Akkermansia muciniphila modifies the association between metal exposure during pregnancy and depressive symptoms in late childhood

Emerging research suggests that exposures to metals during pregnancy and gut microbiome (GM) disruptions are associated with depressive disorders in childhood. Akkermansia muciniphila, a GM bacteria, has been studied for its potential antidepressant effects. However, its role in the influence of prenatal metal exposures on depressive symptoms during childhood is unknown. Leveraging a well-characterized pediatric longitudinal birth cohort and its microbiome substudy (n=112) and using a state-of-the-art machine-learning model, we investigated whether the presence of A.muciniphila in GM of 9-11-year-olds modifies the associations between exposure to a specific group of metals (or metal-clique) during pregnancy and concurrent childhood depressive symptoms. Among children with no A.muciniphila, a metal-clique of Zinc-Chromium-Cobalt was strongly associated with increased depression score (P<0.0001), whereas, for children with A.muciniphila, this same metal-clique was weakly associated with decreased depression score(P<0.4). Our analysis provides the first exploratory evidence hypothesizing A. muciniphila as a probiotic intervention attenuating the effect of prenatal metal-exposures-associated depressive disorders in late childhood.


Introduction
Depression is a global health burden, with 37% of adolescents experiencing elevated depressive symptoms worldwide between 2010 and 2020. 1 Emerging research indicates that disruptions in gut microbiota and metabolites may contribute to the development of depressive disorders. 2Likewise, bene cial or probiotic gut bacteria may help alleviate depressive symptoms.Akkermansia muciniphila is one such potentially bene cial bacteria that may be a promising avenue for preventive intervention of depression in childhood and adolescence. 3 muciniphila is a commensal inhabitant of the human gastrointestinal tract throughout life.3,4 Extensive phylogenetic and metagenomic investigations have consistently identi ed A. muciniphila as one of the top 20 most prevalent species in the human gut.[4][5][6][7][8][9] Within the rst year after birth, A. muciniphila can establish stable colonization in the gut, reaching levels similar to those found in healthy adults, with abundance gradually declining in the elderly.3,4,10 A. muciniphila has also been detected in human milk, indicating its transfer from mothers to infants through breastfeeding. 11 is nding explains its presence in the gastrointestinal tract of newborn infants.3,4 During this early developmental stage, A. muciniphila exhibits successful colonization in the gastrointestinal tract due to its active acid resistance system and capacity to degrade human milk oligosaccharides in the stomach of newborn infants.3,12 A. muciniphila's inhabitance of the human gut microbiome (GM) during critical stages of human development may lead to A. muciniphila playing an important role in neurodevelopment via the gut-brain axis, which facilitates bidirectional interactions between the brain and the gut.13 The GM, including A. muciniphila, has been signi cantly associated with neuropsychiatric disorders, notably depression and anxiety.14 Using a mouse model of depression induced by chronic restraint stress (CRS), researchers found that A. muciniphila treatment signi cantly ameliorated depressive-like behavior in the CRS-exposed mice. 15 urther, A. muciniphila supplementation could relieve depression-like symptoms and aggravation of colitis in recipient mice.16 These relationships may also exist in humans, though further epidemiologic investigation is needed.
Even at low levels, increased metal concentrations during childhood and adolescence have been associated with an increased risk of depression. 17,18Prenatal exposures to lead (Pb) and manganese (Mn) have also been associated with mid-childhood to adolescent internalizing symptoms. 19Exposure to multiple metals simultaneously may further strengthen this association.For instance, combined exposure to organochlorine and metal mixtures has been further associated with anxiety/depressive symptoms during adolescence. 19While this evidence suggests a link between prenatal exposure to individual metals and some mixtures, not all components within a mixture are necessarily equally important. 20aving certain levels of exposure to speci c combinations of few metals, or metal cliques, may have stronger associations than either individual metals or combinations of many metals. 21Metal cliques are a unique measure to assess associations because they lie in between individual components and mixtures of all components.However, few, if any, environmental epidemiology investigations have focused on metal cliques.
3][24][25] We previously found that higher prenatal lead exposure reduced the diversity and abundance of bene cial taxa within the GM in children during mid-childhood (9-11 years) 26, 27 Although metals have been linked to both alterations of the GM and increased depression symptoms, the speci c role of A. muciniphila in modifying the association between metal exposure and depression has yet to be examined.This study, therefore, investigates the role of A. muciniphila in modifying the association between prenatal metal clique and depressive symptoms in late childhood.

Study Population
The study sample comes from the Programming Research in Obesity, Growth, Environment, and Social Stressors (PROGRESS) cohort in Mexico City.PROGRESS cohort is a well-characterized ongoing longitudinal birth cohort that has been continuously funded since 2007. 28Table 1 provides the descriptive statistics of the 112 participants included in this study, strati ed by the presence/absence of A. muciniphila detected in 9-11-year-old gut samples.A. muciniphilia was present in 24% of participants.
Mothers of children with A. muciniphila exhibited a lower BMI of 26.33 kg/m 2 in pregnancy.The average t-scored Childhood Depression Inventory (CDI) was 52.97 (range: 40-81).On average, those without gut A. muciniphila had a higher CDI score (i.e., more depressive symptoms).For further analysis, we converted the relative abundance of A. muciniphila as a binary indicator of presence and absence to ward off any in uence of outliers.The Spearman correlation between the metal concentrations is presented in Supplementary Figure 1.Details on concentrations of prenatal metals at both trimesters (with strati cation by the presence of A. muciniphila) are shown in Supplemental Tables 1 and 2. We used an interpretable machine learning model called repeated holdout signed-iterated Random Forest (rh-SiRF), 27,29,30 along with a regression framework to identify metal cliques that are both predictive and associated with depression score (see Methods).We obtained a total of 123 unique two-component cliques from rh-SiRF, with only 3% of cliques having stability (frequency of occurrence) of more than 5% (see Supplemental Table 3 for the list of top 10 combinations).We chose the top three combinations since those formed a closed-looped network (Supplementary Figure 2 for a forced-directed graph of this clique).We identi ed a three-component metal clique composed of (1) high Zn in the 2 nd trimester (concentration greater than 20 th percentile of the sample), (2) low Co in the 3 rd trimester (concentration below 80 th percentile of the sample), (3) low Cr in the 2 nd trimester (concentration below 55 th percentile of the sample).This three-component metal-clique is essentially a binary indicator that speci es a subgroup of children (comprising almost 40.9% of the sample) using the interactions of a few metals.Next, this clique was used in a regression framework to estimate the associations with (log-tCDI scores.We used a matched-sampling strategy typically applied in causal inference analysis to obtain similar covariate distribution between children with or without A for improved inference.muciniphila. 31The assumption is that, given the covariates, this balancing approach can potentially create "exchangeable" groups of children with or without A. muciniphila such that they are hypothetically randomly assigned, and most importantly, the covariates did not confound the group assignment. 32We conducted the regression analysis on the covariate-balanced dataset, further controlling for covariates and confounders. The distributions of the log-tCDI were shown in Figures 2A and 2B However, this metal-clique was not correlated with the presence of A. muciniphila (Figure 2D), indicating that A. muciniphila is not a mediator between this metal-clique and childhood depression scores.However, the presence of A. muciniphila modi es this association.For 31.8% of children with no A. muciniphila, the metal-clique was strongly associated with increased depression score (β[95% CI]=0.11[0.05,0.18], P rand <0.0001), whereas, for children with A. muciniphila, this same metal-clique was weakly associated with decreased depression score in almost 9.1% children, although the association was not statistically signi cant (β[95% CI]=-0.05[-0.16,0.06], P rand <0.4).We further estimated the Spearman correlations between the components of this metal-clique, the overall indicator of the three-component metal-clique, and the absence of A. muciniphila (Figure 2B).Correlations between the metal components were minimal, which implies that correlation between metal concentrations did not have a signi cant effect in forming the clique, indicating a possibility of non-linear interaction.

Sensitivity Analyses
We conducted multiple sensitivity analyses to substantiate our results: (1) For the major associations (except the forest plots), we estimated randomization-based p-values (P rand ) (that are robust against any assumption of normality) by permuting each of the outcomes 10 6 times.The signi cant P rand values were far lower than the model-based p-values (Supplemental Table 4).( 2) Given the small sample size, we chose a minimal set of covariates to adjust in the models but incorporated techniques like covariatebalancing to obtain robust results (the covariate-balanced love plot is presented in Supplemental Figure 3).(3) Moreover, the presence of A. muciniphila remained strongly associated with a signi cantly decreased log-tCDI scores even on the non-covariate balanced dataset (beta=-0.10,95%CI=[-0.18,-0.01]).
(4) The directionalities of all the metal-clique associations (with and without the presence of A. muciniphila) remained unaltered while each of the thresholds was increased and decreased by ten percentiles (Supplemental Figures 4 and 5), implying greater robustness.(5) Results remained robustly similar when we binarized the t-scored CDI (>= 75 th percentile) and repeated the metal-clique associations (Supplemental Figures 6).

Discussion
In this study, we explored the modifying effect of A. muciniphila on the associations between prenatal exposure to a metal-clique and depressive symptoms in late childhood.Our results suggest that children with exposure to metal-clique of Zinc-Chromium-Cobalt during pregnancy had a higher CDI score in late childhood.In the absence of A. muciniphila in childhood GM, this metal-clique was strongly associated with higher depression symptoms in children.However, for children with A. muciniphila, this metal-clique was weakly associated with lower depression symptoms.This analysis provides the rst exploratory evidence that the presence of A. muciniphila likely attenuates the association between prenatal exposure to metals and depression in later childhood.
A. muciniphila is a symbiotic bacterium colonizing the intestinal epithelium's mucosal layer.This mucus layer comprises high-molecular-weight glycoproteins called mucins. 33A. muciniphila breaks down mucins, producing short-chain fatty acids (SCFAs) that facilitate the bacteria's colonization process and provide energy and neuromodulators for the host. 34These SCFAs contribute to the maturation of the immune and neurological systems. 346][37] Reduced A. muciniphila levels have been seen in mice and rats displaying depression-like behavior. 38,39In the FinnBrain Birth Cohort, the prevalence of Akkermansia was inversely associated with maternal postpartum depression symptoms. 40][43] A. muciniphila counters this by enhancing BDNF expression, fostering synaptic pathways, and reducing depression symptoms. 2,14,155][46] A. muciniphila positively in uences host 5-HT levels in the intestine through factors like its outer membrane protein Amuc_1100, which elevates intestinal 5-HT expression. 47,48When considering our ndings with those presented from these animal and human studies, there appears to be a consistent negative correlation between the presence and prevalence of Akkermansia and depressive behavior, with multiple potential biological mechanisms. 14This recurring pattern strongly suggests the potential utility of Akkermansia as a viable strategy to alleviate depression symptoms; however, further longitudinal intervention studies with large samples are needed. 14 found that presence of a prenatal metal-clique, including high Zn and low Cr in the second trimester and low Co in the third trimester, was associated with a higher depression index in children, and the association was reduced in children who had A. muciniphila in the GM.0][51][52] For example, Rokoff et al. show that prenatal exposure to Pb was found to be linked to increased anxiety symptoms during adolescence.In contrast, prenatal exposure to Mn was positively correlated with internalizing symptoms, particularly among girls from mid-childhood through adolescence. 19Gari et al. report that prenatal concentrations of micronutrients, Se and Zn, and neurotoxic metals, Pb and Hg, exert notable in uences on the neuropsychological development of children at the age of 7. 53 A previous study by our group found that a metal-microbial clique of high Zn in the second trimester, low Co in the third trimester, and high abundance of Bacteroides fragilis and Faecalibacterium prauznitzii in childhood was associated with increased depression scores. 21Co is a component of Vitamin B12, which may be associated with depression, 54,55 and has been previously associated with A. muciniphila. 56The results of our study in the context of these previous ndings suggest that in-utero exposure to metals could be particularly important in contributing to the development of anxiety and depression symptoms.
Exposure to metals can impact GM composition and function, especially during early development.The existing body of knowledge about the interactions between metals and Akkermansia in human physiology is limited, and exposures during the prenatal period appear entirely unexplored with Akkermansia.Our analysis did not indicate strong correlations between the prenatal metal-clique and A. muciniphila; however, previous research indicated that toxic metals like Cd, Pb, Cu, and Al reduced A.
8][59][60][61] Shen et al., found that higher childhood blood Mn may lead to lower mucin degradation and energy generation and is signi cantly associated with lower Akkermansiaceae. 23Their ndings indicate a potential association between metal exposure in childhood and Akkermansia abundance, but they did not nd an association with earlier exposures.Although there is limited evidence of interaction between Akkermansia in GM and metal exposure, there is evidence of potential biological mechanisms that may combine to in uence human health.Metal exposure may alter the relative abundance of Akkermansia within the GM or reduce its ability to produce SCFAs, 62,63 either of which may reduce Akkermansia's ability to communicate with the CNS and potentially in uence depression etiology.A. muciniphila also helps strengthen the epithelial barrier in the gut, 64 while metal exposures can negatively impact gut barrier function. 65Our nding of modi cation by A. muciniphila between metal cliques and depression may function through improved epithelial barrier strength.Alternative mechanisms may also exist, supporting the need for further investigation in this area.
While this study contributes to the growing body of evidence concerning the adverse link between metal exposure, depression, and human GM, some limitations must be acknowledged.The sample size limited our ability to make more robust conclusions due to a lack of power.Nevertheless, consistent associations across various sensitivity analyses and using causal-inference methods boost the inferences, even though some estimates did not reach statistical signi cance.Additionally, measuring prenatal metal exposure through maternal blood during pregnancy is suboptimal as it does not directly gauge fetal metal exposure.In the analysis, we did not control the models by any diet-related covariates due to a lack of information collected during the survey.Strengths of this study include the novel investigation of Akkermansia as a modi er of prenatal metal exposures and childhood depression.We performed robust statistical analysis, applying tools from current state-of-the-art machine-learning and causal inference literatures and using the presence of A. muciniphila instead of its relative abundance to minimize plausibility of measurement error.Our analysis of metal-cliques adds novel insight into combinations of metal exposures that are found in susceptible subgroups of the population, potentially making them more vulnerable to childhood depression.Findings from this study have translational potential, indicating A. muciniphila as an intervention avenue to help prevent depression in children with prenatal metal exposures.Additional future directions of this work include mediation analysis with depression measured later in adolescence and in vitro and animal experiments to help establish the biological plausibility of associations between metal exposures and A. muciniphila and mechanisms of modi cation.
This study suggests that the presence of A. muciniphila may attenuate the effect of prenatal exposure to a select metal-clique on childhood depression.Further observational, experimental, and translational investigation is needed to fully understand the occurrence, mechanisms, and potential interventions along this pathway.

Blood metal
During the 2 and 3 rd trimester visits (18.3 and 31.6 weeks of gestation, respectively), maternal blood samples were collected using standard venipuncture. 66All blood specimens were drawn using tubes free from trace metals and were stored at temperatures between 2°C and 6°C until analysis.Metal concentrations, including lead (Pb), arsenic (As), cadmium (Cd), chromium (Cr), zinc (Zn), selenium (Se), antimony (Sb), copper (Cu), cesium (Cs), cobalt (Co), and manganese (Mn), were determined using the Agilent 8800 ICP Triple Quad (ICP-QQQ) in MS/MS mode at the trace metals laboratory of the Icahn School of Medicine at Mount Sinai.Measurements were taken in ve replicates and reported as an average.For the purpose of QC, all lab recovery rates by this method were 90 to 110%, and inter-day and intra-day precision (given as %relative standard deviation) is less than 6% for samples with concentrations greater than the limit of quanti cation. 67The limits of detection for each metal were: 0.391 ug/L for As, 0.113 ug/L for Cd, 0.117 ug/L for Co, 0.901 ug/L for Cr, 0.122 ug/L for Cs, 1.862 ug/L for Cu, 0.442 ug/L for Mn, 0.377 ug/L for Pb, 0.159 ug/L for Sb, 0.665 ug/L for Se, and 5.053 ug/L for Zn (See Midya, 2024 for more details). 21kermansia muciniphila measurement Details of the gut microbiome sampling and processing procedures have been previously published. 26rie y, participants were recruited during the 9-11-year PROGRESS study visit.Stool samples were collected by participants at home, processed using the Fast method at the clinic in Mexico City, and promptly stored -70°C.Frozen samples were then shipped to the Microbiome Translational Center at Mount Sinai for microbiome sequencing analysis.The subsequent steps involved the processing and sequencing of the samples in two distinct batches: the rst batch containing 50 samples and the 2 nd containing 73 samples.For shotgun metagenomic sequencing, the NEBNext DNA Library Prep kit was used, and puri ed DNA sequencing using the Illumina HiSeq platform.The sequencing reads underwent trimming using Trimmomatic. 68To eliminate any human-associated reads, alignment to a human reference genome was conducted using bowtie2. 69The remaining reads underwent analysis with MetaPhlAn2 and StrainPhlAn to ascertain detailed microbial taxonomy down to the species and strain levels. 70,71Results of the sequencing process can be found in Eggers, et al, 2023.Only children with no antibiotic use within the past month were included in this sub-study.For this analysis, we used the presence/absence of A. muciniphila from this sequencing data.

Depressive Symptom Measurement
As a part of the neurobehavioral follow-up, childhood depressive symptoms were assessed using the Childhood Depression Inventory (CDI) 2 Self-Report Short Version at ages 9-11 years. 72Part of the CDI form was administered to mothers about their children, and part of the form was administered directly to the children.The CDI is a questionnaire appropriate for use with participants aged 7-17 years and validated in Spanish. 73Items are scored on a scale normalized from 0 to 100, with higher scores indicating worse depression symptoms.

Covariate data
We followed a similar set of minimal covariates in line with our previous work. 27,29,30Potential confounding variables used in this analysis encompassed a range of factors identi ed in previous literature and prioritized using DAGs.These included the child's reported sex at birth, age at the time of stool sample collection, the socioeconomic status (SES) of the mother during pregnancy, the mother's age at childbirth, the mother's 2 nd -trimester body mass index (BMI) while pregnant, and the batch of microbiome analysis (two batches).The mother's height and weight were measured during the 2 nd trimester using a professional digital scale and a stadiometer.From these measurements, the BMI was calculated and subsequently treated as a continuous covariate for regression analyses.The 1994 Mexican Association of Intelligence Agencies Market and Opinion (AMAI) rule was employed to assess maternal socioeconomic status during pregnancy.This classi cation system places families into six levels based on responses to 13 questions concerning household characteristics.As most families in the study belonged to the low to middle SES bracket, these six categories were consolidated into three broader classi cations: lower, middle, and higher. 74All of these covariates were adjusted for in the statistical models.This sub-study utilized convenience sampling due to proximity to study location, availability of mothers and their children at a given time, or their willingness to participate in the study.
Although there is potential selection bias and residual confounding bias, we utilized advanced causal inference techniques to address such issues to the best of our ability.Our analyses utilized covariatebalancing techniques and negative control outcomes to address any systematic and potential selection and residual confounding biases.

Statistical Analysis
Statistical analyses were conducted in R (version 4.2.3).The Pearson correlation coe cient was used to estimate the correlation between prenatal metal exposures.The outcome, t-scored CDI, was logtransformed (log tCDI) for all the main analyses (later in the sensitivity analyses, similar results were replicated with the non-log-transformed t-scored CDI).Some covariates (mother's age and child's age at the time of stool collection) included less than 5% missing values; therefore, under the assumption of missing at random, we imputed the missing values using predictive mean matching by the multiple imputation chained equations as implemented in the "MICE" R package. 75A false discovery rate was used to adjust for multiple comparison errors.The statistical analysis was conducted in three stages.
First, we estimated the association with each individual metal and log-tCDI using linear regression models.The results were presented through a forest plot with beta estimates and corresponding 95%CI.
Second, we estimated the association between the presence of A. muciniphila and log-tCDI using linear regression.Third, using a combination of interpretable machine-learning algorithm and regression-based framework, we identi ed the most frequently occurring metal-cliques and then estimated their associations with log-tCDI.
Although the details can be found in previous works, we present and discuss this method comprehensively for interpretability and further replicability. 27,29,30A metal-clique is an indicator of a multi-ordered combination of metals.For example, consider a three-metal combination consisting of metals A, B, and C, denoted as A+B+C-.Here, the metal-clique A+B+C-implies higher concentrations of metals A and B (above certain thresholds) and a lower concentration of metal C in the sample.This binarized indicator form of metal-clique A+B+C-denotes an underlying sub-sample (or subgroup) satisfying the conditions of the clique.We used the repeated holdout signed-iterated Random Forest (rh-SiRF), 27,29,30 treating the concentrations of metal exposures during both the second and third trimesters as predictors and log(t-CDI) as the outcome.7][78] These predictive combinations of metals were chosen following the branches in the decision trees.Moreover, instead of searching for all possible metal combinations (231 two-components, 1540 three-components, for example), rh-SiRF nds the most frequently occurring combinations on the decision path.Further, bagging and repeated-holdout stages were introduced to estimate the "stability" of the discovered combinations.The algorithm was repeated 500 times on a training/test data partitioning of 60%/40%, with 250 bootstraps implemented in each iteration.From the list of most stable metal combinations, we chose the top three.We presented a closed-loop network of this combination through the Fruchterman-Reingold Layout 79 implemented through the igraph package in R. 80,81 Finally, this combination is transformed into a binarized indicator using a quantile-based threshold-nding algorithm.A schematic of this algorithm and R code with illustrations on a simulated dataset is provided on GitHub (https://github.com/vishalmidya/MiCA-Microbial-Co-occurrenceAnalysis/blob/main/MiCA-vignette.md).
For improved inference, we used a matched-sampling strategy typically applied in causal inference analysis to obtain similar covariate distribution between children with or without A. muciniphila. 31The assumption is that, given the covariates, this balancing approach can potentially create "exchangeable" groups of children with or without A. muciniphila such that they are hypothetically randomly assigned, and most importantly, the covariates did not confound the group assignment. 32Due to the small sample size and to prevent greater sample loss due to covariate-balancing, we used a subclass matching procedure with the propensity score as implemented in the R package "MatchIt". 82This approach uses propensity scores based on all the covariates to classify participants into subclasses, which were then weighted to balance the in uence of the covariates for participants with vs. without A. muciniphila.We used love plots of the differences in standardized means in covariates to examine the suitability of balancing. 32All the regression analyses were based on this covariate-balanced matched sample.We further adjusted all the models with the previously described covariates.Figures 1A and 1B show associations (beta estimates and 95% CIs) with metal exposures in the 2 nd and 3 rd trimesters, respectively.The dotted vertical line denotes the null association. Figure 1C shows an association between the presence of A. muciniphila and Childhood Depressive Symptoms.t-CDI: t scored Child Depression Index.
Figure 2 shows the distribution and the association with prenatal metal-clique and log-tCDI scores and the effect modi cation by the absence of A. muciniphila in childhood gut microbiome.Figure 2A shows the distribution of log-tCDI scores for children with the metal-clique (and with/without A. muciniphila).Figure 2B shows the distribution of log-tCDI scores for children without the metal-clique (and with/without A. muciniphila).Figure 2C shows the beta coe cients and 95% CIs for the association with prenatal metalclique and log-tCDI scores and the effect modi cation by the absence of A. muciniphila; the proportions of the sample characterized by the clique are shown in brackets on the y-axis.Figure 2D shows the Spearman correlations among the components of the metal-clique (individual metal concentrations), the metal-clique, and the indicator for the absence of A. muciniphila among the 112 PROGRESS children.t-CDI, t scored Child Depression Index.
for children with (and without) the metal clique and with (without) A. muciniphila.Children without the metal clique (and with/without A. muciniphila) have a similar distribution of log-tCDI.In contrast, the distributions of children with the metal clique differ drastically with or without A. muciniphila.The three-component metal-clique of high Zn, low Cr in the 2 nd trimester, and low Co in the 3 rd trimester was signi cantly associated with an increased depression score (β[95% CI]=0.08[0.02,0.13], P rand <0.0001) (Figures 2C).
(6) We used a negative control outcome -having a pet (at the time of microbial sample collection), which might have similar sources of potential selection bias but would not be causally related to the identi ed metal-clique.In the association with metal-clique, we found a null association with different directionality (OR[95% CI]:0.7[0.2,2.5]) between metal-clique and having a pet, strengthening the possibility of minimal selection and residual confounding biases.
ongoing prospective birth cohort study conducted in Mexico City, Mexico.The study enrolled 948 women a liated with the Mexican Social Security Institute (IMSS) during early pregnancy, and closely tracked the development of the infants during their early years, with assessments every six months initially and later biannually.Comprehensive longitudinal follow-up included surveys, physical examinations, and psychological/behavioral evaluations.Biological samples from mothers and children, including blood, were collected and archived at each visit.Additionally, a convenience sampled subset of participants (n = 123) contributed stool samples when the children reached ages 9-11 years.26Of the 123 participants with stool samples, 112 also had complete outcome data.The research protocols for both the main PROGRESS study and its microbiome sub-study underwent thorough review and were granted approval by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai in New York and the National Institute of Public Health in Cuernavaca, Mexico.

Table 1 .
Descriptive statistics of covariates, and depression scores from 9-11 years of age PROGRESS participants included in this study (n = 112).