ArticlePDF Available

An Exploratory Investigation of Organic Chemicals Detected in Baby Teeth: Differences in Children with and without Autism

Authors:

Abstract and Figures

Autism spectrum disorder (ASD) is a behaviorally defined neurodevelopmental disorder characterized by deficits in language, communication, and social function with an estimated prevalence rate of between 1 in 30 and 44 U.S. births. Gene/environment (G × E) interactions are widely regarded as the most probable explanation for idiopathic ASD, especially because some genes are selectively targeted by various environmental xenobiotics. Because deciduous teeth are a likely biomarker of in utero exposure, the present study investigated if the quantity of chemicals found in deciduous teeth differs between children with and without ASD. Twenty-two deciduous teeth from children with ASD and 20 teeth from typically developed children were prepared and analyzed using THE Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometer (GC × GC-TOF MS) with ChromaTOF version 23H2 software and Agilent 7890 gas chromatograph. The autism sample had significantly more chemicals in their teeth than the typical developing sample (99.4 vs. 80.7, respectively) (p < 0.0001). The majority of chemicals were identified as phthalates, plasticizers, pesticides, preservatives, or intermediary solvents used in the production of fragranced personal care or cleaning products or flavoring agents in foods. The known toxic analytes reported in this study are likely biomarkers of developmental exposure. Why there were greater concentrations of toxic chemicals in the teeth that came from children with ASD is unclear. A further understanding of the cavalcade of multiple biological system interactions (Interactome) could help with future efforts to reduce risks. Notwithstanding, the avoidance of pesticides, plastics, and scented personal care products may be warranted under the precautionary principle rule.
Content may be subject to copyright.
Citation: Palmer, R.F. An Exploratory
Investigation of Organic Chemicals
Detected in Baby Teeth: Differences in
Children with and without Autism. J.
Xenobiot. 2024,14, 404–415. https://
doi.org/10.3390/jox14010025
Academic Editor: Ramji K. Bhandari
Received: 14 December 2023
Revised: 29 February 2024
Accepted: 12 March 2024
Published: 14 March 2024
Copyright: © 2024 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
An Exploratory Investigation of Organic Chemicals Detected in
Baby Teeth: Differences in Children with and without Autism
Raymond F. Palmer
Department of Family and Community Medicine, School of Medicine, University of Texas Health Science Center
San Antonio, San Antonio, TX 78229, USA; palmerr@uthscsa.edu; Tel.: +1-210-827-7681
Abstract: Autism spectrum disorder (ASD) is a behaviorally defined neurodevelopmental disor-
der characterized by deficits in language, communication, and social function with an estimated
prevalence rate of between 1 in 30 and 44 U.S. births. Gene/environment (G
×
E) interactions are
widely regarded as the most probable explanation for idiopathic ASD, especially because some genes
are selectively targeted by various environmental xenobiotics. Because deciduous teeth are a likely
biomarker of in utero exposure, the present study investigated if the quantity of chemicals found in
deciduous teeth differs between children with and without ASD. Twenty-two deciduous teeth from
children with ASD and 20 teeth from typically developed children were prepared and analyzed using
THE Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometer
(GC ×GC-TOF MS)
with ChromaTOF version 23H2 software and Agilent 7890 gas chromatograph. The autism sample
had significantly more chemicals in their teeth than the typical developing sample (99.4 vs. 80.7,
respectively) (p< 0.0001). The majority of chemicals were identified as phthalates, plasticizers, pes-
ticides, preservatives, or intermediary solvents used in the production of fragranced personal care
or cleaning products or flavoring agents in foods. The known toxic analytes reported in this study
are likely biomarkers of developmental exposure. Why there were greater concentrations of toxic
chemicals in the teeth that came from children with ASD is unclear. A further understanding of
the cavalcade of multiple biological system interactions (Interactome) could help with future efforts
to reduce risks. Notwithstanding, the avoidance of pesticides, plastics, and scented personal care
products may be warranted under the precautionary principle rule.
Keywords: environmental toxic exposure; pesticides; deciduous teeth; autism
1. Introduction
Autism spectrum disorder (ASD) is a behaviorally defined neurodevelopmental dis-
order characterized by deficits in language, communication, and social function [
1
]. The
most recent prevalence estimates range from 1 in 30 to 44 U.S. births [13].
Currently, gene/environment (G
×
E) interactions are widely regarded as the most
probable explanations for idiopathic ASD [
4
6
]. With genes that are selectively targeted
by various environmental xenobiotics [
7
], the interface between the emerging genome and
exposome sciences complicates the understanding of interactions between an individual’s
predisposed biology and multiple environmental exposures [68].
The U.S. Toxic Substances Control Act (TSCA, 1976) considered approximately
62,000 chemicals
that were not subject to testing or regulation unless proven to “present(s)
an unreasonable risk of injury to health or the environment [
9
,
10
]”. Now, the number of
untested chemicals is over 83,000 and growing [
10
]. Environmental health monitoring
studies reveal multiple scientific challenges in predicting and preventing disease. As such,
there is a widely held consensus for the reform of outdated TSCA laws [1114].
The National Health and Nutrition Examination Survey (NHANES) confirms that
the exposure of the U.S. population to toxic environmental chemicals has increased over
J. Xenobiot. 2024,14, 404–415. https://doi.org/10.3390/jox14010025 https://www.mdpi.com/journal/jox
J. Xenobiot. 2024,14 405
the last 40 years [
15
] and that exposures to ubiquitous neurotoxins affect women of child-
bearing age and their infants [
16
21
]. The developing fetus is particularly vulnerable to
adverse effects from toxic environmental chemical exposures, including polycyclic aromatic
hydrocarbons, phthalates, pesticides, organophosphates, pyrethroids diester phthalates,
and polybrominated diphenyl ethers—there is a broad range of studies in the literature and
a clear link between in utero chemical exposures, immune system dysregulation, endocrine
disruption, and impaired childhood neurodevelopment, including autism [2232].
While the aforementioned studies measured current exposure levels, no biomarkers
have existed to retrospectively assess early exposure to many organic chemicals, including
organophosphate pesticides and diester phthalates, since the parent compound is rapidly
metabolized, and the metabolites are quickly excreted.
Measuring Exposure: blood, urine, saliva, or hair has often been used to assess the
risk of disease by comparing the concentrations of some environmental toxic substances
between affected and unaffected individuals [
33
,
34
] While this has been a useful approach,
these bio-samples are only measures of recent exposure and cannot inform about distant
past or developmental exposures, particularly during critical neurodevelopmental periods.
Alternatively, deciduous teeth provide a historical record of exposure with the potential to
circumvent the limitation of biomarkers commonly used in epidemiological studies, such
as blood and urine, which only capture current exposures in a child [35].
The use of deciduous tooth crowns has served as a biomarker of early developmental
exposure [
35
,
36
], including a biomarker that showed atypical fetal inflammatory regulation
among those with ASD [
37
]. The mineralization of primary teeth begins prenatally between
14 and 16 weeks’ gestation and concludes postnatally from 1.5 to 3 months for incisors,
9 months for canines, and 5.5 to 11 months for molars [
38
]. Exogenous and endogenous
organic chemicals or their metabolites circulating in the bloodstream absorb into the
developing tooth and remain stored thereafter. The tooth concentrations of these analytes
are likely biomarkers of exposure during the period of tooth crown formation, although
a fraction likely reflects later post-natal exposure [3537].
It has been demonstrated that metals in circulation, which are present during the
period of tooth formation, become incorporated into forming dental tissue and are stored in
the mineral component of teeth [
39
]. Several studies demonstrate the advantage of primary
tooth crown analysis for determining exposures that might be related to various disease out-
comes. Indeed, the conclusions from a 2010 NATO workshop on the effects of heavy metal
pollution on child development recommend that depositions found in teeth can serve as
a valuable
tool in relating heavy metal pollution to childhood
development outcomes [40].
The vast majority of studies using deciduous teeth have been on measuring heavy
metal concentrations. Less focus has been placed on the assessment of organic toxicant expo-
sures such as pesticides, plastics, or pharmaceuticals. Our laboratory has previously discov-
ered, with replication in two cohorts, that analgesics (acetaminophen, ibuprofen), which are
specific metabolites of organophosphate pesticides and phthalates (chlorpyrifos, diazinon,
dibutyl phthalate, and DEHP), and the insect repellant DEET, remain stored in pulverized
deciduous molars in typically developing children at levels detectable by Liquid Chro-
matography Tandem Mass Spectrometry (LC/MS/MS) [
41
,
42
]. Prior to this, semi-volatile
organic chemicals (SVOCs) had not been identified in teeth. While methods with sectioned
teeth have recently been perfected for the timing of SVOC
detection [35,36,43]
, in this study,
the precise timing of exposure was not determined. This study used
GC ×GC-TOF MS
methods with pulverized teeth to identify new chemicals and to quantify the amounts
between children with and without ASD.
The present exploratory study investigated an untargeted approach to identify new
SVOC chemicals and determine if chemical exposure patterns differed between children
with ASD and typically developing children. Our preliminary hypothesis was that more
harmful chemicals would be present in the teeth from children with autism compared to
those without autism.
J. Xenobiot. 2024,14 406
2. Materials and Methods
2.1. Sample and Recruitment
We established a tooth repository of over 900 teeth with survey information on de-
mographics, environmental exposures, and developmental medical histories. Our tooth
biorepository (from 280 mothers with 363 total children) mostly consisted of teeth donated
by the families of children with ASD currently residing throughout the US who participated
in the Interactive Autism Network (IAN). The IAN network is the nation’s largest online
autism research forum, where over 43,000 families complete comprehensive surveys and
participate in various research studies [44,45].
The inclusion criteria for IAN participants are parents with children under 18 years of
age who have been diagnosed with ASD by a health professional. The diagnosis of ASD in
the IAN database has been clinically validated in a subsample of participants [
44
] as well
as verified by a review of parent- and professional-provided medical records [
45
]. This
work reports the 98% concordance of validated ASD diagnoses with parental self-report,
thus supporting the viability of the centralized database recruitment model. In a recently
completed project to verify diagnosis in our non-IAN recruits, a certified diagnostician
used a subset of our sample and confirmed that either parent provided (1) medical records
and/or diagnostic workups that had been previously completed by a qualified health
professional, or (2) an ADIR interview was conducted by phone if verifiable records could
not be obtained.
In the current study, 42 teeth were randomly selected from our repository (22 from
children with ASD and 20 from typically developing children). The careful selection of
age–gender-matched controls for each case was used. Each participant donated between
1 and 10 teeth. Every attempt was made to match tooth types (molars, incisors, canines)
between cases and controls. We excluded the use of teeth of children whose mothers
reported having children with various other medical or neurological conditions, including
ADHD, and retained only those with ASD and those that were pure controls (e.g., typically
developing children with no reported medical, emotional, or psychological comorbidities).
All sample collection was approved by the University of Texas Health Science Internal
Review Board (#HSC 20110313H, approved 13 May 2011).
2.2. Laboratory Methods
Teeth were prepared and analyzed as described by Camann et al. [
40
] at the Southwest
Research Institute (SWI) in San Antionio, TX, USA. Any attached roots were severed
from the tooth crown at the cemento-enamel junction with a heatless wheel. The pulp
was removed from the pulp chamber with a drill bit. The crown was gently swirled in
dichloromethane (DCM), and the wash was retained as a quality measure to evaluate
external tooth contamination. Each tooth crown was pulverized to a fine powder with
a clean mortar and pestle, and the powder was weighed.
A 25 mg aliquot of each tooth powder sample was spiked with 1-methylnaphthalene-
d10, p-terphenyl-d14, and di-n-pentyl phthalate-d4 as extraction surrogates and was ex-
tracted in 1 mL of DCM for 30 min in a water bath sonicator, before being cooled with
ice, and the extract was concentrated to 250
µ
L. A matrix blank consisting of 25 mg of
pulverized kiln-fired synthetic hydroxyapatite and a solvent blank were prepared and
extracted along with each extraction batch of powdered tooth aliquots.
Prior to analysis, tooth extracts and the matrix and solvent blanks were spiked with
isotopically labeled internal standards, including 1,4-dichlorobenzene-d8, acenaphthene-
d10, anthracene-d10, chrysene-d12, and perylene-d12. Analysis was performed on a LECO
Pegasus 4D Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometer
(GC
×
GC-TOF MS) with Chromatof software and the Agilent 7890 gas chromatograph.
The instrument was fitted with two chromatographic columns of distinct phases connected
in series.
GC
×
GC-TOF MS operates similarly to a one-dimensional GC-MS, except there are
two analytical gas chromatography columns connected in series. As the compounds elute
J. Xenobiot. 2024,14 407
from the first column, they pass through a modulator which acts to trap and release the
compounds onto the dissimilar, shorter second column. A Time-of-Flight analyzer is used
to provide the required high speed needed to define the chromatographic peaks, which are
rendered narrower by the modulator. The first column, Restek Rxi-1MS (
250 µm×0.25 µm
,
30 m), separates the compounds based on their boiling point, while the second column,
Restek Rxi-17SilMS (Centre County, PA, USA) (180
µ
m
×
0.18
µ
m, 1.3 m), performs an
additional separation based roughly on polarity and double bond equivalents. This or-
thogonal technique results in the high-resolution chromatographic separation of com-
pounds, resulting in fewer interferences and higher-quality mass spectra over a traditional
one-dimensional GC/MS.
For preliminary screening, the National Institute of Standards and Technology (NIST)
mass spectral library was used for peak identification. The detections of potential interest
based on library searching were compared to the matrix and solvent blanks; retained
detections are present at a level at least 10
×
greater than the solvent and matrix blanks.
The quality of the NIST identifications was reviewed by mass spectral experts to create
a list of candidate compounds for retention time confirmation. Up to 40 analytical stan-
dards for the candidate compounds were procured through our existing inventory or
purchased from commercial vendors. The retention times and mass spectra of the an-
alytical standards were compared to those measured in the tooth extracts. Candidate
compounds matching the corresponding standard in both the retention time and mass
spectrum were qualitatively confirmed and subsequently quantified in the teeth using
linear regression. High-Performance Liquid Chromatography Quadrupole Time-of-Flight
(HPLC/qTOF) Mass Spectrometry was also used in the event that additional confirmation
of a compound’s accurate mass is required. The relevance of the aforementioned untargeted
analytic approach and more specific details of the methods used in this paper have been
published elsewhere by the SWRI lab [46,47].
The product use categories of the identified chemicals were classified using
PubChem [48]
and other databases, including the Good Sense Company (https://www.thegoodscentscompany.
com) (accessed on 11 March 2024), U.S. Environmental Protection Agency Chemical As-
sessment Summary-Integrated Risk Information System (IRIS), and National Center for
Environmental Assessment (https://iris.epa.gov) (accessed on 3 December 2024).
2.3. Statistical Analysis
Comparisons of the chemical concentration between cases and controls were made
using t-tests with p< 0.05 as the critical cutoff for significance. The t-distribution is most
useful for small sample sizes, when the population standard deviation is not known,
or both.
2.4. Statistical Power
Sample sizes of 20–30 with a minimum of 12 for pilot studies have been recommended
by various research statisticians [
49
,
50
]. In this pilot study (n= 22 ASD group, and
n= 20
for the reference group), the power to detect a between-group medium effect size was
relatively low (power = 0.71, using two-tail, alpha = 0.05). However, there was 80% power
to detect a moderately large effect size (0.6).
Pilot/exploratory studies are performed prior to definitive trials to provide enough
evidence of their overall potential intervention benefits [
51
,
52
]. Schoenfeld [
53
] suggests
that preliminary hypothesis testing for efficacy could be conducted with a high type I
error rate (a false positive rate up to p< 0.25). Notwithstanding, in this study, we still
accept
a conservative
alpha of p< 0.05. Pursuant to avoid missing a true effect (Type II
error), we did not adjust for multiple comparisons. All analyses were performed with SAS
software V.9.4 [54].
J. Xenobiot. 2024,14 408
3. Results
Table 1shows the sample demographics. Due to initial matching, there were no
significant differences between the cases and controls for mothers’ age, child’s gender or
ethnicity, and household income. Therefore, covariate adjustment in the statistical analysis
was unnecessary.
Table 1. Sample demographics.
Cases (n= 22) Controls (n= 20)
p
Mean (SD) or % Mean (SD) or %
Mothers Age 40.4 (6.4) 41.1 (5.3) 0.77
Child Gender (% male) 80.1% 70.1% 0.67
Family Income
Less than USD 15,000 6.7% 18.5%
USD 15,000–USD 24,999 86.7% 44.4%
USD 25,000–USD 34,999 6.7% 11.1%
Not reported 0% 25.9% 0.61
Ethnicity
Hispanic 6.7% 22.2% 0.09
Non-Hispanic White 60.0% 44.4%
Other 26.7% 7.4%
Not Reported 6.6% 25.9%
A total of 11,971 chemicals were found, including unknowns without a suitable
library match. The average was 315 compounds per tooth. A total of 201 compounds of
interest were identified and confirmed as positive matches in Chemical Abstract Services
(CASs) libraries. In general, these were exogenous xenobiotic compounds with a high
identity confidence score. The first row in Table 2shows that the autism sample had
significantly more chemicals in these teeth than the teeth from the typically developing
sample (
99.4 vs. 80.7
, respectively) (p< 0.0001). The distribution is shown graphically
in Figure 1.
Of the 201 identified compounds, one-fourth of these chemicals (n= 54 or 27%) were
statistically distinguishable between teeth from those with and without autism. Table 3
shows the means, standard deviation, and minimum/maximum range of each of these
chemicals. The overwhelming majority of these chemical concentrations were higher
in the teeth from the autism sample. There were only four chemicals where the teeth
of typically developing children were higher. These were Limonene (a citrus flavoring
or fragrant agent), Benzothiazole (antimicrobial), Butyl benzoate (antimicrobial), and
Butylated Hydroxytoluene (preservative/food additive and antioxidant).
Table 2. Total number of chemicals and total micrograms/grams (
µ
g/g) between children with and
without ASD.
Autism Typically Developing Controls
p
Mean (SD) Min–Max Mean (SD) Min–Max
Total number of chemicals 99.41 (12.78) 75–129 80.65 (13.18) 62–115 0.0001
Total grams 4799.44 (2570.36) 1388.16–11,332.95 3192.90 (1603.39) 606.25–7707.95 0.01
J. Xenobiot. 2024,14 409
J. Xenobiot. 2024, 14, FOR PEER REVIEW 6
Figure 1. Distribution of the number of chemicals in the primary teeth of children with and without
autism. Solid lines represent the population distribution, and the dashed lines indicate the sample
distribution. Given they are a close match signies that the sample is suciently well represents the
population.
Of the 201 identied compounds, one-fourth of these chemicals (n = 54 or 27%) were
statistically distinguishable between teeth from those with and without autism. Table 3
shows the means, standard deviation, and minimum/maximum range of each of these
chemicals. The overwhelming majority of these chemical concentrations were higher in
the teeth from the autism sample. There were only four chemicals where the teeth of typ-
ically developing children were higher. These were Limonene (a citrus avoring or fra-
grant agent), Benzothiazole (antimicrobial), Butyl benzoate (antimicrobial), and Butylated
Hydroxytoluene (preservative/food additive and antioxidant).
Table 3. Chemicals (µg/g) that statistically dier between Autism cases and controls (55 out of
202).
Chemical
Autism Cases
Typically Developing Child Cases
Mean
(Std Dev)
Minimum
Maximum
Mean
Minimum
Maximum
Chem1
0.10
(0.17)
0.00
0.49
0.01
0.00
0.15
Chem2
0.32
(0.26)
0.00
0.99
0.11
0.00
0.77
Chem3
0.51
(0.60)
0.00
1.84
0.16
0.00
1.05
Chem4
1.97
(1.69)
0.00
7.43
0.77
0.00
5.62
Chem5
0.22
(0.32)
0.00
0.99
0.07
0.00
0.43
Chem6
1.08
(1.23)
0.00
5.13
0.30
0.00
2.00
Chem7
0.23
(0.27)
0.00
0.80
0.03
0.00
0.33
Chem8
1.49
(1.67)
0.17
7.72
0.25
0.00
1.47
Chem9
0.15
(0.24)
0.00
0.78
0.04
0.00
0.68
Chem10
15.23
(22.83)
0.00
49.71
2.36
0.00
47.24
Chem11
6.75
(6.12)
0.00
19.38
2.30
0.00
9.76
Chem12
87.56
(109.22)
0.00
538.23
27.17
0.00
116.99
Chem13
1.04
(0.87)
0.29
3.41
0.56
0.00
2.20
Chem14
0.40
(0.53)
0.00
2.36
0.16
0.00
1.44
Chem15
3.38
(2.88)
0.94
11.76
5.29
0.00
13.88
Number of chemicals
Typically
Developing
control (0)
autism (1)
Figure 1. Distribution of the number of chemicals in the primary teeth of children with and without
autism. Solid lines represent the population distribution, and the dashed lines indicate the sample
distribution. Given they are a close match signifies that the sample is sufficiently well represents
the population.
Table 3. Chemicals (
µ
g/g) that statistically differ between Autism cases and controls (55 out of 202).
Chemical
Autism Cases Typically Developing Child Cases
Mean (Std Dev) Minimum Maximum Mean (Std Dev) Minimum Maximum
Chem1 0.10 (0.17) 0.00 0.49 0.01 (0.03) 0.00 0.15
Chem2 0.32 (0.26) 0.00 0.99 0.11 (0.18) 0.00 0.77
Chem3 0.51 (0.60) 0.00 1.84 0.16 (0.32) 0.00 1.05
Chem4 1.97 (1.69) 0.00 7.43 0.77 (1.31) 0.00 5.62
Chem5 0.22 (0.32) 0.00 0.99 0.07 (0.15) 0.00 0.43
Chem6 1.08 (1.23) 0.00 5.13 0.30 (0.52) 0.00 2.00
Chem7 0.23 (0.27) 0.00 0.80 0.03 (0.09) 0.00 0.33
Chem8 1.49 (1.67) 0.17 7.72 0.25 (0.42) 0.00 1.47
Chem9 0.15 (0.24) 0.00 0.78 0.04 (0.16) 0.00 0.68
Chem10 15.23 (22.83) 0.00 49.71 2.36 (10.56) 0.00 47.24
Chem11 6.75 (6.12) 0.00 19.38 2.30 (3.36) 0.00 9.76
Chem12 87.56 (109.22) 0.00 538.23 27.17 (27.88) 0.00 116.99
Chem13 1.04 (0.87) 0.29 3.41 0.56 (0.49) 0.00 2.20
Chem14 0.40 (0.53) 0.00 2.36 0.16 (0.37) 0.00 1.44
Chem15 3.38 (2.88) 0.94 11.76 5.29 (3.18) 0.00 13.88
Chem16 0.24 (0.49) 0.00 1.87 1.41 (1.76) 0.00 6.18
Chem17 3.08 (2.12) 0.98 10.03 2.01 (1.63) 0.00 6.60
Chem18 45.60 (29.33) 0.00 111.20 12.20 (15.62) 0.00 61.78
J. Xenobiot. 2024,14 410
Table 3. Cont.
Chemical
Autism Cases Typically Developing Child Cases
Mean (Std Dev) Minimum Maximum Mean (Std Dev) Minimum Maximum
Chem19 1.37 (2.45) 0.00 10.89 0.21 (0.33) 0.00 1.00
Chem20 1.40 (1.31) 0.19 5.83 0.59 (0.53) 0.00 1.65
Chem21 3.32 (3.31) 0.58 15.02 1.86 (2.03) 0.00 8.58
Chem22 0.03 (0.09) 0.00 0.31 0.39 (0.77) 0.00 2.77
Chem23 1.17 (0.60) 0.33 2.90 3.45 (5.01) 0.41 19.98
Chem24 0.90 (0.97) 0.00 3.36 0.25 (0.95) 0.00 4.21
Chem25 1.22 (0.62) 0.60 2.97 0.83 (0.51) 0.00 2.11
Chem26 4.03 (5.63) 0.00 18.09 0.85 (1.57) 0.00 5.98
Chem27 1.19 (0.75) 0.00 2.80 0.15 (0.37) 0.00 1.40
Chem28 0.24 (0.29) 0.00 1.18 0.06 (0.15) 0.00 0.61
Chem29 0.10 (0.26) 0.00 1.09 0.00 (0.00) 0.00 0.00
Chem30 0.37 (0.45) 0.00 1.87 0.08 (0.16) 0.00 0.62
Chem31 8.26 (11.79) 0.00 30.80 0.26 (0.84) 0.00 3.27
Chem32 0.20 (0.22) 0.00 0.76 0.07 (0.16) 0.00 0.69
Chem33 7.90 (6.86) 0.00 30.65 1.53 (2.04) 0.00 6.28
Chem34 0.40 (0.45) 0.00 1.61 0.07 (0.17) 0.00 0.60
Chem35 13.55 (14.14) 0.43 55.87 3.81 (9.86) 0.00 44.85
Chem36 0.26 (0.24) 0.00 1.04 0.14 (0.19) 0.00 0.59
Chem37 104.19 (84.68) 40.70 364.51 34.96 (39.04) 0.00 149.21
Chem38 5.08 (12.02) 0.00 44.51 23.79 (27.90) 0.00 119.15
Chem39 7.55 (8.19) 1.94 40.46 3.77 (2.19) 0.46 9.13
Chem40 3.65 (3.16) 0.00 12.24 1.63 (1.92) 0.00 7.02
Chem41 3.49 (2.51) 0.00 11.00 0.92 (1.94) 0.00 8.27
Chem42 2.96 (7.63) 0.00 22.41 0.00 (0.00) 0.00 0.00
Chem43 6.90 (11.73) 0.67 57.12 2.16 (2.55) 0.00 11.11
Chem44 139.17 (103.90) 0.00 339.60 75.98 (117.41) 0.00 394.04
Chem45 5.73 (7.64) 0.86 35.25 1.46 (1.64) 0.00 6.26
Chem46 340.09 (485.55) 30.49 1966.46 59.84 (58.48) 4.53 244.78
Chem47 35.42 (28.77) 5.63 122.78 17.25 (29.99) 0.48 136.16
Chem48 3.16 (3.20) 0.38 12.29 1.43 (2.03) 0.00 8.99
Chem49 3756.79 (2500.01) 895.83 10,276.18 2643.87 (1612.12) 376.14 6898.29
Chem50 2.40 (2.86) 0.00 10.10 0.34 (0.67) 0.00 1.99
Chem51 6.31 (9.62) 0.32 44.91 0.99 (2.23) 0.00 9.99
Chem52 1.64 (4.51) 0.00 17.88 0.00 (0.00) 0.00 0.00
Chem53 1.59 (1.92) 0.00 9.14 0.41 (0.65) 0.00 1.90
Chem54 75.20 (51.88) 0.00 189.40 32.07 (39.70) 0.00 126.47
The bottom row of Table 2summarizes the results of Table 3, showing that teeth from
children with autism had statistically greater concentrations (
µ
g/g) than children without
autism (p< 0.01). Figure 2shows the graphic distribution. It can also be seen that the upper
J. Xenobiot. 2024,14 411
range (Max) of the number and concentration of chemicals is higher in the teeth from the
autism sample.
J. Xenobiot. 2024, 14, FOR PEER REVIEW 8
Figure 2. Distribution of micrograms (µg/g) of chemicals distinguishing children with and without
autism. Solid lines represent the population distribution, and the dashed lines indicate the sample
distribution. Given they are a close match signies that the sample is suciently well represents the
population.
The name and product usage of the 54 chemicals that were signicantly dierent be-
tween the 2 groups (reected in Ta ble 3) can be seen in Supplementary Ta bl e S1. The in-
spection of this table shows that the majority of chemicals are classied as phthalates,
plasticizers, pesticides/microbiocides, antimicrobials, preservatives, or intermediary sol-
vents used in the production of these compounds. Five of the chemicals were identified
by the Pesticide Action Network (PAN) as PAN Bad Actors. These pesticides are at least a
known or probable carcinogen, reproductive or developmental toxicants, a neurotoxic
cholinesterase inhibitor, a known groundwater contaminant, or a pesticide with high
acute toxicity. Many of the chemicals found in this study are primarily used as fragrance
compounds for personal care, cleaning products, or avoring agents in foods.
4. Discussion
Using an exploratory untargeted mass spectrometry approach, a broad array of
chemicals in children’s deciduous teeth were captured. Since the mineralization of pri-
mary teeth begins prenatally [38], both exogenous and endogenous organic chemicals or
their metabolites circulating in the maternal bloodstream absorb into the developing tooth
and remain stored thereafter. The tooth concentrations of the analytes reported in this
study are likely biomarkers of developmental exposure during the period of tooth crown
formation, although a fraction may also reect later post-natal exposure [35,36].
From the over 11,000 chemicals that were captured, there were 201 exogenous chem-
icals that were positive matches identied in the known chemical abstracts with Chemical
Abstract Services (CASs) as unique identifiers. One-fourth of those 201 chemicals (55 or
27%) were statistically distinguishable between teeth from those with and without autism.
Of those 55 teeth, all but 4 were signicantly elevated in the teeth of children with ASD.
Why there were more chemicals found in the teeth of ASD children remains unclear. One
plausible explanation may be due to group dierences in the ability to metabolize xeno-
biotics [55,56].
Overall, the teeth of children with autism had signicantly more and greater concen-
trations of chemicalsprimarily from pesticides and plastics/polymers often found in
0 = Typically
developing
1 = Autism
Number of micrograms
Figure 2. Distribution of micrograms (
µ
g/g) of chemicals distinguishing children with and without
autism. Solid lines represent the population distribution, and the dashed lines indicate the sample
distribution. Given they are a close match signifies that the sample is sufficiently well represents
the population.
The name and product usage of the 54 chemicals that were significantly different
between the 2 groups (reflected in Table 3) can be seen in Supplementary Table S1. The
inspection of this table shows that the majority of chemicals are classified as phthalates,
plasticizers, pesticides/microbiocides, antimicrobials, preservatives, or intermediary sol-
vents used in the production of these compounds. Five of the chemicals were identified
by the Pesticide Action Network (PAN) as PAN Bad Actors. These pesticides are at least
a known or probable carcinogen, reproductive or developmental toxicants, a neurotoxic
cholinesterase inhibitor, a known groundwater contaminant, or a pesticide with high
acute toxicity. Many of the chemicals found in this study are primarily used as fragrance
compounds for personal care, cleaning products, or flavoring agents in foods.
4. Discussion
Using an exploratory untargeted mass spectrometry approach, a broad array of chem-
icals in children’s deciduous teeth were captured. Since the mineralization of primary
teeth begins prenatally [
38
], both exogenous and endogenous organic chemicals or their
metabolites circulating in the maternal bloodstream absorb into the developing tooth and
remain stored thereafter. The tooth concentrations of the analytes reported in this study are
likely biomarkers of developmental exposure during the period of tooth crown formation,
although a fraction may also reflect later post-natal exposure [35,36].
From the over 11,000 chemicals that were captured, there were 201 exogenous chem-
icals that were positive matches identified in the known chemical abstracts with Chem-
ical Abstract Services (CASs) as unique identifiers. One-fourth of those 201 chemicals
(
55 or 27%
) were statistically distinguishable between teeth from those with and without
autism. Of those 55 teeth, all but 4 were significantly elevated in the teeth of children
J. Xenobiot. 2024,14 412
with ASD. Why there were more chemicals found in the teeth of ASD children remains
unclear. One plausible explanation may be due to group differences in the ability to
metabolize xenobiotics [55,56].
Overall, the teeth of children with autism had significantly more and greater concen-
trations of chemicals—primarily from pesticides and plastics/polymers often found in
food, cosmetics, or household products. This aligns well with the literature implicating
that these compounds increase the risk of autism. Pesticides and plastics are neurotoxins
and endocrine disruptors, from which systematic reviews suggest that gestational expo-
sure to pesticides is linked to autism risk [
57
59
]. Similarly, phthalate plasticizers have
been shown to affect neurodevelopment with adverse consequences, including autism and
ADHD risk [60,61].
The effects of xenobiotic exposure and its role in autism are rapidly evolving, and
gene/environment interactions are now considered a rich area of research [
62
64
]. This
is particularly relevant given the report by Carter and Blizard [
7
], who report that autism
genes are selectively targeted by environmental pollutants, including pesticides and phtha-
lates found in food, cosmetics, or household products.
This study is consistent with previous reports demonstrating that pesticides and
plastics are found in the teeth of children with autism [
41
,
42
]. While the exploratory results
of this study are preliminary and lack genetic information, our primary hypothesis was
supported and is aligned with the literature showing an increased risk of autism from
exposure to neurotoxicants and endocrine disruptors during development.
The genome has opened a pandora’s box of possibilities [
65
,
66
] in terms of interacting
environmental and biological risk factors that act to increase the risk of autism. The
resulting impact on gene expression and interactions between the Proteome, Metabolome,
and Microbiome [
65
,
67
] adds massive complexity. Furthermore, the interactive effect
of the exposome on these systems can be acknowledged, all coming together as a vast
Interactome [
68
,
69
]. While it will take time to fully understand these processes and how
they interact to mount prevention efforts and reduce the risk of ASD, it may not be necessary
to understand all the biological components to immediately mount risk reduction strategies.
The precautionary principle is a conservative decision rule used to reduce the risk of
disease when the risk is not completely known but has the potential for critical damage [
70
].
While some debate exists due to the principle’s vague definition [
71
], in many instances, it
seems relevant to take preventative action to prevent harm, even if the likelihood of harm is
uncertain [
68
]. A relevant example of autism includes the avoidance of artificial sweeteners
during pregnancy. Fowler et al. [
72
] have shown that aspartame in diet sodas and other
artificial chemical sweeteners is associated with a threefold risk for ASD and ADHD.
A mother’s chemical intolerance status may even further compound the risk of ASD [73].
Conclusions: In light of the findings from this study, the avoidance of pesticides,
plastics, and scented personal care products [
74
] may be warranted under the precautionary
principle rule. This is highly relevant for women of childbearing age who want to have
children. There is a need to share information with the lay public and among healthcare
professionals on how best to avoid toxic exposures in order to reduce the risk of neurological
impairment during development.
Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/jox14010025/s1, Table S1: Chemical function and product usage.
Funding: This study was funded in part by Autism Speaks Grant no. 8426, the Suzanne and Bob
Wright Trail Blazer award, granted to the first author.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Institutional Review Board of the University of Texas Health Science
Internal Review board (#HSC 20110313HU) (approved 13 May 2011).
Informed Consent Statement: Written informed consent was obtained from the participants in this
study as required by the Institutional Review Board of the University of Texas Health Science Internal
Review board (#HSC 20110313HU) (approved 13 May 2011).
J. Xenobiot. 2024,14 413
Data Availability Statement: Data are available by reasonable request to the author.
Conflicts of Interest: The author declares no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
References
1.
Maenner, M.J.; Shaw, K.A.; Baio, J.; Washington, A.; Patrick, M.; DiRienzo, M.; Christensen, D.L.; Wiggins, L.D.;
Pettygrove, S.
;
Andrews, J.G.; et al. Prevalence of Autism Spectrum Disorder among Children Aged 8 Years—Autism and Developmental
Disabilities Monitoring Network, 11 Sites, United States, 2016. MMWR Surveill. Summ. 2020,69, 1–12. [CrossRef]
2.
Li, Q.; Li, Y.; Liu, B.; Chen, Q.; Xing, X.; Xu, G.; Yang, W. Prevalence of Autism Spectrum Disorder Among Children and
Adolescents in the United States from 2019 to 2020. JAMA Pediatr. 2022,176, 943–945. [CrossRef]
3.
Kogan, M.D.; Vladutiu, C.J.; Schieve, L.A.; Ghandour, R.M.; Blumberg, S.J.; Zablotsky, B.; Perrin, J.M.; Shattuck, P.;
Kuhlthau, K.A.
;
Harwood, R.L.; et al. The Prevalence of Parent-Reported Autism Spectrum Disorder Among US Children. Pediatrics 2018,
142, e20174161. [CrossRef]
4.
Modabbernia, A.; Velthorst, E.; Reichenberg, A. Environmental risk factors for autism: An evidence-based review of systematic
reviews and meta-analyses. Mol. Autism 2017,8, 13. [CrossRef]
5.
Masini, E.; Loi, E.; Vega-Benedetti, A.F.; Carta, M.; Doneddu, G.; Fadda, R.; Zavattari, P. An Overview of the Main Genetic,
Epigenetic and Environmental Factors Involved in Autism Spectrum Disorder Focusing on Synaptic Activity. Int. J. Mol. Sci.
2020,21, 8290. [CrossRef] [PubMed]
6.
Bhandari, R.; Paliwal, J.K.; Kuhad, A. Neuropsychopathology of Autism Spectrum Disorder: Complex Interplay of Genetic,
Epigenetic, and Environmental Factors. Adv. Neurobiol. 2020,24, 97–141. [CrossRef] [PubMed]
7.
Carter, C.J.; Blizard, R.A. Autism genes are selectively targeted by environmental pollutants including pesticides, heavy metals,
bisphenol A, phthalates and many others in food, cosmetics, or household products. Neurochem. Int. 2016,101, 83–109.
[CrossRef] [PubMed]
8.
Saxena, R.; Babadi, M.; Namvarhaghighi, H.; Roullet, F.I. Role of environmental factors and epigenetics in autism spectrum
disorders. Prog. Mol. Biol. Transl. Sci. 2020,173, 35–60. [CrossRef] [PubMed]
9.
Krimsky, S. The unsteady state and inertia of chemical regulation under the U.S.Toxic Substances Control Act. PLoS Biol. 2017,
15, e2002404. [CrossRef]
10.
Gross, L.; Birnbaum, L. Regulating toxic chemicals for public and environmental health. PLoS Biol. 2017,15, e2004814. [CrossRef]
11.
Gwinn, M.R.; Axelrad, D.A.; Bahadori, T.; Bussard, D.; Cascio, W.E.; Deener, K.; Dix, D.; Thomas, R.S.; Kavlock, R.J.;
Burke, T.A. Chemical risk assessment: Traditional vs public health perspectives. AJPH Am. J. Public Health 2017,107, 1032–1039.
[CrossRef] [PubMed]
12.
Vogel, S.A.; Roberts, J.A. Why the toxic substances control act needs an overhaul, and how to strengthen oversight of chemicals in
the interim. Health Aff. 2011,30, 898–905. [CrossRef]
13.
McPartland, J.; Shaffer, R.M.; Fox, M.A.; Nachman, K.E.; Burke, T.A.; Denison, R.A. Charting a Path Forward: Assessing the
Science of Chemical Risk Evaluations under the Toxic Substances Control Act in the Context of Recent National Academies
Recommendations. Environ. Health Perspect. 2022,130, 25003. [CrossRef] [PubMed]
14.
Rayasam, S.D.G.; Koman, P.D.; Axelrad, D.A.; Woodruff, T.J.; Chartres, N. Toxic Substances Control Act (TSCA) Implementation:
How the Amended Law Has Failed to Protect Vulnerable Populations from Toxic Chemicals in the United States. Environ. Sci.
Technol. 2022,56, 11969–11982. [CrossRef]
15.
Department of Health and Human Services Centers for Disease Control and Prevention. CDC National Report on Human
Exposure to Environmental Chemicals. 2022. Available online: https://www.cdc.gov/exposurereport/index.html (accessed on
9 May 2022).
16.
Woodruff, T.J.; Zota, A.R.; Schwartz, J.M. Environmental chemicals in pregnant women in the United States: NHANES 2003–2004.
Environ. Health Perspect. 2011,119, 878–885. [CrossRef]
17.
Herbstman, J.B.; Sjodin, A.; Kurzon, M.; Lederman, S.A.; Jones, R.S.; Rauh, V.; Needham, L.L.; Tang, D.; Niedzwiecki, M.;
Wang, R.Y.; et al. Prenatal exposure to PBDEs and neurodevelopment. Environ. Health Perspect. 2010,118, 712–719. [CrossRef]
18.
Wigle, D.T.; Arbuckle, T.E.; Walker, M.; Wade, M.G.; Liu, S.; Krewski, D. Environmental hazards: Evidence for effects on child
health. J. Toxicol. Environ. Health B Crit. Rev. 2007,10, 3–39. [CrossRef]
19.
Talsness, C.E.; Andrade, A.J.M.; Kuriyama, S.N.; Taylor, J.A.; vom Saal, F.S. Components of plastic: Experimental studies in
animals and relevance for human health. Philos. Trans. R. Soc. B 2009,364, 2079–2096. [CrossRef]
20.
Mostafalou, S.; Abdollahi, M. Pesticides: An update of human exposure and toxicity. Arch. Toxicol. 2017,91, 549–599. [CrossRef]
21. Rochester, J.R. Bisphenol A, and human health: A review of the literature. Reprod. Toxicol. 2013,42, 132–155. [CrossRef]
22. Fisher, B.E. Most unwanted. Environ. Health Perspect. 1999,107, A18–A23. [CrossRef] [PubMed]
23.
Alavanja, M.C. Introduction: Pesticides use and exposure extensive worldwide. Rev. Environ. Health 2009,24, 303–309.
[CrossRef] [PubMed]
J. Xenobiot. 2024,14 414
24.
von Ehrenstein, O.S.; Ling, C.; Cui, X.; Cockburn, M.; Park, A.S.; Yu, F.; Wu, J.; Ritz, B. Prenatal and infant exposure to
ambient pesticides and autism spectrum disorder in children: Population-based case-control study. BMJ 2019,364, l962.
[CrossRef] [PubMed]
25.
Hertz-Picciotto, I.; Park, H.Y.; Dostal, M.; Kocan, A. Prenatal exposures to persistent and non-persistent organic compounds and
effects on immune system development. Basic Clin. Pharmacol. Toxicol. 2008,102, 146–154. [CrossRef] [PubMed]
26. Rosas, L.G.; Eskenazi, B. Pesticides, and child neurodevelopment. Curr. Opin. Pediatr. 2008,20, 191–197. [CrossRef] [PubMed]
27.
Eskenazi, B.; Rosas, L.G.; Marks, A.R.; Bradman, A.; Harley, K.; Holland, N.; Johnson, C.; Fenster, L.; Barr, D.B. Pesticide toxicity
and the developing brain. Basic. Clin. Pharmacol. Toxicol. 2008,102, 228–236. [CrossRef]
28.
Aldridge, J.E.; Seidler, F.J.; Meyer, A.; Thillai, I.; Slotkin, T. Serotonergic systems targeted by developmental exposure to
chlorpyrifos: Effects during different critical periods. Environ. Health Perspect. 2003,111, 1736–1743. [CrossRef]
29.
Shelton, J.F.; Geraghty, E.M.; Tancredi, D.J.; Delwiche, L.D.; Schmidt, R.J.; Ritz, B.; Hertz-Picciotto, I. Neurodevelopmental
disorders, and prenatal residential proximity to agricultural pesticides: The CHARGE Study. Environ. Health Perspect. 2014,
122, A266. [CrossRef]
30.
Sarigiannis, D.A.; Papaioannou, N.; Handakas, E.; Anesti, O.; Polanska, K.; Hanke, W.; Salifoglou, A.; Gabriel, C.; Karakitsios, S.
Neurodevelopmental exposome: The effect of in utero co-exposure to heavy metals and phthalates on child neurodevelopment.
Environ. Res. 2021,197, 110949. [CrossRef]
31.
Kardas, F.; Bayram, A.K.; Demirci, E.; Akin, L.; Ozmen, S.; Kendirci, M.; Canpolat, M.; Oztop, D.B.; Narin, F.; Gumus, H.; et al.
Increased Serum Phthalates (MEHP, DEHP) and Bisphenol A Concentrations in Children with Autism Spectrum Disorder: The
Role of Endocrine Disruptors in Autism Etiopathogenesis. J. Child. Neurol. 2016,31, 629–635. [CrossRef]
32.
Testa, C.; Nuti, F.; Hayek, J.; De Felice, C.; Chelli, M.; Rovero, P.; Latini, G.; Papini, A.M. Di-(2-ethylhexyl) phthalate and autism
spectrum disorders. ASN Neuro 2012,4, 223–229. [CrossRef]
33.
Rappaport, S.M.; Barupal, D.K.; Wishart, D.; Vineis, P.; Scalbert, A. The blood exposome and its role in discovering causes of
disease. Environ. Health Perspect. 2014,22, 769–774. [CrossRef] [PubMed]
34.
Drexler, H.; Shukla, A. Importance of Exposure Level for Toxicological Risk Assessment. In Regulatory Toxicology; Springer: Cham,
Switzerland, 2020; pp. 1–8.
35.
Arora, M.; Austin, C. Teeth as a biomarker of past chemical exposure. Curr. Opin. Pediatr. 2013,25, 261–267. [CrossRef] [PubMed]
36.
Andra, S.S.; Austin, C.; Arora, M. Tooth matrix analysis for biomonitoring of organic chemical exposure: Current status,
challenges, and opportunities. Environ. Res. 2015,142, 387–406. [CrossRef] [PubMed]
37.
Dumitriu, D.; Baldwin, E.; Coenen, R.J.J.; Hammond, L.A.; Peterka, D.S.; Heilbrun, L.; Frye, R.E.; Palmer, R.; Norrman, H.N.;
Fridell, A.; et al. Deciduous tooth biomarkers reveal atypical fetal inflammatory regulation in autism spectrum disorder. iScience
2023,26, 106247. [CrossRef] [PubMed]
38.
Berkovitz, B.K.B.; Holland, G.R.; Moxham, B.J. A Colour Atlas and Textbook of Oral Anatomy, Histology and Embryology; Wolfe
Publishing Ltd.: London, UK, 1992.
39.
Rabinowitz, M.B.; Leviton, A.; Bellinger, D. Relationships between serial blood lead levels and exfoliated tooth dentin lead levels:
Models of tooth lead kinetics. Calcif. Tissue Int. 1993,53, 338–341. [CrossRef] [PubMed]
40.
Simeonov, L.; Kochubovski, M.; Simeonova, B.; Draghici, C.; Chirila, E.; Canfield, R. NATO Advanced research workshop on
environmental heavy metal pollution and effects on child mental development: Discussion conclusions and recommendations. In
Environmental Heavy Metal Pollution and Effects on Child Mental Development; Simeonov, L., Kochubovski, M., Simeonova, B., Eds.;
Springer: Dordrecht, The Netherlands, 2010; pp. 331–342.
41.
Camann, D.E.; Schultz, S.T.; Yau, A.Y.; Heilbrun, L.P.; Zuniga, M.M.; Palmer, R.F.; Miller, C.S. Acetaminophen, pesticide, and
diethylhexyl phthalate metabolites, anandamide, and fatty acids in deciduous molars: Potential biomarkers of perinatal exposure.
J. Expo. Sci. Environ. Epidemiol. 2013,23, 190–196. [CrossRef] [PubMed]
42.
Palmer, R.F.; Heilbrun, L.; Camann, D.; Yau, A.; Schultz, S.; Elisco, V.; Tapia, B.; Garza, N.; Miller, C.S. Organic compounds
detected in deciduous teeth: A replication study from children with autism in two samples. J. Environ. Public Health 2015,
2015, 862414. [CrossRef]
43.
Yu, M.; Tu, P.; Dolios, G.; Dassanayake, P.S.; Volk, H.; Newschaffer, C.; Fallin, M.D.; Croen, L.; Lyall, K.; Schmidt, R.; et al. Tooth
biomarkers to characterize the temporal dynamics of the fetal and early-life exposome. Environ. Int. 2021,157, 106849. [CrossRef]
44.
Lee, H.; Marvin, A.R.; Watson, T.; Piggot, J.; Law, J.K.; Law, P.A. Accuracy of phenotyping of autistic children based on internet
implemented parent report. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2010,153B, 1119–1126. [CrossRef]
45.
Daniels, A.M.; Rosenberg, R.E.; Anderson, C.; Law, J.K.; Marvin, A.R.; Law, P.A. Verification of parent-report of child autism
spectrum disorder diagnosis to a web-based autism registry. J. Autism Dev. Disord. 2012,42, 257–265. [CrossRef]
46.
Favela, K.; Hartnett, M.; Janssen, J.; Vickers, D.; Schaub, A.; Spidle, H.; Pickens, K. Nontargeted Analysis of Face Masks:
Comparison of Manual Curation to Automated GCxGC Processing Tools. J. Am. Soc. Mass Spectrom. 2021,32, 860–871. [CrossRef]
47.
Place, B.; Ulrich, E.; Challis, J.; Chao, A.; Du, B.; Favela, K.; Feng, Y.; Fisher, C.; Gardinali, P.; Hood, A.; et al. An Introduction to
the Benchmarking and Publications for Non-Targeted Analysis Working Group. Anal. Chem. 2021,93, 16289–16296. [CrossRef]
48.
Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; et al. PubChem 2023
update. Nucleic Acids Res. 2023,51, D1373–D1380. [CrossRef]
49. Birkett, M.A.; Day, S.J. Internal pilot studies for estimating sample size. Stat. Med. 1994,13, 2455–2463. [CrossRef]
50. Browne, R.H. On the use of a pilot study for sample size determination. Stat. Med. 1995,14, 1933–1940. [CrossRef]
J. Xenobiot. 2024,14 415
51.
Lee, E.; Whitehead, A.L.; Jacques, R.M.; Julious, S.A. The statistical interpretation of pilot trials: Should significance thresholds be
reconsidered? BMC Med. Res. Methodol. 2014,14, 41. [CrossRef]
52.
Leon, A.; Davis, L.; Kraemer, H. The role and interpretation of pilot studies in clinical research. J. Psychiatr. Res. 2011,45, 626–629.
[CrossRef] [PubMed]
53. Schoenfeld, D. Statistical considerations for pilot studies. Int. J. Radiat. Oncol. Biol. Phys. 1980,6, 371–374. [CrossRef]
54. SAS Institute Inc. SAS®9.4 Statements: Reference; SAS Institute Inc.: Cary, NC, USA, 2013.
55.
Frye, R.E.; Rossignol, D.A. Metabolic disorders and abnormalities associated with autism spectrum disorder. J. Pediatr. Biochem.
2012,2, 181–191.
56.
Nisar, S.; Haris, M. Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum
disorder. Mol. Psychiatry 2023. [CrossRef]
57.
Biosca-Brull, J.; Pérez-Fernández, C.; Mora, S.; Carrillo, B.; Pinos, H.; Conejo, N.M.; Collado, P.; Arias, J.L.; Martín-Sánchez, F.;
Sánchez-Santed, F.; et al. Relationship between Autism Spectrum Disorder and Pesticides: A Systematic Review of Human and
Preclinical Models. Int. J. Environ. Res. Public. Health 2021,18, 5190. [CrossRef] [PubMed]
58.
Maleki, M.; Noorimotlagh, Z.; Mirzaee, S.A.; Jaafarzadeh, N.; Martinez, S.S.; Rahim, F.; Kaffashian, M. An updated systematic
review on the maternal exposure to environmental pesticides and involved mechanisms of autism spectrum disorder (ASD)
progression risk in children. Rev. Environ. Health 2022.ahead of print. [CrossRef] [PubMed]
59.
Xu, Y.; Yang, X.; Chen, D.; Xu, Y.; Lan, L.; Zhao, S.; Liu, Q.; Snijders, A.M.; Xia, Y. Maternal exposure to pesticides and autism or
attention-deficit/hyperactivity disorders in offspring: A meta-analysis. Chemosphere 2023,313, 137459. [CrossRef] [PubMed]
60.
Eales, J.; Bethel, A.; Galloway, T.; Hopkinson, P.; Morrissey, K.; Short, R.E.; Garside, R. Human health impacts of exposure to
phthalate plasticizers: An overview of reviews. Environ. Int. 2022,158, 106903. [CrossRef] [PubMed]
61. Fujiwara, T.; Morisaki, N.; Honda, Y.; Sampei, M.; Tani, Y. Chemicals, Nutrition, and autism spectrum disorder: A Mini-Review.
Front. Neurosci. 2016,10, 174. [CrossRef]
62.
Volk, H.E.; Ames, J.L.; Chen, A.; Fallin, M.D.; Hertz-Picciotto, I.; Halladay, A.; Hirtz, D.; Lavin, A.; Ritz, B.; Zoeller, T.; et al.
Considering toxic chemicals in the etiology of autism. Pediatrics 2022,149, e2021053012. [CrossRef]
63. Keil-Stietz, K.; Lein, P.J. Gene×environment interactions in autism spectrum disorders. Curr. Top. Dev. Biol. 2023,152, 221–284.
64.
Santos, J.X.; Rasga, C.; Marques, A.R.; Martiniano, H.; Asif, M.; Vilela, J.; Oliveira, G.; Sousa, L.; Nunes, A.; Vicente, A.M. A Role
for Gene-Environment Interactions in Autism Spectrum Disorder Is Supported by Variants in Genes Regulating the Effects of
Exposure to Xenobiotics. Front. Neurosci. 2022,16, 862315. [CrossRef]
65.
Thomas, T.R.; Koomar, T.; Casten, L.G.; Tener, A.J.; Bahl, E.; Michaelson, J.J. Clinical autism subscales have common ge-
netic liabilities that are heritable, pleiotropic, and generalizable to the general population. Transl. Psychiatry 2022,12, 247.
[CrossRef] [PubMed]
66.
Warrier, V.; Zhang, X.; Reed, P.; Havdahl, A.; Moore, T.M.; Cliquet, F.; Leblond, C.S.; Rolland, T.; Rosengren, A.; EU-AIMS LEAP;
et al. Genetic correlates of phenotypic heterogeneity in autism. Nat. Genet. 2022,54, 1293–1304. [CrossRef] [PubMed]
67.
Pintus, R.; Dessi, A.; Bosco, A.; Fanos, V. Metabolomics in Pediatric Neuropsychiatry. BRAIN Broad Res. Artif. Intell. Neurosci.
2021,12, 335–341. [CrossRef]
68. Vidal, M.; Cusick, M.E.; Barabási, A.L. Interactome networks and human disease. Cell 2011,144, 986–998. [CrossRef] [PubMed]
69.
Stanton, J.E.; Malijauskaite, S.; McGourty, K.; Grabrucker, A.M. The Metallome as a Link Between the “Omes” in autism spectrum
disorders. Front. Mol. Neurosci. 2021,14, 695873. [CrossRef] [PubMed]
70.
Rechnitzer, T. Unifying ‘the’ Precautionary Principle? Justification and Reflective Equilibrium. Philosophia 2022,50, 2645–2661.
[CrossRef] [PubMed]
71. Elkin, L. The Precautionary Principle and Expert Disagreement. Erkenntnis 2023,88, 2717–2726. [CrossRef]
72.
Fowler, S.P.; Gimeno Ruiz de Porras, D.; Swartz, M.D.; Stigler Granados, P.; Heilbrun, L.P.; Palmer, R.F. Daily Early-Life Exposures
to Diet Soda and Aspartame Are Associated with Autism in Males: A Case-Control Study. Nutrients 2023,15, 3772. [CrossRef]
73.
Heilbrun, L.P.; Palmer, R.F.; Jaen, C.R.; Svoboda, M.D.; Perkins, J.; Miller, C.S. Maternal Chemical and Drug Intolerances:
Potential Risk Factors for Autism and Attention Deficit Hyperactivity Disorder (ADHD). J. Am. Board. Fam. Med. 2015,28,
461–470. [CrossRef]
74.
Steinemann, A. Fragranced consumer products: Exposures and effects from emissions. Air Qual. Atmos. Health 2016,9,
861–866. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
... Зворотній механізм пояснюється тим, що пестициди та пластмаси є нейротоксинами та руйнівниками ендокринної системи, тому гестаційний вплив пестицидів пов'язаний із ризиком аутизму. Серед рекомендацій відмова під час вагітності від штучних підсолоджувачів [10]. Дослідження Фаулер та ін. ...
Article
Вступ. Мета дослідження. Проаналізувати дані літературних джерел з щодо вивчення взаємозв’язку мінерального склад твердих тканин зубів та загального стану організму. Матеріали та методи дослідження: наукові публікації дослідників за визначеною тематикою; був використаний бібліо-семантичний метод та структурно-логічний аналіз. Результати. В організмі людини 99% його елементного складу представлено такими складниками як кисень, водень, вуглець, азот, натрій, кальцій, магній, калій, сірка, фосфор, хлор та фтор. В свою чергу більш ніж 90% хімічного складу живих клітин складають вуглець, водень, кисень і азот. Мінеральний склад твердих тканин здорових зубів перебуває в динамічній рівновазі все життя, з віком змінюються фізичні властивості зубної емалі та дентину, зокрема вірогідна відмінність показників твердості, модуля пружності та крихкості, що впливає й на механічні та фізіологічні характеристики всіх тканин зуба та функціонування зуба як органа в цілому. В ході проведених досліджень доведено, що концентрація всіх металів (Ca, Mg, Cd, Cu, Pb, K, Cr) у тканинах тимчасових зубів з віком знижується (p≤0,05). За результатами проведеного кореляційного та кластерного аналізу визнана участь іонів свинцю, заліза, марганцю та хрому у формуванні вмісту міді в твердих тканинах тимчасових зубів. Проте, не дивлячись на те, що певні елементи присутні в невеликих кількостях в емалі і дентині зубів, їх відсутність може порушити здоровий розвиток емалі та дентину та призвести до дефектів розвитку зубів, а також до карієсу. Окрім того, надмірне споживання деяких мікроелементів може зворотно вплинути на розвиток і здоров’я зубів. Дослідники встановили, що іони свинцю замінюють кальцій, а також кальцій і фосфор у кристалах кісткових мінералів, викликаючи гіперкальціємію та гіперфосфатемію, тобто свинець вважається елементом, що сприяє карієсу. Точний вплив мікроелементів на здоров’я зубів і ротової порожнини досі залишається невивченим. Для визначення хімічного складу твердих тканин зубів використовують методи фізико-хімічного аналізу, зокрема: атомну, силову, скануючу, електронну, інфрачервону, оптичну мікроскопію, електронне мікрозондування, мас-спектрометрію, термодеріватографію, інфрачервону спектроскопію.Результати спектрального дослідження твердих тканин зубів допоможуть підібрати пломбувальний матеріал з урахуванням його адгезивних властивостей, що покращить віддалені результати лікування карієсу та якість здійснених реставраційних робіт. Висновок. Обгрунтований підхід до вивчення елементарного складу емалі та дентину на різних рівнях в зубах при карієсі та ураженнях тканин пародонта представляє суттєвий науковий інтерес.
Article
Full-text available
Since its introduction, aspartame—the leading sweetener in U.S. diet sodas (DS)—has been reported to cause neurological problems in some users. In prospective studies, the offspring of mothers who consumed diet sodas/beverages (DSB) daily during pregnancy experienced increased health problems. We hypothesized that gestational/early-life exposure to ≥1 DS/day (DSearly) or equivalent aspartame (ASPearly: ≥177 mg/day) increases autism risk. The case-control Autism Tooth Fairy Study obtained retrospective dietary recalls for DSB and aspartame consumption during pregnancy/breastfeeding from the mothers of 235 offspring with autism spectrum disorder (ASD: cases) and 121 neurotypically developing offspring (controls). The exposure odds ratios (ORs) for DSearly and ASPearly were computed for autism, ASD, and the non-regressive conditions of each. Among males, the DSearly odds were tripled for autism (OR = 3.1; 95% CI: 1.02, 9.7) and non-regressive autism (OR = 3.5; 95% CI: 1.1, 11.1); the ASPearly odds were even higher: OR = 3.4 (95% CI: 1.1, 10.4) and 3.7 (95% CI: 1.2, 11.8), respectively (p < 0.05 for each). The ORs for non-regressive ASD in males were almost tripled but were not statistically significant: DSearly OR = 2.7 (95% CI: 0.9, 8.4); ASPearly OR = 2.9 (95% CI: 0.9, 8.8). No statistically significant associations were found in females. Our findings contribute to the growing literature raising concerns about potential offspring harm from maternal DSB/aspartame intake in pregnancy.
Article
Full-text available
Autism-spectrum disorders (ASDs) are developmental disabilities that manifest in early childhood and are characterized by qualitative abnormalities in social behaviors, communication skills, and restrictive or repetitive behaviors. To explore the neurobiological mechanisms in ASD, extensive research has been done to identify potential diagnostic biomarkers through a neuroimaging genetics approach. Neuroimaging genetics helps to identify ASD-risk genes that contribute to structural and functional variations in brain circuitry and validate biological changes by elucidating the mechanisms and pathways that confer genetic risk. Integrating artificial intelligence models with neuroimaging data lays the groundwork for accurate diagnosis and facilitates the identification of early diagnostic biomarkers for ASD. This review discusses the significance of neuroimaging genetics approaches to gaining a better understanding of the perturbed neurochemical system and molecular pathways in ASD and how these approaches can detect structural, functional, and metabolic changes and lead to the discovery of novel biomarkers for the early diagnosis of ASD.
Article
Full-text available
Atypical regulation of inflammation has been proposed in the etiology of autism spectrum disorder (ASD); however, measuring the temporal profile of fetal inflammation associated with future ASD diagnosis has not been possible. Here, we present a method to generate approximately daily profiles of prenatal and early childhood inflammation as measured by developmentally archived C-reactive protein (CRP) in incremental layers of deciduous tooth dentin. In our discovery population, a group of Swedish twins, we found heightened inflammation in the third trimester in children with future ASD diagnosis relative to controls (n = 66; 14 ASD cases; critical window: −90 to −50 days before birth). In our replication study, in the US, we observed a similar increase in CRP in ASD cases during the third trimester (n = 47; 23 ASD cases; −128 to −21 days before birth). Our results indicate that the third trimester is a critical period of atypical fetal inflammatory regulation in ASD.
Article
Full-text available
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the 'standardize' option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.
Article
Full-text available
The precautionary principle (PP) is an influential principle for making decisions when facing uncertain, but potentially severe, harm. However, there is a persistent disagreement about what the principle entails, exactly. It exists in a multitude of formulations and has potentially conflicting ideas associated with it. Is there even such a thing as ‘the precautionary principle’? This paper analyses the debate between unificationists and pluralists about ‘the PP’, arguing that the debate is hindered by neglecting the question of justification. It introduces reflective equilibrium as a method of justification, and sketches how it could be applied to justify a PP.
Article
Full-text available
Exposures to industrial chemicals are widespread and can increase the risk of adverse health effects such as cancer, developmental disorders, respiratory effects, diabetes, and reproductive problems. The amended Toxic Substances Control Act (amended TSCA) requires the U.S. Environmental Protection Agency (EPA) to evaluate risks of chemicals in commerce, account for risk to potentially exposed and susceptible populations, and mitigate risks for chemicals determined to pose an unreasonable risk to human health and the environment. This analysis compares EPA's first 10 chemical risk evaluations under amended TSCA to best scientific practices for conducting risk assessments. We find EPA's risk evaluations underestimated human health risks of chemical exposures by excluding conditions of use and exposure pathways; not considering aggregate exposure and cumulative risk; not identifying all potentially exposed or susceptible subpopulations, and not quantifying differences in risk for susceptible groups; not addressing data gaps; and using flawed systematic review approaches to identify and evaluate the relevant evidence. We present specific recommendations for improving the implementation of amended TSCA using the best available science to ensure equitable, socially just safeguards to public health. Failing to remedy these shortcomings will result in continued systematic underestimation of risk for all chemicals evaluated under amended TSCA.
Article
Full-text available
This cross-sectional study estimates the prevalence of autism spectrum disorder in children and adolescents aged 3 to 17 years in the US in 2019 and 2020.
Chapter
There is credible evidence that environmental factors influence individual risk and/or severity of autism spectrum disorders (hereafter referred to as autism). While it is likely that environmental chemicals contribute to the etiology of autism via multiple mechanisms, identifying specific environmental factors that confer risk for autism and understanding how they contribute to the etiology of autism has been challenging, in part because the influence of environmental chemicals likely varies depending on the genetic substrate of the exposed individual. Current research efforts are focused on elucidating the mechanisms by which environmental chemicals interact with autism genetic susceptibilities to adversely impact neurodevelopment. The goal is to not only generate insights regarding the pathophysiology of autism, but also inform the development of screening platforms to identify specific environmental factors and gene × environment (G × E) interactions that modify autism risk. Data from such studies are needed to support development of intervention strategies for mitigating the burden of this neurodevelopmental condition on individuals, their families and society. In this review, we discuss environmental chemicals identified as putative autism risk factors and proposed mechanisms by which G × E interactions influence autism risk and/or severity using polychlorinated biphenyls (PCBs) as an example.
Article
Objective To analyze the association between maternal pesticide exposure and autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorders (ADHD) in offspring. Method Five databases including PubMed, Embase, Web of Science, Medline, as well as PsycINFO were systematically retrieved for the records related to pesticide exposure during pregnancy and ASD and ADHD in offspring before August 30, 2022. The pesticide category, maternal age and window of exposure as the main subgroups were presented. Results 949 studies were initially identified, and 19 studies were eventually included. Eleven were on ASD, seven were on ADHD, and one was on both disorders. Maternal pesticide exposure was positively related to ASD (pooled OR = 1.19 (95%CI: 1.04 to 1.36)) and ADHD (pooled OR = 1.20 (95%CI: 1.04 to 1.38)) in offspring. In the subgroup analysis, organophosphorus pesticides (OPs) (pooled OR = 1.14 (95%CI: 1.04 to 1.24)), pyrethroid (pooled OR = 1.40 (95%CI: 1.09 to 1.80)), and maternal age ≥30 years old (pooled OR = 1.24 (95%CI: 1.10 to 1.40)) increased the risk of ASD in offspring. Maternal organochlorine pesticides (OCPs) exposure was a risk factor for ADHD in offspring (pooled OR = 1.22 (95%CI: 1.03 to 1.45)). Conclusion Maternal pesticide exposure increased the risk of ASD and ADHD in offspring. Moreover, OPs, pyrethroid, and maternal age ≥30 years old were found to be risk factors affecting children's ASD. Maternal exposure to OCPs increased the risk of ADHD in offspring. Our findings contribute to our understanding of health risks related to maternal pesticide exposure and indicate that the in utero developmental period is a vulnerable window-of-susceptibility for ASD and ADHD risk in offspring. These findings should guide policies that limit maternal exposure to pesticides, especially for pregnant women living in agricultural areas.
Article
Autism spectrum disorder (ASD) increased dramatically over the past 25 years because of genetic and environmental factors. This systematic review (SR) aimed to determine the association between maternal exposure during pregnancy to environmental pesticides and other associations with the risk of ASD progression in children. PubMed (MEDLINE), Scopus (Elsevier) and the Institute for Scientific Information (ISI) Web of Science were searched using appropriate keywords up to March 2021. Twenty-four studies met the inclusion/exclusion criteria and were selected. Most studies reported that ASD increases the risk of offspring after prenatal exposure to environmental pesticides in pregnant mother’s residences, against offspring of women from the same region without this exposure. The main potential mechanisms inducing ASD progressions are ROS and prostaglandin E2 synthesis, AChE inhibition, voltage-gated sodium channel disruption, and GABA inhibition. According to the included studies, the highest rates of ASD diagnosis increased relative to organophosphates, and the application of the most common pesticides near residences might enhance the prevalence of ASD.