Healthy dietary patterns are associated with exposure to environmental chemicals in a pregnancy cohort

Abstract

Healthy dietary patterns, such as the alternate Mediterranean diet and alternate Healthy Eating Index, benefit cardiometabolic health. However, several food components of these dietary patterns are primary sources of environmental chemicals. Here, using data from a racially and ethnically diverse US cohort, we show that healthy dietary pattern scores were positively associated with plasma chemical exposure in pregnancy, particularly for the alternate Mediterranean diet and alternate Healthy Eating Index with polychlorinated biphenyls and per- and poly-fluoroalkyl substances. The associations appeared stronger among Asian and Pacific Islanders. These findings suggest that optimizing the benefits of a healthy diet requires concerted regulatory efforts aimed at lowering environmental chemical exposure.

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https://doi.org/10.1038/s43016-024-01013-x
Brief Communication
Healthy dietary patterns are associated with
exposure to environmental chemicals in a
pregnancy cohort
Guoqi Yu1,2,3, Ruijin Lu4, Jiaxi Yang1,2,3, Mohammad L. Rahman5, Ling-Jun Li1,2,3,
Dong D. Wang 6, Qi Sun 6,7, Wei Wei Pang 1,2,3, Claire Guivarch 1,2,3,
Anna Birukov6, Jagteshwar Grewal8, Zhen Chen8 & Cuilin Zhang 1,2,3,7
Healthy dietary patterns, such as the alternate Mediterranean diet and
alternate Healthy Eating Index, benet cardiometabolic health. However,
several food components of these dietary patterns are primary sources of
environmental chemicals. Here, using data from a racially and ethnically
diverse US cohort, we show that healthy dietary pattern scores were
positively associated with plasma chemical exposure in pregnancy,
particularly for the alternate Mediterranean diet and alternate Healthy
Eating Index with polychlorinated biphenyls and per- and poly-uoroalkyl
substances. The associations appeared stronger among Asian and Pacic
Islanders. These ndings suggest that optimizing the benets of a healthy
diet requires concerted regulatory eorts aimed at lowering environmental
chemical exposure.
Current dietary guidelines endorse higher adherence to healthy dietary
patterns, such as the alternate Mediterranean diet (aMED), alternate
Healthy Eating Index (aHEI) and Dietary Approaches to Stop Hyper-
tension (DASH) over specific, individual foods or nutrients as people
usually do not eat single foods1,2. Accumulating evidence demonstrates
that adherence to aMED, aHEI and DASH is linked to reduced risks for
cardiometabolic diseases and pregnancy complications3.
Several major food components of the three healthy dietary
patterns, however, are sources of some environmental chemicals.
For example, per- and poly-fluoroalkyl substances (PFAS), heavy met-
als and polychlorinated biphenyls (PCBs) can be traced to seafood
consumption due to bioaccumulation
4
. Despite efforts to reduce their
release, many of these chemicals persist in the environment and perme-
ate the human body via diverse pathways, especially through the food
chain
5
. The majority of these chemical exposure have been associated
with diverse health outcomes, particularly for pregnant women and
foetuses who are sensitive to environmental stimuli owing to intensive
metabolic disturbance, incomplete immune protection and rapid cell
division during early embryonic development6.
As such, it is pivotal to understand the environmental-chemical
portfolio of the commonly recommended healthy dietary patterns.
Such data are sparse so far, with most previous studies focusing on
individual food items, overlooking the synergic effects of major food
groups and nutrients on circulating chemicals7.
Elucidating the associations between healthy dietary patterns and
blood concentrations of environmental chemicals has the potential
to unveil the degree to which these healthy dietary patterns may be
related to potentially ‘harmful’ chemical exposures. Such findings
Received: 10 December 2023
Accepted: 14 June 2024
Published online: 1 July 2024
Check for updates
1Global Centre for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 2Department of
Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 3Bia-Echo Asia Centre for
Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 4Division of Biostatistics,
School of Medicine, Washington University in St. Louis, St. Louis, MO, USA. 5Occupational and Environmental Epidemiology Branch, Division of Cancer
Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA. 6Channing Division of Network Medicine, Harvard Medical School and
Brigham and Women’s Hospital and Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 7Department of Nutrition,
Harvard T. H. Chan School of Public Health, Boston, MA, USA. 8Division of Population Health Research, Eunice Kennedy Shriver National Institute of Child
Health and Human Development, National Institutes of Health, Bethesda, MD, USA. e-mail: obgzc@nus.edu.sg
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Nature Food | Volume 5 | July 2024 | 563–568 564
Brief Communication https://doi.org/10.1038/s43016-024-01013-x
Table 1 | Plasma concentration differences of chemicals according to aMED, aHEI and DASH scores among the NICHD Fetal
Growth Study–Singletons cohort
aMED aHEI DASH
Chemical Low aMED High aMED Adjusted
P value Low aHEI High aHEI Adjusted
P value Low DASH High DASH Adjusted
P value
BetaHCH 0.74 (2.36) 1.21 (4.57) ** 0.59 (1.94) 1.51 (5.77) *** 0.89 (3.08) 1.28 (4.64)
HCB 5.56 (9.68) 6.59 (7.42) *6.1 (9.64) 6.48 (7.53) *6.2 (9.18) 6.56 (6.67)
TransNo_chlor 3.98 (4.44) 4.56 (5.46) *4.18 (4.85) 4.47 (5.16) 4.26 (4.92) 4.54 (4 .97)
P_P_DDE 66.59 (86.63) 83.22 (123.08) *** 63.59 (82.78) 88.78 (135.1) *** 71.71 (103.49) 85.4 (122.59) *
P_P_DDD 0.31 (0.14) 0.32 (0.29) *0.32 (0.14) 0.32 (0.31) 0.32 (0.24) 0.32 (0.22)
P_P_DDT 0.89 (1.8) 1.37 (2 .64) *** 0.83 (1.59) 1.51 (3.05) *** 1.02 (2.18) 1.46 (2.61) *
Mirex 0.32 (0.32) 0.3 3 (0.57 ) 0.32 (0.23) 0.3 4 (0.7) *** 0.32 (0.38) 0.33 (0.62)
Total OCPs 90.92 (109.14) 109.92 (139.99) *** 87.41 (100.86) 118.54 (155.67) *** 98.55 (121.66) 110.06 (142.71) *
BDE47 9.65 (14.3) 8.2 (13.28) *9.78 (14.46) 7.6 (12.34) *** 9.27 (14.56) 7.48 (11.12) ***
BDE100 2.86 (4.21) 2.3 (4.15) ** 2.84 (3.98) 2.12 (4.19) *** 2.72 (4.29) 2.02 (3.8) ***
BDE99 2.47 (4.89) 2.1 (4.37) 2.55 (5.2) 1.98 (4.33) *2.42 (5.12) 1.83 (3.59) **
BDE183 0.31 (0.1) 0.3 (0.07 ) *0.31 ( 0.09) 0.3 (0.08) *0. 31 (0.09 ) 0.3 ( 0.07) *
Total PBDEs 26.68 (34.86) 21.53 (31.2) *** 25.71 (34.19) 21.19 (31.09) *** 25.34 (35.18) 18.53 (29.22) ***
PCB74_61 0.93 (1.08) 1.14 (1.33) *** 0.9 (0.94) 1.23 (1.48) *** 1.01 (1.14) 1.19 (1.35) *
PCB66_80 0.61 (0.18) 0.63 (0.2) 0.61 (0.16) 0.63 (0.22) *0.62 (0.19) 0.62 (0.2)
PCB99 0.91 (1.08) 1.12 (1.4) *** 0.92 (1.05) 1.15 (1.5) *** 1.06 (1.19) 1.07 (1.33)
PCB118_106 1.6 ( 2.04) 2.05 (2.38) *** 1.58 (1.99) 2.13 (2.55) *** 1.84 (2.26) 2 (2.46)
PCB105_127 0.65 (0.31) 0.69 ( 0.57) *** 0.65 (0.31) 0.71 (0.65) *** 0.67 (0.41) 0.69 ( 0.56)
PCB146_161 0.63 (0.25) 0.69 ( 0.66) *** 0.64 (0.26) 0.72 (0.77) *** 0.66 (0.48) 0.68 ( 0.64)
PCB153 4.43 (5.03) 6.09 (7.59) *** 4.43 (4.97) 6.63 (8.26) *** 5.15 (6.24) 6.1 (7.79) ***
PCB138_158 3.76 (4.15) 4.91 (6.06) *** 3.78 (4.07) 5.33 (6.19) *** 4.19 (4.85) 5.14 (6.22) ***
PCB156 0.63 (0.23) 0.67 (0.53) *** 0.64 (0.23) 0.6 8 (0.63) *** 0.65 (0.32) 0.69 (0.65) **
PCB182_187 0.92 (1.21) 1.35 (1.82) *** 0.92 (1.15) 1.44 (1.95) *** 1.12 (1.54) 1.3 1 (1.78) **
PCB183 0.61 (0.17) 0.65 (0.3) *** 0.62 (0.18) 0.65 (0.33) *** 0.63 (0.22) 0.64 ( 0.28)
PCB177 0.6 (0.1 4) 0.61 (0.15) *0.6 (0.14) 0.6 (0.16) 0.61 (0.15) 0.6 (0.15)
PCB180 2.54 (3) 3.69 (4.3) *** 2.43 (2.75) 4.02 (4.51) *** 2.87 (3.44) 3.99 (4.32) ***
PCB170 1.07 (1.1) 1.47 ( 1.68) *** 0.99 (1.07) 1.59 (1.79) *** 1.17 (1.33) 1.56 (1.77) ***
PCB199 0.64 (0.32) 0.71 (0.6 8) *** 0.65 (0.29) 0.7 3 (0.8) *** 0.67 (0.44) 0.71 (0.6 7)
PCB196_203 0.67 ( 0.41) 0.76 (0.75) *** 0.67 (0.42) 0.78 (0.83) *** 0.69 ( 0.54) 0.77 (0.79) *
PCB194 0.64 (0.26) 0.71 (0.58) *** 0.64 (0.25) 0.74 (0.64 ) *** 0.66 (0.36) 0.75 (0.63) ***
PCB206 0.6 (0.16) 0.62 (0.2) *** 0.61 (0.16) 0.62 (0.2) *0.61 (0.16) 0.62 (0.21)
PCB209 0.59 (0.14) 0.61 (0.14) *0.6 (0.14 ) 0.6 (0.15) 0.6 ( 0.14) 0.59 (0.14)
Total PCBs 21.21 (30.1) 29.96 (40.2) *** 20.86 (29.59) 32.9 (42.13) *** 25.33 (35.73) 29.95 (39) **
NMeFOSAA 0.06 ( 0.09) 0.05 (0.08) ** 0.06 (0.1) 0.04 (0.08) *** 0.06 (0.09) 0.04 (0.0 8) ***
PFDS 0.04 (0) 0.04 (0) 0.04 (0) 0.04 (0) ** 0.04 (0) 0.04 (0)
PFDoDA 0.02 (0.02) 0.03 ( 0.04) *** 0.02 (0.02) 0.03 ( 0.04) *** 0.02 (0.03) 0.02 (0.03)
PFOS 4.48 (3.67) 4.84 (4.19) *4.69 (3.61) 4.81 (4.4) 4.8 (3.89) 4.66 (4.26)
PFOA 1.73 (1.42) 1.81 (1.4) *1.72 (1.26) 1.89 (1.49) *** 1.73 (1.3) 1.92 (1.6) ***
PFNA 0.71 (0.52) 0.78 (0.63) ** 0.7 (0.49) 0.82 (0.64) *** 0.73 ( 0.57) 0.77 (0.58)
PFDA 0.2 (0.19) 0.24 (0.27) *** 0.2 (0.19) 0.25 (0.29) *** 0.22 (0.23) 0.24 (0.25)
PFUnDA 0.13 (0.16) 0.2 (0.28) *** 0.13 (0.17) 0.22 (0.32) *** 0.16 (0.24) 0.18 (0.22)
Total PFASs 8.8 (6.66) 9.37 (6.92) *8.86 (5.63) 9.55 (7.69) ** 9.19 (6.33) 9.07 (7.37)
As 0.49 (0) 0.49 (0) *** 0.49 (0) 0.49 (0) *** 0.49 (0) 0.49 (0)
Cs 0.34 (0.18) 0.39 (0.19) *** 0.33 (0.16) 0.42 (0.2) *** 0.35 (0.19) 0.42 (0.19) ***
Cu 1,895 (463) 1,875 (464) 1,903 (454) 1,855 (460) *1,894 (475) 1,848 (453)
Hg 0.19 (0.17) 0.19 (0.26) *** 0.19 (0.14) 0.29 (0.33) *** 0.19 (0.22) 0.19 (0.26)
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Nature Food | Volume 5 | July 2024 | 563–568 565
Brief Communication https://doi.org/10.1038/s43016-024-01013-x
may raise caution in monitoring chemical exposure introduced by
specific food consumption that is linked to healthy dietary patterns,
and enrich dietary recommendations by promoting a healthy diet while
minimizing the intake of toxic chemicals. In a large racially and ethni-
cally diverse pregnancy cohort, we aimed to examine associations of
three dietary patterns with a comprehensive panel of environmental
chemicals and evaluate whether such associations could be attributed
to the consumption of specific food groups.
Results
Food groups and nutrients that constitute the three dietary patterns
and the amounts consumed are listed in Supplementary Table 1. The
characteristics of study participants are presented in Supplementary
Table 2. Plasma concentration differences of chemicals according
to aMED, aHEI and DASH scores (high versus low, dichotomized by
median) are presented in Table 1 and Supplementary Table 3. Differ-
ences in the levels of chemicals between participants with high versus
low dietary pattern scores were observed. Specifically, compared with
those with lower scores, participants with higher aMED or aHEI scores
had higher concentrations of perfluorododecanoic acid (PFDoDA),
perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), per-
fluoroundecanoic acid (PFUnDA), total PFASs, caesium (Cs), mercury
(Hg), molybdenum (Mo) and thallium (Tl) and lower concentrations
of N-methylperfluoro-1-octanesulfonamidoacetic acid (NMeFOSAA),
copper (Cu), zinc (Zn) and total metals. Higher scores of healthy dietary
patterns tended to be linked to higher organochlorine pesticide (OCP)
concentrations. On the contrary, participants with high dietary pattern
scores tended to have low concentrations of total polybrominated
diphenyl ethers (PBDEs), BDE47, BDE100, BDE99 and BDE183.
Overall, we observed significant and positive associations of
the three healthy dietary pattern scores with plasma concentra-
tions of PCBs, with notable associations observed for aMED and aHEI
with PCBs. Specifically, aMED and aHEI were positively associated
with total PCBs, with per cent change (95% CI) of 5.2 (2.5–8.0) and
1.3 (0.8–1.8), respectively (Fig. 1a and Supplementary Table 4).
Positive associations with aMED and aHEI were observed for the major-
ity of individual PCB congeners. Adherence to aMED was positively
associated with PFDoDA, PFDA and PFUnDA. Adherence to aHEI was
positively associated with perfluorodecane sulfonate (PFDS), PFDoDA,
PFNA, PFDA and PFUnDA but inversely associated with NMeFOSAA.
aHEI score was positively associated with β-hexachlorocyclohexane
(BetaHCH), p,p′-dichlorodiphenyldichloroethylene (P_P_
DDE), p,p′-dichlorodiphenyldichloroethane (P_P_DDD), p,p′-
dichlorodiphenyltrichloroethane (P_P_DDT) and total OCPs as well.
The DASH score was inversely associated with NMeFOSAA and perfluo-
rooctanesulfonic acid (PFOS). The aMED was inversely associated with
Zn and total metals; the aHEI score was positively associated with As,
Cs and Hg but inversely associated with selenium (Se).
Reduced rank regression (RRR) analyses indicated that fish (0.52)
and eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) (0.56)
were the highest loading factors for the variation in total chemicals
(Fig. 1c). Loading factors of similar food groups/nutrients were
observed for variations in PCBs and PFASs, with 0.57 and 0.61 for fish
and 0.63 and 0.64 for EPA + DHA, respectively. The ratio of monoun-
saturated fatty acids (MUFAs) to saturated fatty acids (SFAs) (0.63)
and vegetables (0.33) were high loading factors for OCPs. Fish (0.52),
EPA + DHA (0.55) and vegetable (0.40) contributed positively to the
variation in metals. Consistent findings were observed in multivariable
linear regression models (Supplementary Fig. 3).
Associations of the healthy dietary pattern scores with chemi-
cals appeared to be more pronounced in Asian and Pacific Islanders
(Supplementary Fig. 4a) than in other race or ethnic groups, with P for
interaction <0.05 (Supplementary Fig. 4b). Group differences between
different races and ethnicities were mainly observed in the associa-
tions of aHEI with plasma PCB and PFAS concentrations. Meanwhile,
the race and ethnic heterogeneity in the associations between food
groups and chemicals was more pronounced in fish and EPA + DHA
(Supplementary Fig. 5).
Overall, primary findings on the associations of dietary pattern
scores with chemical concentrations were consistent and did not
change materially after applying inverse probability weighting to
represent the total cohort population8 and after additional adjustment
for total lipids (Supplementary Tables 5 and 6), modelling chemicals
as a binary variable (≥80th percentile versus <80th percentile)
(Supplementary Table 7), controlling for clinical centres as random
effect intercept (Supplementary Table 8), or imputing chemical values
below the limit of detection (Supplementary Table 9). After exclud-
ing nutrients that were used to calculate dietary pattern scores, fish
consumption remained the highest loading factor, indicating the
highest contribution to variation in total chemicals, total PCBs and total
PFASs (Supplementary Fig. 6). Results of the elastic network regression
analyses were in line with results from RRR (Supplementary Fig. 7).
Discussion
In this large multi-racial pregnancy cohort in the United States,
we observed that greater adherence to aMED, aHEI and DASH in
peri-conception and early pregnancy was significantly associated with
higher levels of plasma chemical concentrations. The associations
were most pronounced for aMED and aHEI with PCBs and PFASs and
appeared driven mainly by the associations with fish, EPA + DHA, the
MUFA:SFA ratio and vegetables. These associations were more pro-
nounced in the Asian and Pacific Islander population. Given that the
World Health Organization and the Food and Agricultural Organization
of the United Nations both recommend the consumption of a combina-
tion of healthy foods rather than single foods to avoid over-nutrition or
nutritional deficiency9, findings from this study provide a more holistic
portrayal of the interplay between diet and chemicals coming from diet.
Our study systematically examined the associations between
dietary patterns and concentrations of diverse chemicals in a large
sample of women. Our findings provide a significant clue for future
aMED aHEI DASH
Chemical Low aMED High aMED Adjusted
P value Low aHEI High aHEI Adjusted
P value Low DASH High DASH Adjusted
P value
Mo 1.74 (1.18) 2.01 (1.3) *** 1.77 (1.16) 2.02 (1.28) *** 1.84 (1.21) 2.04 (1.28) **
Tl 0.02 (0.01) 0.02 (0.02) *0.02 (0.01) 0.03 (0.02) *0.02 (0.01) 0.02 (0.02)
Zn 817 (201) 776 (182) *** 813 (191) 775 (184) *** 810 (191) 753 (176) ***
Total metals 2,827 (533) 2,784 (523) *2,823 (512) 2,773 (545) *2,821 (520) 2,739 (539) ***
Signiicant chemicals are presented, while the complete table can be found in Supplementary Table 3. Group differences of chemicals (high versus low, dichotomized by the median of each
dietary pattern score) were examined by a non-parametric test. To account for multiple comparisons, Benjamini–Hochberg (BH)-adjusted and two-sided P values were calculated. *P<0.05,
**P<0.01, ***P<0.001. Chemicals (ngg−1 lipid, except for PFASs and metals, ngml−1) were standardized by total lipids.
Table 1 | (continued) Plasma concentration differences of chemicals according to aMED, aHEI and DASH scores among the
NICHD Fetal Growth Study–Singletons cohort
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Nature Food | Volume 5 | July 2024 | 563–568 566
Brief Communication https://doi.org/10.1038/s43016-024-01013-x
1.80
0.01
0.95
−1.83
0.23
−0.73
2.11
−0.62
2.43
2.34
1.32
1.88
0.16
−1.51
−1.48
−1.25
1.14
−0.04
−0.54
−1.64
0.29
−1.77
3.04
2.70
2.75
3.16
3.72*
2.41
1.78
2.34
1.93
2.02
3.37
2.92
3.91*
2.95
4.22*
4.39*
3.13
4.27*
1.57
4.33*
5.52**
4.21*
1.81
4.37*
2.99
4.29*
3.82*
1.94
6.17***
4.45*
5.44**
4.55*
5.78***
5.81***
5.17**
6.31***
4.88**
3.47
6.35***
4.41*
5.45**
5.21***
5.23**
−3.76
1.81
4.70**
3.30
−1.59
0.60
1.70
2.94
4.53*
6.54***
0.53
1.98
−0.16
0.83
0.82
3.85
2.88
−2.23
2.24
−2.45
2.73
−0.45
−0.93
−1.83
0.12
1.12
−4.62*
−4.22*
0.77**
−0.13
0.39
−0.37
−0.21
−0.36
0.90**
−0.14
0.88**
1.05**
0.58
0.81**
−0.23
−0.32
−0.30
0.01
0.24
0.25
0.09
−0.27
0.15
−0.25
0.47
0.38
0.44
0.80*
0.99**
0.50
0.65*
0.91**
0.64
0.69*
0.82**
0.88**
1.44***
0.55
0.90**
1.10***
0.56
1.11***
0.68*
1.36***
1.56***
1.20***
0.73*
1.27***
1.07***
1.24***
1.04***
0.75*
1.45***
1.12***
1.28***
0.96**
1.16***
1.22***
0.98**
1.20***
0.86**
0.74*
1.13***
1.07***
1.11***
1.10***
1.33***
−0.95**
0.80*
1.45***
0.17
0.11
0.41
0.39
0.86**
1.28***
2.09***
0.50
0.89**
−0.47
0.28
−0.19
0.47
1.17***
−0.46
1.36***
−0.03
0.29
−0.18
0.02
−1.07**
−0.40
0.52
−0.64
−0.71*
1.21
0.01
0.86
−1.03
−0.45
−1.00
1.21
−0.84
1.30
1.73
0.32
1.19
0.43
−0.68
−0.67
−0.86
0.10
0.53
−0.17
−0.39
0.06
−0.85
0.56
0.63
0.87
1.14
1.26
0.57
0.66
1.06
0.66
0.87
0.27
1.14
1.34
0.53
0.88
−0.01
0.59
0.07
0.50
0.19
0.42
0.45
−0.30
0.74
0.60
0.66
0.38
0.22
0.94
0.58
0.85
1.10
1.05
0.94
0.71
0.88
0.61
0.96
1.59
0.81
1.21
0.66
0.70
−2.42*
−0.14
0.05
1.46
−1.04
−1.96*
−0.02
−0.34
−0.38
0.07
−1.71
−0.31
−0.70
1.35
0.46
0.55
1.29
0.03
0.31
−0.83
1.06
−0.46
0.49
−0.91
−0.71
1.01
−1.62
−0.85
BetaHCH
GammaHCH
HCB
Oxychlordane
TransChlordane
TransNo_chlor
P_P_DDE
O_P_DDD
P_P_DDD
P_P_DDT
Mirex
Total OCPs
BDE28
BDE47
BDE100
BDE99
BDE85
PBB153
BDE154
BDE153
BDE183
Total PBDEs
PCB5_8
PCB18_17
PCB31_28
PCB33_20
PCB22
PCB52_73
PCB49_43
PCB47_48_75
PCB44
PCB41_64
PCB74_61
PCB70_76
PCB66_80
PCB93_95
PCB90_101_89
PCB99
PCB110
PCB118_106
PCB114_122
PCB105_127
PCB146_161
PCB153
PCB137
PCB138_158
PCB128
PCB167
PCB156
PCB157
PCB182_187
PCB183
PCB177
PCB172_192
PCB180
PCB170
PCB202
PCB199
PCB196_203
PCB195
PCB194
PCB208
PCB206
PCB209
Total PCBs
NMeFOSAA
PFDS
PFDoDA
PFHpA
PFHxS
PFOS
PFOA
PFNA
PFDA
PFUnDA
Total PFASs
As
Ba
Cd
Co
Cr
Cs
Cu
Hg
Mn
Mo
Pb
Sb
Se
Sn
Tl
Zn
Total metals
aMED
aHEI
DASH
−4
−2
0
2
4
6
Fruit
Vegetable
Nut and legume
Whole grain
Low-fat dairy
Sodium
Red and processed meat
SSB (excluding juice)
Whole fruit
SSB (including juice)
Trans fat
EPA + DHA
Z1
OCPs (11):
BetaHCH
GammaHCH
HCB
Oxychlordane
TransChlordane
TransNo_chlor
P_P_DDE
O_P_DDD
P_P_DDD
P_P_DDT
Mirex
21 constituent food groups
Group1
PUFA excluding EPA and DHA
Legume
Nut
Fish
MUFA:SFA ratio
Fruit & nut
Dairy
Grain
Meat
…Zr
PBDEs (9):
BDE28
BDE47
BDE100
BDE99
BDE85
PBB153
BDE154
BDE153
BDE183
PFASs (10):
NMeFOSAA
PFDS
PFDoDA
PFHpA
PFHxS
PFOS
PFOA
PFNA
PFDA
PFUnDA
PCB5_8
PCB18_17
PCB31_28
PCB33_20
PCB22
PCB52_73
PCB49_43
PCB47_48_75
PCB44
PCB41_64
PCB74_61
PCB70_76
PCB66_80
PCB93_95
PCB90_101_89
PCB99
PCB110
PCB118_106
PCB114_122
PCB105_127
PCB146_161
PCBs (42)
PCB153
PCB137
PCB138_158
PCB128
PCB167
PCB156
PCB157
PCB182_187
PCB183
PCB177
PCB172_192
PCB180
PCB170
PCB202
PCB199
PCB196_203
PCB195
PCB194
PCB208
PCB206
PCB209
Metals (16)
As
Ba
Cd
Co
Cr
Cs
Cu
Hg
Mn
Mo
Pb
Sb
Se
Sn
Tl
Zn
Total OCPs
Total PBDEs
Total PCBs
Total PFASs
Total metals
Total chemicals:
Group 2 Group 20 Group 21
…
Latent variables
Multiple exposure variables
Multivariate outcomes for each class
0.52
0.56
−0.09
0.03
0.10
−0.17
−0.14
0.09
−0.31
−0.02
−0.29
−0.13
−0.08
−0.05
−0.03
−0.03
−0.13
−0.13
−0.20
−0.09
−0.22
0.28
0.29
−0.06
−0.15
−0.24
−0.07
−0.18
0.63
0.22
0.33
0.21
−0.02
0.07
0.16
0.12
0.11
−0.01
−0.01
0.20
0.07
0.15
−0.07
−0.11
0.31
0.26
0.13
−0.01
0.31
−0.43
0.10
−0.06
−0.38
−0.01
−0.01
0.01
−0.02
−0.30
−0.29
−0.30
−0.15
−0.14
−0.23
0.57
0.63
0.02
0.02
0.01
−0.15
−0.29
0.24
−0.01
0.18
0.16
−0.00
0.03
0.05
0.07
−0.01
0.10
0.10
−0.01
−0.11
−0.10
0.61
0.64
−0.12
−0.08
−0.07
−0.21
−0.10
0.21
−0.13
0.12
−0.13
−0.01
0.01
0.01
0.00
0.04
−0.11
−0.11
−0.04
−0.11
−0.07
0.52
0.55
−0.06
−0.16
−0.16
−0.15
−0.20
0.21
0.23
0.40
0.02
0.05
0.10
0.05
0.07
−0.01
−0.03
−0.03
0.12
−0.04
−0.09
Total chemicals
OCPs
PBDEs
PCBs
PFASs
Metals
Fish
EPA + DHA
Whole grain
SSB (excluding juice)
SSB (including juice)
Dairy
Trans fat
MUFA:SFA ratio
Legume
Vegetable
Whole fruit
Grain
Sodium
Red and processed meat
Meat
Nut
Fruit
Fruit and nut
Nut and legume
Low-fat dairy
PUFA excluding EPA & DHA
−0.4
−0.2
0
0.2
0.4
0.6
a b
c
Change (%)
Loading factor
Fig. 1 | Associations of different dietary patterns with chemicals among
the NICHD Fetal Growth Study–Singleton cohort. a, Per cent difference in
grouped and individual plasma chemical concentrations per 1 s.d. increase in
dietary pattern indices of aHEI, aMED and DASH. All estimations were assessed
by multivariable linear regression models with adjustment for maternal race/
ethnicity, age, physical activity level, pre-pregnancy body mass index (BMI),
education level, income, parity, tobacco exposure and total energy intake.
Significance with two-sided raw P value <0.05 is bolded. To account for multiple
comparisons, Benjamini–Hochberg (BH)-adjusted P values were calculated.
*P < 0.001, **P < 0.01, ***P < 0.05. Per cent change ((exp(β) − 1) × 100) was reported
to benefit interpretation. b, Conceptual diagram of the Kernal RRR. The black
arrows represent the dependency structure. c, The loading effect of different
food groups and components on chemical classes with residuals of the above
confounders adjusted, which can help describe the strength and directionality
of how the intake of each food group is loaded onto a specific dietary pattern
with different chemical classes. SSB, sugar-sweetened beverage; PUFA,
polyunsaturated fatty acid.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Nature Food | Volume 5 | July 2024 | 563–568 567
Brief Communication https://doi.org/10.1038/s43016-024-01013-x
investigations into the joint impacts of these factors on pregnancy and
foetal outcomes. Only one previous study examined such associations,
albeit using umbilical cord blood10. Studies among non-pregnant
individuals are sparse, too, with only two studies identified and
inconsistent associations reported
11,12
. Inferences from these studies
were limited by their relatively small sample size, measured chemi-
cal classes, absence of food groups/nutrients and diet variations
by regions and populations. The present study examined multiple
purportedly healthy dietary patterns and diverse classes of chemi-
cals comprehensively, which can facilitate the identification of toxic
chemicals associated with healthy dietary patterns, shedding light on
the previously overlooked aspect of single pattern or chemical class
in similar investigations.
Seafood and aquatic products may have driven the associations
of aMED and aHEI with PFASs and PCBs as the calculation of dietary
pattern scores include fish and EPA + DHA (supplemental use was not
included) for aMED and aHEI, respectively. The majority of EPA + DHA
comes from fish and other seafood consumption. Fish consumption is
one of the major sources of exposure to PFAS and PCB in humans among
diverse populations of different ages
13,14
. Besides, inverse associations
of aHEI, fish and EPA + DHA with the precursor NMeFOSAA suggest that
the transformation reactions may have occurred already. While specific
PFAS control standards for human blood have not been established, the
US Environmental Protection Agency is consistently lowering environ-
mental limits for PFAS exposures from food sources due to a growing
body of evidence indicating their potential metabolic and reproductive
toxicity, even at low dose
15
. Similarly, PCB concentrations in marine
fish are generally higher than in other foods. Despite that most of the
PCBs have been phased out for many years, people are still at risk of
exposure due to historical emission and multi-media transportation
16
.
Even though fish is generally regarded as healthy food, it also car-
ries specific heavy metals such as As and Hg owing to their bioaccumu-
lation along the food chain
17
. Women with high aHEI scores in our study
have comparable metal concentrations to participants of the National
Health and Nutrition Examination Survey (NHANES) conducted in
the same study period (2009–2012)18. Despite the low detection rates
of toxic heavy metals in our present study, the extrapolation to the
broader population suggests a potential public health threat and that
raising the awareness of heavy metal contamination for healthy food in
the general public is needed. Similarly, associations between OCPs and
vegetable, whole fruit and the MUFA:SFA ratio in this study have shown
that, even though some OCPs have been phased out, the US population
is still at risk due to their historical release or transportation globally
through various environmental media. Consistent associations were
found in European and Asian populations
14,19
, suggesting a potentially
worldwide threat to food safety.
The more pronounced associations among Asians and Pacific
Islanders may be due to higher consumption of fish and EPA + DHA
among the Asian and Pacific Islander ethnic groups than other races and
ethnicities
16
. Thus, the health benefits of a given healthy dietary pat-
tern may vary across ethnic groups, and population-specific targeted
dietary guidance that considers the burden from potential pollutant
exposure is needed to optimize health interventions.
This study is distinguished by several notable strengths. First,
this study examined the association of three of the most widely rec-
ommended dietary patterns with a comprehensive profile of envi-
ronmental chemicals in a relatively large multi-racial population.
Second, we used a well-validated food frequency questionnaire (FFQ) to
capture long-term habitual dietary patterns, and detailed information
on covariates was assessed to control potential confounders. Third, we
examined the associations of healthy dietary patterns with multiple
chemicals simultaneously and further examined the associated drivers
of food groups and nutrients.
Several potential limitations merit consideration. First, as with
the nature of other observational studies, we cannot completely rule
out the possibility of impacts from unmeasured confounders such as
variables related to air and water sources of chemicals and chemical
accumulation across organs. Second, given that our study was con-
ducted on multi-racial cohort, it is possible that FFQ may not capture
all the commonly consumed foods in each race and ethnicity group.
It would be optimal to develop and apply race- and ethnicity-specific
FFQ to assess habitual dietary patterns. Third, it is noteworthy that our
population was exposed to relatively low chemical levels compared
with other populations at the same period. Thus, caution should be
exercised when extrapolating our findings. Though the effect size
of associations was modest, considering the long-term and gradual
accumulation of chemicals from diet over years, it underscores the
significance of considering the combined impact of chemicals and
dietary patterns on human health, particularly for countries where
pollution levels are rising.
In conclusion, we observed that greater adherence to healthy
dietary patterns was associated with higher chemical exposure, espe
-
cially aMED and aHEI dietary patterns with PCBs and PFASs. The asso-
ciations were probably driven by the consumption of fish and related
ingredients and appeared to be more pronounced among Asian and
Pacific Islanders. Strengthening the regulation and supervision of chem-
icals in fish (PCBs and PFASs) and vegetables (OCPs) is critical, espe-
cially for unregulated seafood markets, coastal dwellers and farmers.
Collective efforts from both government and society along with uni-
fied worldwide regulatory efforts are pivotal in mitigating exposure to
hazardous chemicals, particularly for vulnerable populations such as
pregnant women. Findings from the present study suggest that future
studies characterizing healthy diets should consider both healthy food
and nutrient components and related chemical exposure to better
optimize the health benefits of healthy diets.
Methods
Detailed methods beyond the shortened description are provided in
Supplementary Information. Briefly, the study was based on pregnant
individuals from the Eunice Kennedy Shriver National Institute of Child
Health and Human Development (NICHD) Fetal Growth Studies–Sin-
gletons, and 1,618 women with both chemical measurements and FFQ
data were included (Supplementary Fig. 1)
8
. Averaged maternal age of
the included women was 28 (standard deviation (s.d.) 9) years, and all
recruited women provided written informed consent. This study was
approved by the institutional review board of the National Institutes of
Health, and all participating clinical sites with a Clinical Trial Registry
registered (NCT 00912132). aMED, aHEI and DASH scores were derived
from FFQ, and 88 of 97 chemicals of different classes with detection
rates above 1% were analysed. Covariates were selected on the basis of
a priori evidence outlined in a causal diagram using a directed acyclic
graph (Supplementary Fig. 2).
Multivariable linear regression models were applied to assess
associations of individual dietary pattern scores and food components
with each of the chemicals. To aid in the interpretation, β coefficients
were converted into per cent difference using the following formula:
(e
β
− 1) × 100. Stratified analyses for different covariates were con-
ducted to explore potential effect modifications. All significant levels
of the P values were adjusted by the Benjamini–Hochberg procedure.
Meanwhile, we performed RRR analysis to assess the contribution of
each constituent food group and nutrient to the variations of individual
chemicals and chemical classes (Fig. 1b)20.
Several additional analyses were conducted to validate the robust-
ness of our results, including analysis that (1) incorporated the popula-
tion weight by inverse probability weighting; (2) additionally adjusted
for total lipids; (3) dichotomized each of the chemicals according to
the 80th percentile (high level: ≥80th, common level <80th); (4) addi-
tionally adjusted for clinical centres as a random effect intercept using
generalized linear mixed models; (5) excluded EPA + DHA, trans fat and
the MUFA:SFA ratio in RRR analyses and elastic network regression
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Nature Food | Volume 5 | July 2024 | 563–568 568
Brief Communication https://doi.org/10.1038/s43016-024-01013-x
models; (6) applied multivariate imputation by chained equations to
impute chemical values below the limit of detection.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The data used in this study are not publicly available due to privacy and
confidentiality agreements. Access to the data is restricted to protect
the personal and health information of the participants, in accord-
ance with ethical guidelines and regulations. Researchers interested
in accessing the data may contact the corresponding author with a
detailed request and may be required to sign a data use agreement to
ensure the protection of participant confidentiality.
Code availability
Open source codes and scripts used for the analyses or figures are
publicly available at the GitHub repository (https://github.com/
GuoqiYu2023/Nature-Food-2024).
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Acknowledgements
The NICHD Fetal Growth Study–Singletons cohort and researchers
(J.W., Z.C. and C.L.Z) were supported by the Division of Population
Health Research, Division of Intramural Research, Eunice
Kennedy Shriver NICHD, National Institutes of Health (contract
numbers HHSN275200800013C, HHSN275200800002I,
HHSN27500006, HHSN275200800003IC, HHSN275200800014C,
HHSN275200800012C, HHSN275200800028C and
HHSN275201000009C). A.B. was supported by the German
Research Foundation (DFG) (individual fellowship BI 2427/1-1). Q.S.
was supported by the NIH grant ES022981. The funders had no role
in study design, data collection and analysis, decision to publish or
preparation of the manuscript.
Author contributions
G.Y. participated in the data analysis and interpretation and wrote
the initial draft of the paper. C.Z. conceived the study concept and
designed the study. R.L. and J.Y. were responsible for data curation
and interpretation. M.L.R., L.-J.L., D.D.W., Q.S., W.W.P., C.G., A.B., J.G.
and Z.C. contributed to data interpretation and revision of the paper.
C.Z. obtained funding and supervised the study. All authors reviewed,
edited and approved the inal version of the paper. C.Z. and G.Y. are
the guarantors of this work and, as such, had full access to all the data
in the study and take responsibility for the integrity of data and the
accuracy of data analysis.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s43016-024-01013-x.
Correspondence and requests for materials should be addressed to
Cuilin Zhang.
Peer review information Nature Food thanks Anne Lise Brantsæter,
Jesse Goodrich and Thorhallur Ingi Halldorsson for their contribution
to the peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional ailiations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
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as long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
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use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons.
org/licenses/by/4.0/.
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Last updated by author(s): Jun 11, 2024
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were investigated, no gender- or sex-based analysis was performed.
Population characteristics We included a total of 1,618 pregnancies from 2,802 women who gave birth between 2009-2013 in our cohort. The
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tolerance test (OGTT) during 24-28 weeks of gestation, was 3.8%. Mean age at pregnancy initiation was 28.0, 56.7% women
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