 Elementary Ref. Ref.
 Secondary 0.62 −0.12; 1.36 0.48 −0.27; 1.23
 Tertiary 0.55 −0.21; 1.31 0.32 −0.45; 1.08

  • Residuals were calculated from the model tAs~β1 (Asb) + β2 (Asb)2.

  • Models were adjusted for mother’s age, mother’s education level, pregestational BMI.

  • aScore 1 (arsenic toxicokinetics difference between women), obtained from principal components analysis, is higher when %DMA is lower, meaning a reduced metabolic capability.

  • Bold letters indicate a p < 0.05.

In the stratified analysis by newborn sex, no significant association was found between arsenic exposure or the interaction term related to arsenic toxicokinetic differences and birth weight. However, for gestational age at birth, a significant association (p = 0.041) was observed for males, indicating that each increase of 1000 units in urinary arsenic exposure is associated with an increase of 7.36 weeks in gestational age at birth (Table 4).

Table 4.

Association between urinary arsenic and interaction with arsenic metabolism with birth weight and gestational age at birth stratified by newborn sex

Newborn sex Regression term Birth weight Gestational age at birth
Adjusted p-value Adjusted p-value
Male Urinary arsenic 2.79 (−0.02; 5.60) 0.052 7.36 (0.30; 14.42) 0.041
Urinary arsenica Score 1a 0.36 (−1.41; 2.13) 0.689 −2.79 (−8.84; 3.26) 0.364
Female Urinary arsenic −0.47 (−4.27; 3.32) 0.806 −6.92 (−16.62; 2.77) 0.160
Urinary arsenica Score 1 −1.41 (−3.79; 0.98) 0.245 0.95 (−4.83; 6.72) 0.745

  • Regressions were adjusted for mother’s age, pregestational BMI and education.

  • Coefficients for gestational age at birth are scaled (urinary arsenic/1000).

  • aPCA Score 1 (arsenic toxicokinetics difference between women) is higher when %DMA is lower, meaning a reduced metabolic capability.

  • For both models, the adjusted regression coefficient (95% CI) is shown.

We then evaluated if arsenic or the interaction term with arsenic toxicokinetic differences were associated with both outcomes, stratifying it by pregnancy trimester. As seen in Table 5, there was no association between urinary arsenic exposure and the interaction term with birth weight and gestational age at birth.

Table 5.

Association between urinary arsenic and interaction with arsenic metabolism with birth weight and gestational age at birth stratified by visit

Trimester Regression term Birth weight Gestational age at birth
Adjusted p-value Adjusted p-value
Second Urinary arsenic 1.61 (−1.44; 4.67) 0.298 −5.11 (−14.43; 4.20) 0.280
Urinary arsenica Score 1a −1.36 (−3.32; 0.59) 0.170 −5.13 (−12.00; 1.75) 0.142
Third Urinary arsenic −1.91 (−6.09; 2.27) 0.368 7.88 (−5.81; 21.57) 0.257
Urinary arsenica Score 1 1.60 (−0.84; 4.05) 0.197 −0.81 (−8.19; 6.57) 0.828

  • Regressions were adjusted for mother’s age, pregestational BMI and education.

  • Coefficients for gestational age at birth are scaled (urinary arsenic/1000).

  • aPCA Score 1 (arsenic toxicokinetics difference between women) is higher when %DMA is lower, meaning a reduced metabolic capability.

  • For both models, the adjusted regression coefficient (95% CI) is shown.

Discussion

The present study aimed to evaluate the association between urinary arsenic and metabolism with birth weight and gestational age at birth. No association with these outcomes was found, and this null relationship is unaffected by arsenic toxicokinetic differences reflected in urine.

No association may have been found because exposure levels might not be high enough to exert an effect. Previous studies have found a decrease in birth weight with increasing levels of urinary arsenic, at exposure levels ≥100 μg/L.3 In this study, the median level of urinary arsenic for the cohort across pregnancy was 33.34 μg/L with a range of 2.50–167.48 μg/L. A total of 25 and 36 women showed urinary tAs levels ≥100 μg/L in the second and third trimester of pregnancy, respectively, but no difference in birth weight was found (Table A2). In some previous studies, low levels of arsenic in urine (1.8–27.7 μg/L) have not been found to be associated with a decrease in birth weight [17]. However, other studies with similar exposure levels in urine have found a significant association with birth weight or estimated foetal weight [18,19]. A Wuhan cohort study that showed median urinary arsenic levels of 31.22 μg/L for the first, 25.23 μg/L for the second and 24.98 μg/L for the third trimester found a significant decrease of 24.27 g in birth weight only for the third trimester [12]. This suggests that even low exposure levels might be harmful for foetal development. Additionally, it is important to remark that no arsenic exposure level is considered to be safe as even water arsenic exposure levels between 1 and 10 μg/L have been associated with increased cardiovascular mortality compared to concentrations <1 μg/L [20].

In a cohort study from Bangladesh, it was found that water and toenail arsenic association with birth weight was mediated by gestational age [21,22]. In the present study, pregnancy duration, seen as gestational age at birth, was not associated with arsenic exposure. This difference might be attributed to the level of arsenic exposure in drinking water observed in the Bangladeshi cohort. Although the median arsenic concentration was 2.3 μg/L at the time of enrolment, 33.3% of pregnant women were exposed to levels ranging from 18.4 to 1400 μg/L [21]. On the one hand, it has been found that low arsenic levels in biological samples such as umbilical cord (3.82 ± 3.81 μg/L) and whole blood (4.13 ± 3.21 μg/L) were associated with a decrease in gestational age by 0.342 weeks [23]. On the other hand, in a study that included a total of 212 mother–infant pairs, no association was found between total urinary arsenic (median 7.77 μg/L) and urinary DMA (3.44 μg/L) with gestational age [24]. The lack of association with birth weight and gestational age at birth could be due to an exposure below harmful levels, or to unmeasured nutritional, genetical and other factors.

When analysing the impact of arsenic exposure on birth outcomes by newborn sex, we found no significant relationship between arsenic levels and birth weight. However, for male infants, there was a notable increase in gestational age – specifically, an increase of 0.0746 weeks for every 10 units rise in urinary arsenic concentration. In contrast, a previous study involving 113 mother–child pairs reported no significant associations between arsenic exposure and gestational age across both sexes [25]. This discrepancy may stem from different exposure levels, particularly if Tacna has higher arsenic concentrations. Despite the modest effect size observed in our study, it remains unclear why urinary arsenic correlates positively with gestational age.

Arsenic can be metabolised, and a higher arsenic methylation capability of the body can reduce this metalloid toxicity [26]. Higher concentration of urinary MMA and urinary iAs are shown to have the biggest impact in decreasing birth weight and birth length, respectively [13]; evidence is less clear for DMA. Nonetheless, a higher proportion of DMA, which means a better arsenic metabolism, is associated with better health outcomes compared to those with lower DMA, such as general health status of children [27] and neurodevelopment in low birth weight preterm children [28]. We have observed in pregnant women from Tacna, Peru that DMA at 84.78% (tAs minus Asb) represents the main arsenic component present in urine. This may explain the low negative impact of arsenic on birthweight and gestational age at birth; and suggests that the difference in arsenic toxicokinetics might modify the association.

The effect modification of arsenic toxicokinetics was also assessed in the study by including the interaction term of arsenic with the PCA Score 1. For both birth weight and gestational age at birth, differences in arsenic metabolism seemed to modify the association by reducing these outcomes, although it was non-significant. Despite not finding an association, there might be an interaction between arsenic exposure and metabolism, as suggested in a Romanian longitudinal pilot study, where women who had low birth weight children showed a higher percentage of iAs and MMA [29], suggesting a slower or reduced metabolism.

Consideration of arsenic species and speciation is essential for a better understanding of exposure, not only in research studies but also in nationwide screenings such as the one done in the National Health and Nutrition Examination Survey (NHANES) [30,31]. Currently, the Peruvian Demographic and Health Survey does not consider water or urinary arsenic evaluation.

It is possible that birth weight was not affected due to the variation in arsenic exposure between pregnancy trimesters. Other studies showed that there are seasonal variations in water and urinary arsenic concentration [32–34], although depending on the area, the change can be very small (3.3 μg/L in well water between the dry and rainy season) [35]. The first study visit was conducted in the summer and autumn, while the second visit occurred during the winter and spring. At the second visit, median tAs was 28.32 μg/L, compared with 41.57 μg/L found in the first study visit. In the stratified analysis, no association was found with arsenic exposure, nor with toxicokinetic differences.

The fetus experiences the fastest weight gain during the third trimester [36], and different arsenic exposures in this developmental window have been found to reduce birth weight [37], although some authors have found that early pregnancy arsenic exposure might be the critical window for birth weight and other pregnancy outcomes [38] Nonetheless, trimester-based analysis might not reflect an adequate association [39]. Daily exposure assessment is difficult for exposures that need biological samples such as urinary arsenic. Arsenic has been found to be associated with a decrease in birth weight and gestational age at birth, possibly through lowering the thyroid hormones ratio during early pregnancy [18]. Seasonal variation in exposure, along with the analysis of pregnancy-relevant hormones, should be considered for a better evaluation and interpretation.

It is notable that pregnant women from Tacna, despite living in the highest arsenic-exposed region in Peru, have one of the highest mean birth weights [10]. One contributing factor may be the considerable proportion of individuals from the Aymara ethnicity in Tacna [8,14]. This is an indigenous group, predominantly located in high-altitude settings, that is known for higher birth weight compared to other high-altitude populations [40]. In our sample, neonates of pregnant women who self-identified as Aymara had a mean birth weight of 3711 g, higher compared to the other ethnic groups (3536 g for mestizo and 3466 g for Quechua) (Table A3). These findings suggests that the Aymara population may possess genetic traits that support foetal weight gain, even in the context of arsenic exposure.

When considering arsenic metabolism, polymorphisms in the AS3MT gene-related increased arsenic metabolic capability [41–44], were found in Aymara populations of Argentina [45]. However, while 55.41% of our sample self-identified as Aymara, %DMA was not different between the ethnic groups in our study (Table A4). These hypotheses should be explored in further studies.

The study has some limitations. There were unmeasured confounders such as the consumption of folates, which are part of the one-carbon metabolism and methyl donors for arsenic metabolism, which could modify the association between arsenic metabolism and birth weight [46]. Based on the Peruvian national programme on pregnancy, it is mandatory to supplement pregnant women with folic acid; therefore, the folate deficiency in our population is reduced; however, folate intake should be considered in further studies. Covariates such as gestational weight gain should also be evaluated as it is strongly associated with birth weight, especially during the first half of gestation [47]. The exposure assessment at the beginning of pregnancy (first trimester) is encouraged, as it would also allow testing of the effects of arsenic on placenta formation, as has been suggested in both human [48] and animal studies [49]. This would also allow for a better evaluation of seasonal variation in arsenic exposure. This study used specific gravity to adjust arsenic concentration in urine, which may have different sources of measurement error than, for instance, creatinine adjustment [50].

Conclusions

Arsenic was not associated with birth weight or gestational age at birth in this study, and this null relationship was unaffected by arsenic toxicokinetic differences reflected in the analysis. This should not be interpreted as if the Tacna population is protected against arsenic toxicity. Further studies should include other variables to better understand this phenomenon and the mechanism(s) behind it, including the evaluation of other clinical outcomes. Additionally, the inclusion of arsenic exposure assessment and its speciation in national programmes should be encouraged for better monitoring, along with the elimination of arsenic contamination in drinking water.

Acknowledgements

The authors thank Edwin Obando, Luis Lloja, Virginia Fernández, Alonso Plata, Diana Lloja, Sujey Gómez, Paul Valeriano and Red de Salud Tacna for their help in the study.

Authorship contribution

Diego Fano-Sizgorich: conceptualisation, methodology, investigation, formal analysis, data curation, writing-original draft; Matthew O. Gribble: conceptualisation, methodology, formal analysis, writing – review and editing, visualisation, supervision Cinthya Vásquez-Velásquez: investigation, writing – review and editing: Claudio Ramírez-Atencio: conceptualisation, resources, writing – review and editing; Julio Aguilar: conceptualisation, resources, writing – review and editing; Jeffrey K. Wickliffe: conceptualisation, writing – review and editing, supervision; Maureen Y. Lichtveld: conceptualisation, writing – review and editing, supervision; Dana B. Barr; validation, investigation, resources, writing – review and editing; Gustavo F. Gonzales: conceptualisation, resources, writing – review and editing, visualisation, supervision, project administration, funding acquisition.

Open data and materials availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations and conflicts of interest

Research ethics statement

The authors declare that research ethics approval for this article was provided by the Universidad Peruana Cayetano Heredia IRB (R-29420-20) ethics board. Project identification code R-121-12-23. The study was conducted in accordance with the Declaration of Helsinki.

Consent for publication statement

The authors declare that research participants’ informed consent to publication of findings – including photos, videos and any personal or identifiable information – was secured prior to publication.

Conflicts of interest statement

Dr. Gribble is a current Editor for this journal. All authors declare no conflicts of interest with this work.

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Appendix

Figure A1
Figure A1

Arsenic principal component analysis, correlation and eigenvectors.

Table A1.

Water arsenic concentrations distribution of pregnant women in the second and third trimester, and its correlation with urinary DMA

Water arsenic level category (μg/L)a Second trimester Third trimester
No. pregnant women % DMA correlationb No. pregnant women % DMA correlationb
5 15 9.43 0.345** 24 17.52 0.279*
10 32 20.13 47 34.31
25 55 34.59 39 28.47
50 33 20.75 19 13.87
100 22 13.84 5 3.65
250 2 1.26 3 2.19

  • aWater arsenic concentrations were obtained by analysing household drinking water samples, using a semi-quantitative method described in [8].

  • bSpearman’s correlation analysis (Spearman’s rho).

  • *p < 0.01, **p < 0.001.

Table A2.

Mean birth weight comparison between women with total urinary arsenic exposure levels ≥100 μg/L and <100 μg/L by trimester of pregnancy

Trimester tAs exposure (μg/L) No. participants Birth weight p-value*
Mean SD
Second <100 122 3623.82 489.99 0.749
≥100 25 3589.79 412.38
Third <100 91 3622.06 430.73 0.865
≥100 36 3606.32 631.28

  • SD: Standard deviation.

  • *p-value for Student’s t-test.

Table A3.

Mean birth weight according to the mother’s self-reported ethnic group, and one-way analysis of variance (ANOVA) analysis

Ethnic group (n) Birth weight p-value*
Mean SD
Mestizo (52) 3536.06 486 0.037
Quechua (18) 3466.11 391.47
Aymara (87) 3711.38 480.2

  • The group size for each ethnic group is displayed in parenthesis.

  • *p-value for one-way ANOVA test.

Table A4.

Percentage of dimethylarsinic acid (%DMA) in different ethnic groups

Ethnic group (n) %DMA p-value*
Mean SD
Mestizo (43) 84.88 4.10 0.463
Quechua (14) 84.99 4.23
Aymara (65) 84.66 4.02

  • The group size for each ethnic group is displayed in parenthesis.

  • *p-value for one-way ANOVA test.

 Open peer review from Dan Osborn

Review
I have looked at this paper on arsenic and outcomes for newborns. The paper informs the discussion of where the no-effect level for arsenic sits. In various parts of the world with high arsenic exposure, arsenic has substantial negative impacts on birth outcomes and subsequent health issues. In other places arsenic exposure is lower and outcomes are less severely affected or not affected at all. The paper tries to disentangle some of the complexities involved and although there are no overall effects there are a number of statistically significant findings that are worth noting.

The authors have responded to my earlier review by modifying the text and have also addressed at least some of the points raised by other reviewers. They have also adjusted and explained statistical approaches and extended these as requested - importantly to look at boy-girl differences.

I feel some of the points raised by the group of reviewers taken as a whole would require a quite different paper to be prepared: one that would constitute more of a review of the no-effect literature as well as consideration of the specifics of this study. Such a review would be valuable, especially if it included consideration of the wide range of statistical approaches used in studies of this kind, but to try to do that and deal with the specifics would be very difficult.

I am content that the revised submission can be fully published since it addresses and raises a number of important public health issues even if the study analysis cannot resolve all of these.

Note:
This review refers to round 2 of peer review.

 Open peer review from Dan Osborn

Review
This submission needs some revision before it can be fully-published. Although marked up as needing major revisions I do not think the suggested amendments require too much additional work
by the authors and I think the study could be published after amendments are made.

The study design is clearly focused on exploring the links between birth weight and urinary levels of arsenic. It also discusses some explanation for the “no-effect” result obtained, suggesting that either exposure was not high enough or that there was a protective role within the toxicokinetics - for which there was some pertinent and potentially important data.

Some points for clarification or to help the reader have been included as comments on the submitted manuscripts. These would not be difficult to take into account, adjusting the material accordingly. Marked up documents have been sent to the editorial office by email.

Other points that would improve the paper would be:

It would help the paper to give it a little more context. For example, I wonder if ref 34 provides some of that. Perhaps the results of this study into over 2,000,000 births in Peru in the 21st century ought to be given prominence earlier than it is. It does not consider arsenic at all as far as I am aware and does (as referenced) say birth weights in Tacna are higher – it also suggests why this might be, albeit qualitatively. Making reference to this study earlier might provide a helpful background to the submission as the submission itself raises a potentially important point seemingly not included in ref 34 and also discusses toxicokinetics and demographic differences between the different peoples of Peru – such topics are not often covered in high level studies such as ref 34.

Although the work was designed to address one specific potential relationship and the statistical analysis shows there was “no-effect” or at least no significant relationship as revealed by the statistical analysis, there are a number of significant results in the tables and possibly in the supplementary material. Some of these results are shown in bold in the tables although one is not (see comments on supplementary materials text). It would be helpful to draw these statistically significant results out a little more than has been done in the text – or at least make the link between the significant result and the text clearer. I recognise these significant results, to some degree, sit outside the scope of the main hypothesis but nevertheless they deserve greater prominence especially as they relate to the explanation for “no-effect” put forward.

It should be possible to make more of the point that the paper goes some way to establishing what a “no-effect” level for arsenic exposure might be. There is discussion around this point in the submission – along the lines of making use of references that suggest some effects seen at lower levels of exposure but establishing where no effect levels are seems to be important and again as educational level is known it is potentially possible to link this material to some aspects of ref 34.

It was a surprise to see the data analysed as a group without breaking the analysis into two for boys and for girls. Although weight ranges at birth overlap between the sexes the additional detail, potentially, would improve the paper. If there is an explanation for not doing this please provide the reason. It may be due to sample size and subsequent lack of statistical power, for example.

Lastly, to relate the submission back to ref 34; is it possible to say anything about how the educational status of the mothers might influence the apparent, potentially protective, toxicokinetics, i.e. does toxicokinetics vary with socio-economic status? This might be a step to far for this submission and detract from the existing focus.

Note:
This review refers to round 1 of peer review and may pertain to an earlier version of the document.

 Open peer review from Zafar Adeel

Review
The authors explore the association of birth weight to exposure to arsenic, presumably consumed through drinking water. The selected population in Tacna, Peru is exposed to arsenic level of greater than 10 microgram/litre (also ppb) - which is a guideline value described by the World Health Organization (WHO). Based on the study of 158 pregnant women, the authors find no correlation between the arsenic metabolites found in urine samples and the corresponding birthweight.

The authors should consider the following general observations:

1. In the grand scheme, the arsenic exposure through drinking water in Tacna appears to be on the low side - which range from 10 to 25 ppb (based on citation 6, which is the authors' paper published in 2021). In their analysis, the authors compare their findings to those from Bangladesh where the concentrations are often one to two orders of magnitude greater, resulting in marked health impacts including those on pregnancy. The authors should acknowledge this major difference when they undertake such comparisons.

2. The authors do not provide strong evidence for ingestion routes for arsenic. Put differently, they do not present drinking water arsenic concentrations, and there is no attempt at correlating drinking water arsenic concentration to the DMA concentrations, which is a primary arsenic metabolite.

The authors should consider the following revisions:

a. The descriptive paragraph on arsenic in drinking water (lines 10-16) needs to be revised by including a summary of arsenic concentrations found in drinking water, including their speciation (i.e., arsenite vs. arsenate). They should also comment on how these values concentrations compared with those found elsewhere, particularly in Bangladesh/India (partly because they reference Bangladesh in their discussion later in the paper).

b. In the Discussion session, there are several references to arsenic concentration (valued at µg/L) that do not clarify whether they are talking about arsenic concentration in the drinking water or the composite value in urine samples. For example, one can surmise they are referring to arsenic concentration in water on line 131. Whereas the tAs levels listed on line 133 are referring to arsenic in the urine level, one may surmise. This obfuscation, ostensibly not intentional, causes some difficulty for the reader and the chances of miscommunication are great. The authors should revise their presentation of values and clearly distinguish between arsenic concentrations in water and those in urine samples.

c. The statement on line 152, the way it is phrased could be misleading: "Some studies did not find any significant association with adverse birth outcomes, even with exposure levels ≥10 µg/L." 10 µg/L is the lowest end of the adverse value (although no level of arsenic is absolutely safe) identified as a "guideline" by WHO. There should not be an expectation, as implied here, to always observe adverse health impacts any time drinking water concentration goes above 10 µg/L.

d. Some of the explanations provided in the paragraph (line 196-204) seems highly speculative. The authors should consider re-phrasing these statements.

Note:
This review refers to round 1 of peer review and may pertain to an earlier version of the document.