Environmental exposure to metal mixtures and linear growth in healthy Ugandan children.

BACKGROUND: Stunting is an indicator of poor linear growth in children and is an important public health problem in many countries. Both nutritional deficits and toxic exposures can contribute to lower height-for-age Z-score (HAZ) and stunting (HAZ < -2).
OBJECTIVES: In a community-based cross-sectional sample of 97 healthy children ages 6-59 months in Kampala, Uganda, we examined whether exposure to Pb, As, Cd, Se, or Zn were associated with HAZ individually or as a mixture.
METHODS: Blood samples were analyzed for a mixture of metals, which represent both toxins and essential nutrients. The association between HAZ and metal exposure was tested using multivariable linear regression and Weighted Quantile Sum (WQS) regression, which uses mixtures of correlated exposures as a predictor.
RESULTS: There were 22 stunted children in the sample, mean HAZ was -0.74 (SD = 1.84). Linear regression showed that Pb (β = -0.80, p = 0.021) and Se (β = 1.92, p = 0.005) were significantly associated with HAZ. The WQS models separated toxic elements with a presumed negative effect on HAZ (Pb, As, Cd) from essential nutrients with presumed positive effect on HAZ (Se and Zn). The toxic mixture was significantly associated with lower HAZ (β = -0.47, p = 0.03), with 62% of the effect from Pb. The nutrient WQS index did not reach statistical significance (β = -0.47, p = 0.16). DISCUSSION: Higher blood lead and lower blood selenium level were both associated with lower HAZ. The significant associations by linear regression were reinforced by the WQS models, although not all associations reached statistical significance. These findings suggest that healthy children in this neighborhood of Kampala, Uganda, who have a high burden of toxic exposures, may experience detrimental health effects associated with these exposures in an environment where exposure sources are not well characterized.

Introduction

Stunting, poor linear growth for age, is a major problem that affects children worldwide particularly in low- and middle-income countries. It has been associated with poor neurodevelopmental and health outcomes in childhood and into adulthood. It has been associated with behavioral problems, cognitive deficits, and greater risk of hypertension and cardiovascular disease later in life [1-5]. The most common causes of stunting include malnutrition, micronutrient deficiencies and infection [5, 6]; however there is growing recognition of the contribution of toxic environmental exposures including lead [7-9]. The negative effects of lead exposure on growth and development are of particular concern because of the universal exposure to children around the globe and disproportionately high exposures in low- and middle-income countries [10, 11]. In countries without strong regulations on industrial use and disposal of toxic substances, the health effects of exposure to growing children are likely increased compared to those in more highly regulated countries. However the actual levels of exposure and their health effects are often unknown due to lack of health surveillance programs and research. The paradigm for understanding exposure sources of toxic metals, metalloids, and metallic elements and the research on cognitive and health effects of these exposures occurs in wealthier countries; yet due to vast differences in housing conditions, urban development and zoning laws, drainage, sanitation, and home-based industry [12], there are likely very different risk factors for exposure and more dangerous exposure patterns facing children in poorer countries. Little is known about toxic metal exposures to young children in Uganda [13, 14] or their effect on stunting in low-and middle-income countries in general [7].

There is rapidly growing interest in the health effects of exposure to mixtures of environmental metals as opposed to those of single exposures because mixed exposure is a better representation of reality. Furthermore, there is evidence that co-exposures to mixtures of chemicals can have synergistically more harmful health effects than individual exposures, especially in the realm of neurodevelopmental effects [15-18]. For health outcomes including stunting and linear growth, we anticipate that in addition to detrimental effects of single exposures, there may be interactions between toxic metals such as lead and essential nutrients such as zinc and selenium [8]. Statistical techniques for analysis of chemical mixtures have been developed to better be able to understand the interactions between individual components of mixtures, and to gain a more comprehensive picture of the health effects of toxicants in concert. For simplicity, we refer to our mixture including lead (Pb), arsenic (As), cadmium (Cd), selenium (Se), and zinc (Zn), as metals, although Zn is a semi-metallic element and Se is a non-metallic element. To our knowledge, statistical techniques for mixtures have not been previously employed to study the effects of metal mixtures on linear growth of children in low income countries.

To determine the effect of exposure to environmental metal mixtures on height-for-age Z-score (HAZ) in this under-studied population, we undertook this analysis of data collected as part of a community-based study [13] on metal exposure in Ugandan children living in the Katanga urban settlement of Kampala. This study contributes to the understanding of metal exposures to children in a poor neighborhood of Kampala, Uganda and, to our knowledge, it is the first study to investigate the effects of exposure to metal mixtures on linear growth of young children.

Material and methods

Study population

In May 2016 community-based cohort of 100 children age 6 to 59 months was recruited using probability sampling method from the Katanga urban settlement near Mulago Hospital in Kampala, Uganda. The community and recruitment procedures are described in detail elsewhere [13]. In short, all children were from the same low-lying community of makeshift temporary structures with poor drainage and garbage management. Inclusion criteria were age 6–59 months, being a permanent resident of the Katanga settlement, and having a caretaker willing to bring the child to Mulago Hospital that day for a medical examination, blood draw, and environmental questionnaire. Children were excluded from the study if they were found to require urgent medical attention or if their caretaker did not speak English or Luganda. Any child requiring urgent medical attention was transported immediately to Mulago Hospital for care. All eligible children and their caretakers were escorted to Mulago Hospital where written informed consent was obtained from the caretaker, a venous blood sample was collected from each child into a metal-free vacutainer tube. A rapid diagnostic test for malaria was completed for each child and positive results were followed up with a Giemsa blood smear. A physical exam was then completed for each child and environmental questionnaire completed by the child’s caretaker. The study protocol was approved by the Institutional Review Board of the University of Minnesota, the Research & Ethics Committee of Makerere University School of Biomedical Sciences, and the Uganda National Council for Science and Technology.

Laboratory analysis

Upon collection, whole blood samples were refrigerated at 4°C and shipped to New York, USA for analysis. Whole blood samples (1 ml) were acid digested using concentrated nitric acid (2-ml) and hydrogen peroxide (1 ml) at room temperature for 48 hours prior dilution to 10-ml with deionized water and were analyzed using an Agilent 8800 ICP Triple Quad (ICP-QQQ) (Agilent technologies, Inc., Delaware, USA) in MS/MS mode with appropriate cell gases to eliminate molecular ion interferences. Using a method previously described, samples were analyzed for elements including antimony, arsenic, barium, cadmium, cesium, chromium, cobalt, copper, lead, manganese, nickel, selenium, zinc using external calibration with appropriate internal standards (yttrium, indium, tellurium and lutetium) at the Senator Frank R. Lautenberg Environmental Health Sciences Laboratory at the Icahn School of Medicine, Mount Sinai, NY[19, 20]. The limit of detection limits were: 0.013 ng/ml for As, 0.001 ng/ml for Cd, 0.003 ng/ml for Pb, 0.07 ng/ml for Se and 0.1 ng/ml Zn.

Linear growth

Height and weight were measured during the physical exam for each child. Height-for-age Z-score (HAZ) was calculated for each child using Epi Info version 3.5.1 from Centers for Disease Control and Prevention, which applies height and weight measurements against the Centers for Disease Control/World Health Organization 1978 growth references [21, 22]. Stunting was defined as height-for-age Z-score (HAZ) > 2 standard deviations below the reference mean for the same sex and age.

Statistical analyses

Initial descriptive statistics are reported for the whole study population and groups stratified by stunting status. A chi-square test compared the stunted and non-stunted groups for dichotomous population characteristic variables (sex, mother’s education, and whether the child had been admitted to the hospital). The Mann-Whitney U test was used for continuous variables (sex and HAZ). Predictors included five metals and metallic elements: lead (Pb), arsenic (As), cadmium (Cd), selenium (Se), and zinc (Zn). Metal data were log2 transformed because of right-skewness for comparability. We tested for differences in the untransformed metal concentrations in the stunted and non-stunted children using a Wilcoxan rank sum test. Analysis by multivariable linear regression modeled HAZ as a continuous outcome. Metals and metallic elements were selected based upon previously described association with stunting (Pb, Se, Zn) [7–9, 23–26], known developmental toxicity (Pb, As, Cd) [27-29] or nutritional importance for healthy growth and development (Se, Zn) [8, 30, 31]. Two subjects were excluded for missing height measurements, and one subject was excluded for errors in heavy metals measurements, resulting in a final sample size of 97 children.

Multivariable linear regression was done to identify which metals may have a significant association with HAZ score. We identified the correlation structure with Pearson’s coefficients and we plotted it using a heatmap. Metals were divided into those with expected negative (Pb, As, Cd) and positive effects (Se, Zn) on growth. Associations between these mixtures and HAZ score were analyzed by Weighted Quantile Sum (WQS) regression, using the gWQS package in R. The WQS method analyzes high-dimensional datasets such as environmental exposure mixtures through a weighted index estimating the mixed effect of all predictor variables on the outcome. We tested the relationship between HAZ and a WQS index estimated from ranking exposure concentrations in quintiles (q = 5) for parameter estimation. The weight of each component of the mixture reflects the contribution of that component to the overall effect. We analyzed the mixture of all 5 metals together and for the mixtures based on the presumed direction of association with stunting. We constrained the effect of the mixture to be either positive or negative based upon preliminary analyses and literature. We assumed a linear relationship between exposure and growth. All presented models used 40% of the dataset for training and 60% of the dataset for validation. We assigned 100 bootstrapping steps in each model. All analyses were conducted in R version 3.5.1. All data is available in supporting information (S1 Data).

Covariates

Maternal educational level was determined by the child’s caretaker who answered all questionnaire questions. It was assessed on a six-level scale that was collapsed into two levels (completed primary school or less vs. completed secondary or more) to preserve statistical power. Caretakers also answered whether the child had ever been hospitalized for an illness (yes/no) as a marker of history of major medical illness in the child. Models were not adjusted for child sex or age because the outcome HAZ is generated through a sex and age-specific algorithm. Questionnaires were administered by study staff in Luganda or English.

Results

Population characteristics

The final sample included 97 children, and included slightly more boys (n = 51) than girls (n = 46). More than 1 in 5 children were stunted; mean HAZ was below zero and fairly normally distributed with outliers at -6.31 and -8.68. After careful review, these outliers were determined to be accurate observations and were included in the sample. The age range was 6.1 months to 59.9 months (Table 1). Questionnaires were answered by the child’s primary caretaker, who was the mother for the majority (n = 89, 91.8%) of children, a grandparent for 6.2% (n = 6) of children, and the father for 2.1% (n = 2). There were no statistically significant differences between the stunted and non-stunted groups.

Table 1

Demographics of study population.

Study Population Characteristics	All n (%) or mean ± SD	Stunted n (%) or mean ± SD	Not Stunted n (%) or mean ± SD	p-value
Observations (n)	97	22	75
Child sex				0.1544
Female	46 (47.4)	7 (31.8)	39 (52.0)
Male	51 (52.6)	15 (68.2)	36 (48.0)
Stunting
Yes	22 (22.7)
No	75 (77.3)
Mother's education				0.613
Primary school or less	62 (65.3)	13 (59.1)	51 (68.0)
Secondary school or greater	33 (34.7)	9 (40.9)	24 (32.9)
Child ever admitted to hospital				1.0
Yes	19 (19.6)	4 (18.2)	15 (20.0)
No	78 (80.4)	18 (81.8)	60 (80.0)
Child's age (months)	28.0 ± 14.9	30.7 ± 15.0	27.3 ±14.9	0.3156
Child's height-for-age Z-score (HAZ)	-0.74 ± 1.84	-3.12 ± 1.58	-0.04 ± 1.23	<0.0001

Study population characteristics for the entire study group and subgroups stratified by stunting status. Reported p-value for Chi-Square test for dichotomous variables (sex, mother’s education, and whether the child had ever been admitted to the hospital), and for Mann-Whitney U test for continuous variables (age and HAZ).

Exposures

All children in our sample had detectable blood lead levels, and 65% (n = 63) of children had blood lead levels ≥5 ug/dL. Untransformed metal exposures are shown in Table 2; there were no significant exposure differences between children with stunting and with no stunting. Exposure means (S1 Table) and correlations are included in supplemental materials (S1 Fig). Full results of metals analysis and the environmental questionnaire are published previously [13].

Table 2

Exposures.

	All	Stunted	Not Stunted
Metal	Median (IQR)	Median (IQR)	Median (IQR)	p-value
Pb (μg/dL)	5.78 (4.50–7.70)	6.32 (5.63–7.69)	5.64 (4.42–7.61)	0.145
As (μg/L)	0.23 (0.15–0.33)	0.25 (0.17–0.36)	0.22 (0.14–0.32)	0.395
Cd (μg/L)	0.084 (0.038–0.130)	0.084 (0.040–0.120)	0.084 (0.037–0.140)	0.860
Se (μg/dL)	12.20 (10.69–15.02)	11.53 (9.65–12.13)	13.05 (10.9–15.49)	0.0583
Zn (mg/L)	3.53 (3.02–4.24)	3.50 (3.09–4.15)	3.53 (3.02–4.28)	0.617

Metal exposures for the study population (n = 97), and for the stunted (n = 22), and not stunted (n = 75) subgroups. The reported p-value is for a Wilcoxan rank sum test comparing metals exposures in stunted and not stunted populations. All p-values are not significant.

Multivariable linear regression

A multivariable model including all five metals and covariates (mother’s educational level and whether the child had ever been admitted to the hospital) was used to predict child’s HAZ. There was a negative association between Pb and HAZ (β = -0.80, p = 0.021), and a positive association between Se and HAZ (β = 1.92, p = 0.005) (Fig 1).

Fig 1

Effect estimates for individual metals and height-for-age Z-score by multivariable linear regression model.

A multivariable linear regression model including 5 metals shows a significant negative association between Pb exposure and HAZ score, and a significant positive association between Se exposure and HAZ score. The model was adjusted for level of educational attainment of the child’s mother and for a binary variable indicating whether the child had ever been admitted to the hospital.

Weighted Quantile Sum regression

Metals were categorized into two groups by direction of the presumed effect on stunting: toxic metals (Pb, As, Cd) and essential nutrients (Se, Zn). The WQS index for toxic metals, assuming a negative association between exposure and HAZ, was statistically significant (β = -0.47, p = 0.03), with 62% of the effect attributed to Pb (Fig 2). The essential nutrient WQS index, assuming a positive association between exposure and HAZ, showed that the association was driven by Se, but did not reach statistical significance (β = 0.31, p = 0.16) (Fig 3).

Fig 2

Weighted Quantile Sum regression for the toxic metal mixture with negative association with HAZ.

a. Results of the regression model for the WQS index of the toxic metals (Pb, As, Cd). b. A locally estimated scatterplot smoothing (LOESS) fit showing the association between the WQS index and HAZ. c. Relative weight of each metal in the mixture.

Fig 3

Weighted Quantile Sum regression for the nutrientmixture with positive association with HAZ.

a. Results of the regression model for the WQS index of the essential nutrients (Se, Zn). b. A locally estimated scatterplot smoothing (LOESS) fit showing the association between the WQS index and HAZ. c. Relative weight of each nutrient in the mixture.

A WQS model including all metals and assuming a negative correlation with HAZ did not show a significant association; the relative weights of each component of the mixture showed that the association was driven by Pb and As (S2 Fig). When a positive correlation was assumed using the same mixture, there was a significant association between the WQS index and HAZ (p = 0.027). The relationship was driven primarily by Zn and Se (S3 Fig).

Discussion

These findings suggest that elevated Pb and lower Se in young children in the Katanga settlement of Kampala, Uganda are associated with decreased HAZ. We demonstrate the novel use of the WQS method to analyze the influence of a mixture of toxic and essential metals and metallic elements as a predictor of HAZ in a low-resource setting, a step in expanding the concept of expososme research to important health outcomes in low resource settings.

These findings, the first to examine the effect of metal exposure on linear growth in this population in Uganda, are consistent with previously observed associations between lead exposure and stunting in other populations. In a two-site study of 618 children 20–40 months of age in rural Bangladesh, concurrent blood lead level was associated with increased odds of stunting [7]. The population in this study had a higher prevalence of stunting, at 52.4%, compared to our urban population in Uganda (22.7%) and a lower median blood lead level at 4.2 μg/dL (IQR: 1.7–7.6) vs 5.8 μg/dL (4.5–7.7) in our Ugandan population. Another Bangladeshi study of 729 children under age 2 years in an urban slum environment, of which 39% were stunted and 86.6% had an elevated blood lead level (≥5μg/dL), found no difference in blood lead levels between children who were stunted and those who were not [32]. In addition to childhood concurrent Pb level, prenatal Pb exposures have been associated with decreased stature. Higher third trimester blood lead levels in mothers was associated with lower HAZ (β-0.10; 95% CI -0.19, -0.01) in a Mexican population of children age 4–6 years [33].

Selenium has a U-shaped toxicity dose-response curve with negative health effects at both deficient and toxic levels. Reported reference ranges vary by population, and prevalence of deficiency and toxicity vary widely based on Se content of the local soil and quality of diet [34, 35]. Our analysis used Se as a continuous variable; we were unable to determine Se deficiency using our measure of Se in whole blood. However, the whole blood Se levels in our population were slightly higher than those reported in 2012 from a population of the same age in Kinshasa, DRC (median 10.7 μg/dL, compared to 12.2 μg/dL in our population), likely due to differences in local Se concentration in soil and drinking water [36]. Low Se is commonly associated with cognitive effects, reduced immune function and other adult diseases related to inflammation; however, it is not commonly associated with childhood growth impairment. Mechanistic plausibility for this association comes from animal studies which have shown that Se deficiency is associated with growth retardation secondary to impaired bone metabolism [23]. There is a need for more research on Se deficiency and its effect on child growth and development [37].

Zn deficiency is more widely recognized to have a negative association with childhood growth and stature [38], although nutrient supplementation trials have had mixed results [39]. In addition to a direct association with stature, recent studies have shown that Zn status may interact with Pb in its effect on linear growth. In a population of 291 Mexican children aged 1–2 years, Zn adequacy was shown to attenuate the negative effect of lead on HAZ [8]. Although it is reasonable to suspect a high prevalence of Zn deficiency in our study population based on local diet and previous studies in similar populations [40-42], whole blood Zn cannot be used to diagnose deficiency so we were unable to determine rates of Zn deficiency in this study. The whole blood Zn levels in our study (median = 3.53 mg/L) were comparable but slightly lower than a population of the same age in Kinshasa, DRC (median = 5.0 mg/L) [36].

Both the negative (Pb) and positive (Se) associations we observed with linear growth in this analysis may have important implications for a child’s development far beyond stature. Lower HAZ and stunting (HAZ < -2) have been associated with developmental and health outcomes including cognitive deficits, detachment, and poorer learning as well as lower educational achievement and income [5, 6, 43–45]. Many of the same developmental cognitive effects have been independently associated with higher lead exposures [46] as well as selenium [35] and zinc deficiency [38]; further research is needed to more specifically understand the interactions between exposure to metal mixtures, nutrient status, linear growth, and neurodevelopment.

International child health research and interventions in child development have traditionally focused on poverty, nutritional deficiencies, and quality of learning opportunities [47]. While these factors likely are the strongest determinants of child growth and development in low resource settings, the potential detrimental effects of toxic environmental exposures including metals and air pollution are increasingly recognized as important points for intervention. These factors will continue to grow in importance as climate change progresses and as the number of toxic chemicals in our environment continues to grow. Perhaps the most striking finding in this study is that 65% of children had blood lead levels ≥5 ug/dL, the level at which the US CDC recommends intervention [48]. This research points out the importance of metal exposure in this urban setting and its potential pervasive effects on child development. It is clear that more work to characterize sources of Pb and other metal exposures in this environment is needed. An environmental health questionnaire in this population found no significant association between traditionally recognized risk factors for metal exposure in children (parent occupation, painted walls), and blood metal levels [13]. A previous study from another section of Kampala found increased blood lead level in children with increased proximity to a local landfill [14]. When risk factors for increased exposure are not understood, it becomes difficult to provide appropriate public health interventions to reduce exposures and reduce risk to children.

Strengths and limitations

There are multiple limitations of this study to acknowledge. This is an observational community-based sample and the results are not broadly applicable to larger populations of children. We used Epi info version 3.5.1 to derive HAZ scores, which uses growth charts developed by the US CDC/WHO in 1978 based upon a US population of formula-fed infants [21, 49]. Use of this reference for our population of primarily breast-fed babies in Uganda could result in overestimating undernutrition in the babies under 12 months since the rate of growth in formula-fed infants is generally greater than breast-fed infants in the first year of life. We don’t expect this significantly affected our results because of the 19 infants under 12 months of age in our sample, there were only 2 with HAZ <-2. The proportion of infants under 12 months with stunting (10.5%) was less than in the over 12-month old group (25.6%) and the overall sample (22.7%). We were also limited by a small sample size, which may have limited our ability to find significant associations; due to this we used HAZ as a continuous rather than dichotomized variable to increase power. We anticipate that advancing this work beyond the small pilot sample will provide opportunities to learn more about interactions between metals, nutrients, and their effect on growth in Ugandan children. If these associations hold true in larger studies, this may represent important areas for intervention in environments known to have increased risk of metals exposures [50]. Finally, our measure of metals in whole blood precludes us from determining Zn or Se deficiency in this population of children.

Conclusions

This work shows associations between Pb, Se and HAZ, and demonstrates the application of a mixtures analysis methodology to assess the effects of exposures to environmental mixtures in healthy children in urban Kampala Uganda. To date, there is sparse literature on the extent of toxic metal exposure to children in the low- and middle-income countries, the most important sources of exposure in different settings (urban vs rural), and on the expected health effects in these settings. This work suggests opposing effects of toxic and essential metals on HAZ in young children. In order to design more effective interventions to improve early childhood growth and cognitive development, important environmental exposures including lead cannot be ignored. It is important to advance the work to identify sources of contamination from lead and other metals in this poor urban environment, and to work for better protection of children from toxic exposures.

Correlations of metal exposures.

Legend: Correlation plots of metals exposures show the highest correlation between Pb and Cd, and between Se and Zn.

(JPG)

Click here for additional data file.

WQS model including all metals and assuming a negative correlation with HAZ.

a. Results of the regression model for the WQS index of the metals (Pb, As, Cd, Se, Zn). b. A locally estimated scatterplot smoothing (LOESS) fit showing the association between the WQS index and HAZ. c. Relative weight of each metal in the mixture.

(JPG)

Click here for additional data file.

WQS model including all metals and assuming a positive correlation with HAZ.

a. Results of the regression model for the WQS index of the metals (Pb, As, Cd, Se, Zn). b. A locally estimated scatterplot smoothing (LOESS) fit showing the association between the WQS index and HAZ. c. Relative weight of each metal in the mixture.

(JPG)

Click here for additional data file.

Unadjusted exposure means.

Caption: Unadjusted means of metal exposures for the study population (n = 97), and for the stunted (n = 22), and not stunted (n = 75) groups.

(DOCX)

Click here for additional data file.

(XLSX)

Click here for additional data file.

11 Dec 2019

PONE-D-19-30717

Exposure to metal mixtures and linear growth in healthy Ugandan children

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Reviewer #1: To study whether toxic metals and essential nutrients were associated with growth of children (using height-for-age Z-score, HAZ). There were 97 children enrolled, aged from 6 to 59 months, and their whole blood samples were analyzed for arsenic (As), cadmium (Cd), lead (Pb), selenium (Se), and zinc (Zn) by ICP-MS at the Senator Frank R. Lautenberg Environmental Health Sciences Laboratory at the Icahn School of Medicine, Mount Sinai, NY. The result showed that lower Se and higher Pb blood levels were both associated with lower HAZ.

Abstract line 42, “Lower selenium and higher blood level were both associated with lower HAZ.” What were higher blood levels?

Were all the concentrations of metals in blood analyzed whole blood sample? I know Pb was analyzed with whole blood sample, however, for the other elements, especially, Se and Zn, the standard analysis method are serum. Please clarify this point and provide the method of preparing sample (pre-treatment).

Table 2, please provide the mean and SD as well as median and IQR. Moreover, I suggest to divide into “Stunting” and “Non-stunting” groups, then using independent T test or nonparametric method to test the difference between these 2 groups.

This is an interesting manuscript. Thanks for giving me the chance to review…

Reviewer #2: The manuscript associates the levels of Pb, Cd, As, Zn and Se with stunting in Ugandan children aged 6 to 60 months. Particularly, a negative association between the height-for-age score (HAZ) and Pb in blood and a positive association between HAZ and Se in blood. An new approach introducing a weighted quantile sum (WQS) statistics was applied in the attempt to evaluate the impact of multiple metals in blood on the levels of height for age score.

Some weaknesses are present in the manuscript. Particularly:

1-the living environment and the social status of study children were not included in the analysis, so these potential confounding factors were not taken into account in studying the association between HAZ and levels of metals in blood;

2-the score HAZ is built taking into account the different countries or it was built with US data and adapted to Ugandan children? Could you explain something more about HAZ?

3- the levels of Zn in blood reported in the study are much higher than those expected based on reference values for the general population. How can you explain this?

All this considering, I think that additional information should be included in the paper and that results of the study are only suggestive of a role played by Pb exposure on stunting, so that a major caution should be used in drawing conclusion about the detrimental health effect due to heavy metals

Suggested revisions

Methods

Add information about the year of the field study;

Add the list of metals measured in the original study;

Add information about the living environment and the social status of the study children and explain their effect of the association between HAZ and metals in blood.

Add information about the HAZ score. Is this country specific? In the case this was calculated for US children, do the author have confidence about its applicability in Ugandan children?

The authors describe WQS as a statistical tool for high-dimensional dataset; is the dataset used in this study big enough for the use with WQS?

Results

A R2 = 0.088 is very small; so the model multiple regression model is only marginally explaining the HAZ score.

The figures of the WQS regressions are not very clear. What is the meaning of the graph with the curve? Where is the HAZ score on the graph? Moreover, maybe the graphical representation of this WQS model could be limited to Fig 3 and Fig 4.

Discussion

Add information or hypothesis about sources of exposure to Pb and other metals

Compare the levels of metals in blood in study children and reference values in the general population. From this it should be clear that some issue on Zn in blood is present;

Add info about the derivation of HAZ and its limitation when applied in Ugandan children;

Add the limitations of your study;

Consider to smooth the conclusion of the manuscript, to include the weaknesses of the study and the fact that only Pb seems to exert a negative influence on growth.

**********

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Reviewer #1: No

Reviewer #2: No

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25 Jan 2020

January 17, 2020

Dear Editor and Reviewers,

Thank you for your thoughtful comments and suggestions on this paper. I have revised the paper according to your suggestions and think that the resulting paper is both stronger and clearer. I appreciate your time and contributions to this publication.

Sincerely,

Emily Moody

Reviewer #1:

To study whether toxic metals and essential nutrients were associated with growth of children (using height-for-age Z-score, HAZ). There were 97 children enrolled, aged from 6 to 59 months, and their whole blood samples were analyzed for arsenic (As), cadmium (Cd), lead (Pb), selenium (Se), and zinc (Zn) by ICP-MS at the Senator Frank R. Lautenberg Environmental Health Sciences Laboratory at the Icahn School of Medicine, Mount Sinai, NY. The result showed that lower Se and higher Pb blood levels were both associated with lower HAZ.

Abstract line 42, “Lower selenium and higher blood level were both associated with lower HAZ.” What were higher blood levels?

Were all the concentrations of metals in blood analyzed whole blood sample? I know Pb was analyzed with whole blood sample, however, for the other elements, especially, Se and Zn, the standard analysis method are serum. Please clarify this point and provide the method of preparing sample (pre-treatment).

Response: Yes, the analysis method included a single sample preparation from whole blood. Further information on sample preparation has been included, and references from previous publications employing this method. In addition, the inability to comment on Se or Zn deficiency with this sample source was commented on in the limitations section as well as the results sections. To limit the risk to the young children who participated we chose to take a single blood sample to minimize the amount of blood drawn, so we did not have enough to fractionate and analyze whole blood and serum samples.

Updated manuscript text: Page 6, Line 120-131: “Upon collection whole blood samples were refrigerated at 4oC and shipped to New York, USA for analysis. Whole blood samples (1 ml) were acid digested using concentrated nitric acid (2-ml) and hydrogen peroxide (1 ml) at room temperature for 48 hours prior dilution to 10-ml with deionized water and were analyzed using an Agilent 8800 ICP Triple Quad (ICP-QQQ) (Agilent technologies, Inc., Delaware, USA) in MS/MS mode with appropriate cell gases to eliminate molecular ion interferences. Using a method previously described, samples were analyzed for heavy metals including antimony, arsenic, barium, cadmium, cesium, chromium, cobalt, copper, lead, manganese, nickel, selenium, zinc using external calibration with appropriate internal standards (yttrium, indium, tellurium and lutetium) at the Senator Frank R. Lautenberg Environmental Health Sciences Laboratory at the Icahn School of Medicine, Mount Sinai, NY[CHITRA REFS PMID: 30849576, 31357156].”

Page 14, Line 302-303: “we were unable to determine Se deficiency using whole blood Se”

Page 15, Line 316-317: “whole blood Zn cannot be used to diagnose deficiency so we were unable to determine rates of Zn deficiency in this study.”

Page 17, Lines 353-354: “Finally, our measure of metals in whole blood precludes us from determining Zn or Se deficiency in this population of children.”

Table 2, please provide the mean and SD as well as median and IQR. Moreover, I suggest to divide into “Stunting” and “Non-stunting” groups, then using independent T test or nonparametric method to test the difference between these 2 groups.

Response: The mean and SD have been added to the table. Another column for the p-value from a wilcoxan rank sum test was added (stunted children vs non-stunted children) showing no significant differences between the stunted and non-stunted groups for any of the heavy metal concentrations.

This is an interesting manuscript. Thanks for giving me the chance to review…

Reviewer #2:

The manuscript associates the levels of Pb, Cd, As, Zn and Se with stunting in Ugandan children aged 6 to 60 months. Particularly, a negative association between the height-for-age score (HAZ) and Pb in blood and a positive association between HAZ and Se in blood. A new approach introducing a weighted quantile sum (WQS) statistics was applied in the attempt to evaluate the impact of multiple metals in blood on the levels of height for age score.

Some weaknesses are present in the manuscript. Particularly:

1-the living environment and the social status of study children were not included in the analysis, so these potential confounding factors were not taken into account in studying the association between HAZ and levels of metals in blood;

2-the score HAZ is built taking into account the different countries or it was built with US data and adapted to Ugandan children? Could you explain something more about HAZ?

3- the levels of Zn in blood reported in the study are much higher than those expected based on reference values for the general population. How can you explain this?

All this considering, I think that additional information should be included in the paper and that results of the study are only suggestive of a role played by Pb exposure on stunting, so that a major caution should be used in drawing conclusion about the detrimental health effect due to heavy metals.

Suggested revisions

Methods

Add information about the year of the field study;

Updated manuscript text: Page 6, Line 99 “In May 2016, “

Add the list of metals measured in the original study;

Updated manuscript text: Page 7, Lines 119-121 “…samples were analyzed for antimony, arsenic, barium, cadmium, cesium, chromium, cobalt, copper, lead, manganese, nickel, selenium, zinc were measured by LC-tandem mass spectrometry in the Senator Frank R. Lautenberg Environmental Health Sciences Laboratory at the Icahn School of Medicine, Mount Sinai, NY.

Add information about the living environment and the social status of the study children and explain their effect of the association between HAZ and metals in blood.

Response: The neighborhood where the children live, and from which they were recruited, is described in detail in the referenced publication.

Updated Manuscript text: Page 6, Lines 96-98 “The community and recruitment procedures are described in detail elsewhere (1). In short, all children were from the same low-lying community of makeshift temporary structures with poor drainage and garbage management.”

Add information about the HAZ score. Is this country specific? In the case this was calculated for US children, do the author have confidence about its applicability in Ugandan children?

Response: We calculated HAZ scores using Epi info version 3.5.1 These standards were developed form normal formula-fed children in the USA. When applied to this population in Uganda, there may be overestimation of estimates of poor growth in the <1 yo children because breastfed babies grow more slowly than formula fed babies. This has been added to the discussion of limitations of the study.

Updated manuscript text: Page 16, Lines 310-313 “We used Epi info version 3.5.1 to derive HAZ scores, which is based upon a sample of formula-fed babies in the USA in 1978, which could overestimate undernutrition in our sample since the rate of growth in formula-fed infants is greater than breast-fed in the first year of life.”

The authors describe WQS as a statistical tool for high-dimensional dataset; is the dataset used in this study big enough for the use with WQS?

Response: WQS is a statistical tool that can handle from small to high dimensional datasets. We have previously implemented WQS using a small dataset and found good performance of the WQS under those conditions (2). A WQS extension for high-dimension data is available when needed (3). This study did have a small sample size. To overcome the challenges of the smaller sample size, we use 40% of the observations to train the model and 60% of the observations to validate the model. We take confidence that the model performance is satisfactory from the example of another small dataset (N=200) that had a much higher-dimension dataset with a ratio of 5:1 of variables to observations (4). Our data set had a variable: observation ratio of 19.5:1.

Results

A R2 = 0.088 is very small; so the model multiple regression model is only marginally explaining the HAZ score.

Response: In many environmental studies, the R2 are generally small. We expect that had we been able to account for nutritional factors we would have been able to much better account for HAZ variation in the children. However, this R2 is very small which may be due to other conditions we are not aware of. To reduce confusion, we have removed this sentence from the manuscript.

The figures of the WQS regressions are not very clear. What is the meaning of the graph with the curve? Where is the HAZ score on the graph?

Response: Please excuse my oversight that the y-axis label for the graph with the curve was incorrectly labeled. It should say HAZ (our outcome) and this has been changed in all figures. The curve is a LOESS (locally estimated scatterplot smoothing) fit which represents the association between the WQS index and the outcome variable. The outcome variable is adjusted for all covariates, and a summary of each variables’ relative weight within that index.

Moreover, maybe the graphical representation of this WQS model could be limited to Fig 3 and Fig 4.

Response: This is reasonable. The results from the WQS analyses divided by direction of effect are the main findings for this paper, so the WQS figures for those findings remain as part of the main text. The secondary findings including all 5 metals in the WQS analyses have been moved to supporting information.

Discussion

Add information or hypothesis about sources of exposure to Pb and other metals

Response: Description of the environmental questionnaire and discussion of possible sources fo Pb and other metal exposure was in a previous publication (1). However, I have added a small amount of discussion of this issue.

Updated Manuscript Text: Page 16, Lines 297-303 “It is clear that more work to characterize sources of Pb and other heavy metal exposures in this environment is needed. An environmental health questionnaire in this population found no significant association between traditionally recognized heavy metal exposure risk factors in children (parent occupation, painted walls), and blood metal levels (1). One study from another section of Kampala found increased blood lead level in children with increased proximity to a local landfill (5).”

Compare the levels of metals in blood in study children and reference values in the general population. From this it should be clear that some issue on Zn in blood is present;

Response: It was the purpose of a previous publication to discuss the values in detail(1). Also, because our analysis of all metals was in whole blood, we are unable to comment on either Zn or Se deficiency. However, we do compare our results with a publication that similarly analyzed heavy metals in whole blood of children age 6-59 months from Kinshasa DRC.

Updated manuscript text: Page 15, Lines 274-279 “Although it is reasonable to suspect a high prevalence of Zn deficiency in our study population based on local diet and previous studies in similar populations (6-8), whole blood Zn cannot be used to diagnose deficiency so we were unable to determine rates of Zn deficiency in this study. The whole blood Zn levels in our study (median = 3.53 mg/L) were comparable but slightly lower than a population of the same age in Kinshasa, DRC (median = 5.0 mg/L) (9). “

Add info about the derivation of HAZ and its limitation when applied in Ugandan children;

Updated manuscript text: Page 16, Lines 311-314 “We used Epi info version 3.5.1 to derive HAZ scores, which is based upon a sample of formula-fed babies in the USA in 1978, which could overestimate undernutrition in our sample since the rate of growth in formula-fed infants is greater than breast-fed in the first year of life.”

Add the limitations of your study; Consider to smooth the conclusion of the manuscript, to include the weaknesses of the study and the fact that only Pb seems to exert a negative influence on growth.

Response: The limitations section was updated and expanded. Page 16

References:

1. Cusick SE, Jaramillo EG, Moody EC, Ssemata AS, Bitwayi D, Lund TC, et al. Assessment of blood levels of heavy metals including lead and manganese in healthy children living in the Katanga settlement of Kampala, Uganda. BMC Public Health. 2018;18(1):717.

2. Carrico C, Gennings C, Wheeler DC, Factor-Litvak P. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. J Agric Biol Environ Stat. 2015;20(1):100-20.

3. Curtin P, Kellogg J, Cech N, Gennings C. A random subset implementation of weighted quantile sum (WQSRS) regression for analysis of high-dimensional mixtures. Communications in Statistics - Simulation and Computation. 2019.

4. Team CMA, Mazzella M, Sumner SJ, Gao S, Su L, Diao N, et al. Quantitative methods for metabolomic analyses evaluated in the Children's Health Exposure Analysis Resource (CHEAR). J Expo Sci Environ Epidemiol. 2020;30(1):16-27.

5. Graber LK, Asher D, Anandaraja N, Bopp RF, Merrill K, Cullen MR, et al. Childhood lead exposure after the phaseout of leaded gasoline: an ecological study of school-age children in Kampala, Uganda. Environ Health Perspect. 2010;118(6):884-9.

6. Walker BE, Kelleher J. Plasma whole blood and urine zinc in the assessment of zinc deficiency in the rat. J Nutr. 1978;108(10):1702-7.

7. Ndeezi G, Tumwine JK, Bolann BJ, Ndugwa CM, Tylleskar T. Zinc status in HIV infected Ugandan children aged 1-5 years: a cross sectional baseline survey. BMC Pediatr. 2010;10:68.

8. Bitarakwate E, Mworozi E, Kekitiinwa A. Serum zinc status of children with persistent diarrhoea admitted to the diarrhoea management unit of Mulago Hospital, Uganda. Afr Health Sci. 2003;3(2):54-60.

9. Tuakuila J, Kabamba M, Mata H, Mata G. Toxic and essential elements in children's blood (<6 years) from Kinshasa, DRC (the Democratic Republic of Congo). J Trace Elem Med Biol. 2014;28(1):45-9.

Submitted filename: Response letter.docx

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17 Feb 2020

PONE-D-19-30717R1

Exposure to metal mixtures and linear growth in healthy Ugandan children

PLOS ONE

Dear Dr. Moody,

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Reviewers' comments:

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Reviewer #2: All comments have been addressed

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Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Table 2 of this version needed still to improve. I saw the authors said adding a column of p-values to show the test (Wilcoxan rank sum test) of stunted children vs non-stunted children. However, I did not read any of this information in the table 2 nor in the text. The readers will not understand what for these p-values were.

I still suggest that sepreate into 2 groups (stunted children vs non-stunted children) of the metal data distributions for the table 2, as I suggested previously.

Reviewer #2: The manuscript has been revised and improved, but still some points rise concern and need interventions.

Particularly:

1-the authors state that they measured heavy metals. Unfortunately this is questionable, as

• As is a semi metallic element,

• Se in a non metallic element

Sorry for not having pointed this before. Please revise the text considering this issue.

Line 42 in abstract. The negative effect is presumed as well.

Discussion: I still think that the results of this study do not demonstrate, but suggest that children are experiencing detrimental effects associated with exposure to environmental pollutants. This comment was previously given, but authors did not take into account it. Moreover, the effect seems to be associated with Pb and not with other pollutants.

Paragraph Linear Growth. Please add here the information about the HAZ score. That is:

1-this was developed for US children fed with formula.

2. this may be not suitable for breast-feed children up to 1 year. Please specify where we can find the algorithm and apply it. Add references too.

Given that the HAZ index in not working very well for children younger than 1 y, could you please state how many of the stunting children were in this range of age?

Table 1: divide the table and compare the groups of stunting and non-stunting children.

Table 2 and t-test between stunted and non stunted children. Please, divide children based on their classification of stunting /non-stunting and compare the groups. IN the present version, a p value is given, but it is not clear why. Specify it in the legend.

The correlation between metals is not relevant to the study; I propose to remove Figure 1, especially considering that the data on metals were published before.

Discussion.

The study does not demonstrate but suggest, a role of lead on stunting in Ugandan children; this limit the generalization to the other investigated metals.

Moreover, it should be clarified how many children, classified as stunting, were below 12 months, considering the limitation of the HAZ index for those children.

In general, author should add some additional info in the manuscript, and use more caution in the interpretation of their results.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Hung-Yi Chuang

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

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28 Feb 2020

Dear Dr. Jose Moran and Reviewers,

Thank you for the time you have put into reviewing this paper. I appreciate the comments and have done my best to fully and thoughtfully respond to each. I hope that you find the responses have strengthened the manuscript as I certainly do. I look forward to any further communication.

In the following, please find the numbered comments from the reviewers followed by bulleted responses.

Sincerely,

Emily Moody

Reviewer #1:

1. Table 2 of this version needed still to improve. I saw the authors said adding a column of p-values to show the test (Wilcoxon rank sum test) of stunted children vs non-stunted children. However, I did not read any of this information in the table 2 nor in the text. The readers will not understand what for these p-values were.

• In response to the sum of comments on Table 2, and in an effort to make the table easy to read, I have decided to use the log2 transformed metals measures and to provide the mean (SD) rather than both mean (SD) and median (IQR). The t-test to compare stunted and non-stunted groups was done and is described in the methods section as well as the caption for the table.

• The original table with unadjusted metals measures, including both mean (SD) and median (IQR), and including stunted and non-stunted groups and the appropriate Wilcoxon rank sum test is now included in the supplemental material.

• METHODS - LINE 144-146: “Heavy metals data were log2 transformed because of right-skewness for comparability. We tested for differences in the transformed heavy metal concentrations in the stunted and non-stunted children using a t-test.”

• RESULTS – LINE 199-202: “Transformed heavy metal exposures are shown in Table 2; there were no significant differences in heavy metal exposures between children with stunting and with no stunting. Untransformed heavy metal exposures (S1 Table) and correlations between heavy metal exposures are included in supplemental materials (S1Fig).”

• CAPTION – LINE 205-208: “Heavy metal exposures for the study population (n=97), and for the stunted (n=22), and not stunted (n=75) stratified groups. Heavy metals data were log2 transformed because of right-skewness. The reported p-value is for a t-test comparing heavy metals exposures in stunted and not stunted populations. All p-values are not significant.”

2. I still suggest that sepreate into 2 groups (stunted children vs non-stunted children) of the metal data distributions for the table 2, as I suggested previously.

• Thank you for the suggestion, I’m sorry I did not clearly understand your previous request. This has been included.

Reviewer #2: The manuscript has been revised and improved, but still some points rise concern and need interventions. Particularly:

1. the authors state that they measured heavy metals. Unfortunately this is questionable, as

• As is a semi metallic element,

• Se in a non metallic element

Sorry for not having pointed this before. Please revise the text considering this issue.

• Thank you for the comment. I have included a brief definition and citation in the introduction for readers who may be less familiar.

• LINE 74-76: “Heavy metals are a diverse group of naturally occurring metallic elements with high density and toxicity to humans; some are also essential nutrients and are referred to as essential heavy metals (1).”

• I have updated the title and text to more clearly say “heavy metal” rather than “metal” for greater accuracy.

2. Line 42 in abstract. The negative effect is presumed as well.

• This is true. Text was updated to read:

• LINE 36-38 “The WQS models separated nutritional metals with presumed positive effect on HAZ (Se and Zn) from toxic metals with a presumed negative effect on HAZ (Pb, As, Cd).”

3. Discussion: I still think that the results of this study do not demonstrate, but suggest that children are experiencing detrimental effects associated with exposure to environmental pollutants. This comment was previously given, but authors did not take into account it. Moreover, the effect seems to be associated with Pb and not with other pollutants.

• I agree with your observation and interpretation. I have revised in an attempt to change all similar language to more appropriately express that.

• LINE 44-47 “These findings suggest that healthy children in this neighborhood of Kampala, Uganda, who have a high burden of toxic heavy metal exposure, may experience detrimental health effects associated with these exposures in an environment where exposure sources are not well characterized.”

• LINE 253-254: “These findings suggest that elevated Pb and lower Se in young children in the Katanga region of Kampala, Uganda are associated with decreased HAZ.”

4. Paragraph Linear Growth. Please add here the information about the HAZ score. That is:

1-this was developed for US children fed with formula.

2. this may be not suitable for breast-feed children up to 1 year. Please specify where we can find the algorithm and apply it. Add references too.

• Thank you for attending to this important detail. The following text was changed:

• LINE 131-134: “Height-for-age Z-score (HAZ) was calculated for each child using Epi Info version 3.5.1 from Centers for Disease Control and Prevention, which applies height and weight measurements against the Centers for Disease Control/World Health Organization 1978 growth references (2, 3).”

• LINE 329-337 “We used Epi info version 3.5.1 to derive HAZ scores, which uses growth charts developed by the US CDC/WHO in 1978 based upon a US population of formula-fed infants (2, 4). Use of this reference for our population of primarily breast fed babies in Uganda could result in overestimating undernutrition in the babies under 12 months since the rate of growth in formula-fed infants is generally greater than breast-fed infants in the first year of life. We don’t expect this significantly affected our results because of the 19 infants under 12 months of age in our sample, there were only 2 with HAZ <-2. The proportion of infants under 12 months with stunting (10.5%) was less than in the over 12-month old group (25.6%) and the overall sample (22.7%).”

5. Given that the HAZ index in not working very well for children younger than 1 y, could you please state how many of the stunting children were in this range of age?

• Of course. There were 19 children younger than 1 year, two (11%) of whom were stunted. Of the 78 children older than one year, 20 (26%) were stunted.

• LINES 331-337 “We don’t expect this significantly affected our results because of the 19 infants under 12 months of age in our sample, there were only 2 with HAZ <-2. The proportion of infants under 12 months with stunting (10.5%) was less than in the over 12-month old group (25.6%) and the overall sample (22.7%).”

6. Table 1: divide the table and compare the groups of stunting and non-stunting children.

• The table has been updated. A chi-square test compared the stunted and non-stunted groups for dichotomous population characteristic variables (sex, mother’s education, and whether the child had been admitted to the hospital). The Mann-Whitney U test was used for continuous variables (sex and HAZ). This p-value is reported with clear explanation in the methods section and in the table caption.

• LINE 139-143: “Initial descriptive statistics were for the whole study population and groups stratified by stunting status. A chi-square test compared the stunted and non-stunted groups for dichotomous population characteristic variables (sex, mother’s education, and whether the child had been admitted to the hospital). The Mann Whitney U test was used for continuous variables (sex and HAZ).”

• CAPTION – LINE 193-196: “Study population characteristics for the entire study group and groups stratified by stunting status. Reported p-value for Chi-Square test for dichotomous variables (sex, mother’s education, and whether the child had ever been admitted to the hospital), and for Mann-Whitney U test for continuous variables (age and HAZ).”

7. Table 2 and t-test between stunted and non stunted children. Please, divide children based on their classification of stunting /non-stunting and compare the groups. IN the present version, a p value is given, but it is not clear why. Specify it in the legend.

• In response to the sum of comments on Table 2, and in an effort to make the table easy to read, I have decided to use the log2 transformed metals measures and to provide the mean (SD) rather than both mean (SD) and median (IQR). The t-test to compare stunted and non-stunted groups was done and is described in the methods section as well as the caption for the table.

• The original table with unadjusted metals measures, including both mean (SD) and median (IQR), and including stunted and non-stunted groups and the appropriate Wilcoxon rank sum test is now included in the supplemental material.

• METHODS - LINE 144-146: “Heavy metals data were log2 transformed because of right-skewness for comparability. We tested for differences in the transformed heavy metal concentrations in the stunted and non-stunted children using a t-test.”

• RESULTS – LINE 199-202: “Transformed heavy metal exposures are shown in Table 2; there were no significant differences in heavy metal exposures between children with stunting and with no stunting. Untransformed heavy metal exposures (S1 Table) and correlations between heavy metal exposures are included in supplemental materials (S1Fig).”

• CAPTION – LINE 205-208: “Heavy metal exposures for the study population (n=97), and for the stunted (n=22), and not stunted (n=75) stratified groups. Heavy metals data were log2 transformed because of right-skewness. The reported p-value is for a t-test comparing heavy metals exposures in stunted and not stunted populations. All p-values are not significant.”

8. The correlation between metals is not relevant to the study; I propose to remove Figure 1, especially considering that the data on metals were published before.

• The correlation plot has been moved to the supplemental materials where a reader may find the information if they are interested.

9. Discussion.

The study does not demonstrate but suggest, a role of lead on stunting in Ugandan children; this limit the generalization to the other investigated metals.

Moreover, it should be clarified how many children, classified as stunting, were below 12 months, considering the limitation of the HAZ index for those children.

In general, author should add some additional info in the manuscript, and use more caution in the interpretation of their results.

• Thank you for your contribution to this manuscript. These suggestions have been taken into account and answered in the above numerated items.

References:

1. Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ. Heavy metal toxicity and the environment. Exp Suppl. 2012;101:133-64.

2. Center for Disease Control. Use of World Health Organization and CDC Growth Charts for Children Aged 0–59 Months in the United States. September 10, 2010.

3. U.S. Department of Health & Human Services. Epi Info: Center for Surveillance, Epidemiology & Laboratory Services; [Available from: https://www.cdc.gov/epiinfo/index.html.

4. Dibley MJ, Goldsby JB, Staehling NW, Trowbridge FL. Development of normalized curves for the international growth reference: historical and technical considerations. Am J Clin Nutr. 1987;46(5):736-48.

Submitted filename: Response letter 2nd revision.docx

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10 Mar 2020

PONE-D-19-30717R2

Exposure to heavy metal mixtures and linear growth in healthy Ugandan children

PLOS ONE

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Reviewer #1: The new table 2 used a log2 tranformation of metal concentration, but the first column used uints without transformed, such as Pb(ug/dL), Al(ug/L), Cd(ug/L), Se(ug/dL), Zn(mg/L), were incorrected. That is why the negative numbers appears. Think about that, -2.15 ug/L As in blood, is so weird. If the authors used log2 transformation, then the units (ug/dL, ug/L, mg/L) in the table 2 should be ommitted. Otherwise, please used geometric means.

Reviewer #2: The authors addressed the comments and the manuscript is improved.

Still there are two suggestions:

1- Heavy metals. I previously commented that the trace elements measured by this research are not all "heavy metals", but there are semi-metallic elements and non-metallic elements. It is unclear why, following this comment, the authors transformed all the noun "metals" in the manuscript as "heavy metals". Please consider revising this.

2- Table 2 and log transformed data. Log transformation is a very common way to obtain normal distribution of data, useful for applying parametric statistics. This was properly done and I appreciate it. However, when reporting data in tables they should be reported as normal values. To report data in the log transformed form is not very clear for readers and should be avoided.

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Reviewer #2: No

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24 Apr 2020

April 23, 2020

Dear Editor Jose Moran and Reviewers,

I hope you are all well during this difficult time. I appreciate your continued work to improve our manuscript and feel that it has benefitted significantly from your contributions. I have thought carefully about your comments and responded as best I could. I hope you find the updated table and nomenclature acceptable for publication. As with the previous revision, I have included a document with all of the de-identified data to make the data underlying the published findings publicly available. I eagerly await to hear of next steps.

Sincerely,

Emily Moody

COMMENTS:

Reviewer #1: The new table 2 used a log2 tranformation of metal concentration, but the first column used uints without transformed, such as Pb(ug/dL), Al(ug/L), Cd(ug/L), Se(ug/dL), Zn(mg/L), were incorrected. That is why the negative numbers appears. Think about that, -2.15 ug/L As in blood, is so weird. If the authors used log2 transformation, then the units (ug/dL, ug/L, mg/L) in the table 2 should be ommitted. Otherwise, please used geometric means.

Thank you for this comment. I agree that units should have been omitted from that table and that using transformed data in this table is confusing. The table now includes the median and IQR for the entire cohort, and stunted and non-stunted subgroups and a Wilcoxan Rank Sum test for the test for differences in distribution. The mean and sd are included in the supplemental table for anyone interested in this information.

Reviewer #2: The authors addressed the comments and the manuscript is improved.

Still there are two suggestions:

1- Heavy metals. I previously commented that the trace elements measured by this research are not all "heavy metals", but there are semi-metallic elements and non-metallic elements. It is unclear why, following this comment, the authors transformed all the noun "metals" in the manuscript as "heavy metals". Please consider revising this.

I appreciate this comment and have thought about this a lot. I struggled to find a noun that can simply and accurately describe the mixtures. The common usage in environmental chemistry and environmental health includes As and Se as “heavy metals” although this may be an antiquated and ultimately inaccurate term. I have removed the use of “heavy metal” and have continued to generally refer to the mixture as metals with the following explanation. Where possible, I have used other descriptors.

“For simplicity, we refer to our mixture including lead (Pb), arsenic (As), cadmium (Cd), selenium (Se), and zinc (Zn), as metals, although Zn is a semi-metallic element and Se is a non-metallic element.”

2- Table 2 and log transformed data. Log transformation is a very common way to obtain normal distribution of data, useful for applying parametric statistics. This was properly done and I appreciate it. However, when reporting data in tables they should be reported as normal values. To report data in the log transformed form is not very clear for readers and should be avoided.

As in the comment above, I agree that this was not the correct choice. I have changed the table so that it presents the median and IQR for the entire cohort, and stunted and non-stunted sub groups and a Wilcoxan Rank Sum test for the test for differences in distribution. The mean and sd are included in the supplemental table.

Submitted filename: Dear Editor.docx

Click here for additional data file.

29 Apr 2020

Environmental exposure to metal mixtures and linear growth in healthy Ugandan children

PONE-D-19-30717R3

Dear Dr. Moody,

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PLOS ONE

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Reviewers' comments:

5 May 2020

PONE-D-19-30717R3

Environmental exposure to metal mixtures and linear growth in healthy Ugandan children

Dear Dr. Moody:

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