Evidence map›Paper›PMID 36191488›Full record

ArticleEnvironment international2022

Biomarkers of maternal lead exposure during pregnancy using micro-spatial child deciduous dentine measurements.

Lucia Gerbi, Christine Austin, Nicolo Foppa Pedretti, Nia McRae, Chitra J Amarasiriwardena, Adriana Mercado-García, Libni A Torres-Olascoaga, Martha M Tellez-Rojo, Robert O Wright, Manish Arora and 1 more

Open access · goldAbstract read
In one paragraph

Article in Environment international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 14 citations in OpenAlex.

  1. An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 2 countries.

Lucia GerbiDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Christine AustinDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Nicolo Foppa PedrettiDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Nia McRaeDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Chitra J AmarasiriwardenaDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Adriana Mercado-GarcíaNational Institute of Public Health (INSP), Cuernavaca, Mexico.
Libni A Torres-OlascoagaNational Institute of Public Health (INSP), Cuernavaca, Mexico.
Martha M Tellez-RojoNational Institute of Perinatology (INPer), Mexico City, Mexico; National Institute of Public Health (INSP), Cuernavaca, Mexico.
Robert O WrightDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Manish AroraDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Colicino ElenaDepartment of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States. Electronic address: elena.colicino@mssm.edu.
Icahn School of Medicine at Mount Sinai · USInstituto Nacional de Salud Pública · MXInstituto Nacional de Perinatología · MX

Funding

Statistical Services and Methods Development ResourceU2CES026555 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2015 to 2025
$26.4M
The Mount Sinai Transdisciplinary Center on Early Environmental ExposuresP30ES023515 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Robert O Wright · 2014 to 2026
$21.3M
Untargeted Analysis ResourceU2CES030859 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ARORA, MANISH · 2019 to 2025
$8.2M
Early Warning Systems for Childhood and Adult DisordersR35ES030435 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Manish Arora · 2020 to 2026
$6.7M
Organic-metal mixtures and neurodevelopmentR01ES026033 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Manish Arora, Robert O Wright · 2016 to 2026
$6.1M
Early-life metal exposures, mitochondrial heteroplasmy, and child antibody response to vaccinationR01ES034521 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Elena Colicino, Todd Jusko · 2022 to 2026
$3.0M
Air Particulate Pollution and Stress: Effects and Mechanisms for Long-term Maternal Obesity RisksR01ES032242 · NIEHS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI COLICINO, ELENA, WU, HAOTIAN · 2020 to 2024
$2.2M
Determining the Neurodevelopmental Effects of Transition from Breastfeeding to Infant Formula using a Novel BiomarkerR00HD087523 · NICHD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI AUSTIN, CHRISTINE · 2018 to 2020
$695k
NICHD NIH HHS R00 HD087523NIEHS NIH HHS P30 ES023515NIEHS NIH HHS R01 ES026033NIEHS NIH HHS R01 ES032242NIEHS NIH HHS R01 ES034521NIEHS NIH HHS R35 ES030435NIEHS NIH HHS U2C ES026555NIEHS NIH HHS U2C ES030859
6 · The paper itself

Abstract

backgroundLead is a toxic chemical of public health concern, however limited biomarkers are able to reconstruct prior lead exposures in early-life when biospecimens are not collected and stored. Although child tooth dentine measurements accurately assess past child perinatal lead exposure, it has not been established if they reflect maternal exposure in pregnancy.

aimTo assess the prenatal relationship between child tooth dentine and maternal blood lead measurements and to estimate maternal lead exposure during the 2nd and 3rd trimesters of pregnancy from weekly child dentine profiles.

methodsWe measured early-life lead exposure in child tooth dentine and maternal blood from 419 child-mother dyads enrolled in the Programming Research in Obesity, Growth, Environment and Social Stress (PROGRESS) cohort. We employed the Super-Learner algorithm to determine the relationship of dentine lead data with maternal blood lead concentrations and to predict maternal lead from child dentine lead data in blinded analyses. We validated and quantified the bias of our results internally.

resultsMothers had moderate blood lead levels (trimesters: 2nd = 29.45 ug/L, 3rd = 31.78 ug/L). Trimester-averaged and weekly child dentine lead measurements were highly correlated with maternal blood levels in the corresponding trimesters. The predicted trimester-specific maternal lead levels were significantly correlated with actual measured blood values (trimesters: 2nd = 0.83; 3rd = 0.88). Biomarkers of maternal lead exposure discriminated women highly exposed to lead (>mean) with 85 % and 96 % specificity in the 2nd and 3rd trimesters, respectively, with 80 % sensitivity. DISCUSSION: Weekly child dentine lead levels can serve as biomarkers of past child and maternal lead exposures during pregnancy.

Indexed as

LeadMaternal ExposureBiomarkersCohort StudiesDentinFemaleHumansPregnancyBiomarkersLeadBlood lead levelsMachine learningPregnancyPrenatal lead exposureSuper-Learner algorithmTooth dentine lead levels

Identifiers

PMID36191488
PMCPMC9576006
OpenAlexW4296195813

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.