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. Author manuscript; available in PMC: 2025 Aug 21.
Published in final edited form as: Chronobiol Int. 2025 Jul 23;42(9):1145–1160. doi: 10.1080/07420528.2025.2532796

Adolescent dietary patterns and methyl-donor nutrient intakes in relation to blood leukocyte DNA methylation of circadian genes

Jennifer T Lee a, Jaclyn M Goodrich b, Dana C Dolinoy b, Karen E Peterson a, Martha M Téllez-Rojo c, Alejandra Cantoral c, Libni A Torres-Olascoaga c, Edward A Ruiz-Narváez a, Erica C Jansen a,d
PMCID: PMC12366751  NIHMSID: NIHMS2102323  PMID: 40698944

Abstract

Dietary composition may impact circadian rhythms, potentially through DNA methylation of circadian genes. However, research among adolescents remains limited. Cross-sectional association of three dietary patterns, derived from principal component analysis of energy-adjusted food groups, and five energy-adjusted methyl-donor nutrients (folate, methionine, riboflavin, and vitamins B6 and B12) on DNA methylation of 18 circadian-related genes in 526 adolescents was examined. DNA methylation levels at CpG sites were quantified from blood leukocytes using the Illumina Infinium Methylation EPIC BeadChip, with false discovery rate adjustments (q ≤ 0.20). Linear regression was used, adjusting for age, sex, maternal education, smoking status, batch effects, and cell-type proportions. Correlations between CpG sites and gene expression data (RNA-seq) of the corresponding genes were evaluated. Riboflavin was negatively associated with cg06337557 (MTNR1B) and cg02076826 (RORA). Vitamin B6 was positively associated with cg09615953 (PER3) and negatively with cg06337557 (MTNR1B). In males, the Breakfast pattern was negatively associated with cg13146553 (RORA), and riboflavin was positively associated with cg06487986 (PER3). No significant associations were found for the Plant-based & lean proteins pattern, folate, methionine, or vitamin B12. DNA methylation of the 18 clock genes were not correlated with gene expression data of the corresponding genes. Dietary patterns and methyl-donor nutrients may influence core clock and melatonin-related genes, with potential sex-specific relationships.

Keywords: Dietary patterns, methyl-donor nutrients, circadian genes, clock genes, DNA methylation, epigenetics, dietary intake

Introduction

The circadian system, an intrinsic timekeeping system signaled by melatonin, governs various physiological activities including sleep-wake cycles, hormone secretion, body temperature, and metabolism (Lee et al. 2021; Patke et al. 2020; Qian and Scheer 2016). This intricate system is tightly controlled by molecular transcriptional and translational feedback loops, which instruct the 24 h rhythmic fluctuations on the expression pattern of clock genes through epigenetic mechanisms (Fagiani et al. 2022). Dietary patterns and nutrient intakes can influence the regulation of circadian rhythms and, consequently, impact overall health (Sato and Sassone-Corsi 2022).

Adolescents experience a delay in circadian phase, characterized by a shift towards later sleep-wake cycle compared to adults. This preference for a later sleep schedule appears to have strong biological underpinnings and sex specificity, with girls initiating this transition approximately 1 year earlier than boys, coinciding with their earlier onset of puberty (Roenneberg et al. 2004). Notably, sleep initiation, as signaled by melatonin, has been linked to epigenetic regulation of circadian genes in adolescents (Larsen et al. 2023). Adults exposed to disrupted circadian rhythms, such as shift workers, exhibit differential DNA methylation patterns in circadian genes compared to day-shift workers (Bhatti et al. 2015; Reszka et al. 2018; Ritonja et al. 2022). DNA methylation, an epigenetic modification involving the addition of methyl groups to DNA molecules at cytosine residues within cytosine-guanine (CpG) dinucleotides (Papazyan et al. 2016), serves as a regulatory mechanism for gene expression. Distinct gene expression patterns and circadian misalignment, including desynchronization in dim light melatonin onset, core body temperature, and peak cortisol, have been observed between night and day shift workers (Resuehr et al. 2019). Furthermore, alterations in DNA methylation of circadian genes have been linked to obesity, increased waist circumference, impaired glucose tolerance, high blood pressure, and metabolic syndrome (Milagro et al. 2012; Peng et al. 2019; Ramos-Lopez et al. 2018).

Prior studies of adult shift workers have not determined whether alterations in DNA methylation of circadian genes are primarily caused by disruptions in sleep or by alterations in dietary timing and/or composition. Whereas most prior work focuses on the role of sleep, the potential influence of diet is also plausible. Emerging research suggests that both consuming a Mediterranean diet and intake of specific nutrients can affect DNA methylation of circadian genes in adults (Milagro et al. 2012; Ramos-Lopez et al. 2018; Samblas et al. 2016), potentially impacting circadian gene expression and rhythmicity. DNA methylation relies on the availability of methyl groups and the activities of methyl donors (Ducker and Rabinowitz 2017). Micronutrients such as folate, methionine, vitamin B12, and other B vitamins contribute to this process as methyl donors and co-factors (Zeisel 2017). Changes in intake of these methyl-donor nutrients can disrupt methyl metabolism, thereby impacting DNA methylation of circadian genes and subsequently affecting circadian rhythm. Understanding how dietary intake may influence DNA methylation of circadian genes may help elucidate the role of diet in regulating circadian rhythm.

Despite the growing body of evidence, limited research has examined the relationship between dietary patterns and dietary intake of methyl-donor nutrients with DNA methylation of circadian and circadian-related genes among adolescents. Our group previously demonstrated a potential link between dietary patterns and DNA methylation of 12 core circadian genes and sex-specific differential DNA methylation patterns in 142 Mexican adolescents (Jansen et al. 2021). Expanding upon our prior work, the present study aimed to further investigate the relationship between dietary patterns and dietary intake of methyl-donor nutrients with DNA methylation of 18 circadian-related genes in a larger adolescent population in Mexico City. This study sought to shed light on the potential mechanisms underlying the interplay between diet, epigenetics, and circadian rhythm regulation during this important developmental period.

Materials and Methods

Study Population

The study population included adolescents from the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) cohort (Perng et al. 2019). Between 1997 and 2004, 1012 mother and child dyads were recruited from prenatal clinics of the Mexican Social Security Institute in Mexico City and followed periodically since the original recruitment. The clinics mainly served low- to middle-income populations. Children were followed from birth to mid-childhood and adolescence. In 2015, a follow-up visit was conducted on a subset of 550 adolescents during pubertal transition (between ages 9–18 y) in the research facility utilized by the project provided by the ABC hospital of Mexico City. The present study included 526 adolescents with available DNA methylation data and complete data on covariates. The research protocols were approved by the institutional review boards at the Mexico National Institute of Public Health and the University of Michigan. Informed consent was obtained from parents for all participants in addition to participant assent.

Dietary Intake

Information on usual dietary intake was collected by a trained interviewer using a 116-item semi-quantitative food frequency questionnaire (FFQ) adapted from the 2006 Mexican National Health and Nutrition Survey (Denova-Gutiérrez et al. 2016). Age-specific FFQs were used for children aged 7–11 y and 12 y or older. To improve the accuracy and precision of dietary intakes, children aged 7–11 y were assisted by their mothers as needed. The questionnaire asked the adolescents to recall the frequency and portion size of each food item over the previous week. Nine response options ranged from never to ≥ 6 times per day. As previously described (Jansen, Marcovitch, et al. 2020), similar food items were categorized into food groups based on their nutritional similarity. Then total-energy adjusted food group intakes were calculated using the residual method (Willett et al. 1997). Continuous values representing adherence to three dietary patterns were derived from principal component analysis based on all the energy-adjusted food groups, with greater scores representing consumption patterns more highly aligned with the pattern (Jansen, Marcovitch, et al. 2020). The Plant-based & lean proteins pattern was characterized by high intake frequency of the vegetables, fruits, soup, fish, water and unsweetened drinks, and high-fat dairy food groups. The Meat & starchy foods pattern had high intake frequency of “Western” processed foods including chips, refined grains, sugar-sweetened beverages, sweets and desserts, processed meat, high-fat dairy, Mexican food groups (tacos, quesadillas), potatoes and fried plantains, soup, legumes, corn tortillas, and pork. The Eggs, milk & refined grain (“Breakfast”) pattern was characterized by high intake frequency of refined grains, milk, sweetened milk, mayonnaise or margarine, and eggs food groups. Estimated daily dietary intakes of energy (kcal), folate, methionine, riboflavin, vitamin B6 and vitamin B12 were calculated using a software developed by the National Institute of Public Health (National Institute of Public Health 2002) by multiplying nutrient contents in each food item with frequency of usual intake, and taking the sum of each nutrient across all food items. Nutrient intakes were adjusted for total energy intake using the residual method (Willett et al. 1997).

DNA Methylation of Circadian Genes

Methods were previously described (Ehlinger et al. 2023; Jansen et al. 2021). Briefly, DNA was extracted from blood leukocytes using the Flexigene kit (Qiagen) followed by treatment with sodium bisulfite (Grunau et al. 2001). DNA methylation levels at over 850,000 CpG sites located in various genomic regions, including gene promoters, enhancers, gene bodies, and intergenic regions, were assessed using the Illumina Infinium MethylationEPIC BeadChip (Moran et al. 2016). Samples that had been bisulfite converted were randomly distributed across different chips and chip positions, then hybridized onto BeadChips across 3 batches at the University of Michigan Advanced Genomics Core. As previously described (Ehlinger et al. 2023), data were pre-processed including background correction with noob and dye bias correction with RELIC followed by quantile normalization (Pidsley et al. 2016; Xu et al. 2017). Probes that exhibited inadequate detection (present in at least 5% of samples), those recognized for cross-reactivity, and those containing polymorphisms in either the CpG site or the single base extension site were eliminated from the analysis. Estimates of cell-type composition in blood leukocyte samples were derived using information from tissue-specific differentially methylated regions incorporated into the BeadChip. The resulting dataset comprised beta values representing the proportion of methylation (ranging from 0 to 1) at individual CpG sites. This study used probes annotated to 18 circadian genes, including 12 core circadian genes (BMAL1, CLOCK, CRY1, CRY2, NR1D1, NR1D2, PER1, PER2, PER3, RORA, RORB, RORC), two circadian regulator genes (TIMELESS, NPAS2), two melatonin-responsive genes (NPAS4, NEUROD1), and two melatonin-receptor genes (MTNR1A, MTNR1B).

RNA Sequencing of Circadian Genes

Gene expression data for the 18 circadian-related genes was obtained using next-generation sequencing of RNA (RNA-seq) for a subset of adolescents (n = 203). As previously described (Jansen et al. 2021), whole blood was collected in EDTA-containing tubes during participants’ mid-morning clinic visit. White blood cells were then extracted and preserved in RNALater. These samples were stored at − 80°C until further processing. The isolation of RNA was performed using the Maxwell RSC simplyRNA tissue kit (Mawell; catalog # AS1340). The RNA quality and quantity assessments, as well as library preparation, were performed at the Advanced Genomics Core at the University of Michigan. To create the libraries, Universal Plus mRNA-Seq with Human globin AnyDeplete from NuGEN Technologies, Inc. was utilized, which effectively removes the abundant globin transcripts typically found in blood samples. Samples underwent 151bp paired-end sequencing on an Illumina HiSeq 4000. Each sample’s raw read data quality was evaluated using FastQC (version 0.11.8). For alignment, ENCODE standards for RNA-seq were followed (Dobin et al. 2013), followed by a second round of quality control examining expression on Y chromosome genes. RNA-seq analyses were performed on 203 adolescents with dietary intake data. Results for gene expression levels of RORB, NPAS4, NEUROD1, MTNR1A and MTNR1B were not included as their median values across all samples are below <10.

Covariates

During an in-person visit, interviewers collected data on age in years (continuous), sex (categorized as male or female), and ever smoking using the question: “Have you ever smoked in your life, even a single puff?” with possible responses being yes, no, or don’t know. Maternal education, reported by mothers at enrollment, was categorized into four levels: none or elementary school, middle school, commercial or technical career, or high school and above.

Additional information was collected on household socioeconomic status (SES), sedentary behavior, physical activity, pubertal status, and alcohol consumption. Household SES was assessed using a validated 13-item questionnaire developed by the Asociación Mexicana de Agencias de Investigación de Mercados y Opinión Pública (AMAI), version 13x6, administered during the in-person visit. This questionnaire was validated against results from the National Survey of Household Income and Expenditure 2005 in Mexico (Jansen et al. 2017). Adolescents (with input from mothers when available) answered questions about their household resources, including housing quality, available services, material possessions, and the education level of the head of the household. SES was classified into six categories (highest to lowest A/B, C+, C, D+, D, E). The average duration of sedentary behavior and physical activity (in min per day) was estimated from data collected over a 7-d period using ActiGraph GT3X+ accelerometers worn on the non-dominant wrist (X. Wu et al. 2019). Activity levels were categorized into sedentary, light, moderate, and vigorous using the validated Chandler’s Vector Magnitude cutoffs (Chandler et al. 2016). Moderate-to–vigorous physical activity was calculated by summing the total minutes spent in activities classified as moderate or vigorous intensity. Pubertal status was evaluated by trained physicians during the in-person visit (Chavarro et al. 2017). Testicular volumes were measured in boys using orchidometers (ranged 1–25 ml). Later pubertal stage was classified as having experienced menarche for girls or testicular volume ≥15 mL for boys. Alcohol consumption status was determined by the question: “Have you ever consumed any alcoholic drink or substance?” with answers being yes, no, or don’t know. The time of testing was recorded at the approximate time of blood collection and used as a continuous variable.

In addition, batch effects and cell-type proportions were included as covariates in our analysis. Five batch effects derived from principal component analysis representing DNA methylation data across all probes, were considered due to their substantial contribution to the variance in the DNA methylation data. Ten estimates of cell-type composition, including proportions of monocytes, granulocytes, natural killer cells, CD8 T cells, CD4 T cells, B cells, memory and effector T cells, plasma-blasts, naïve CD8 T cells, and naïve CD4 T cells, from blood leukocyte samples were evaluated for potential inclusion in our analysis.

Statistical Analysis

Spearman’s correlation was used to examine the correlations between dietary patterns, methyl-donor nutrients, and covariates of interest. Bivariate analyses were conducted to examine differences between sex, using the Chi-square test for categorical variables and simple linear regression for continuous variables, with statistical significance set at p < 0.05. Linear regression analyses were conducted to evaluate how dietary patterns and methyl-donor nutrients were associated with DNA methylation of circadian gene CpG sites. Separate models were run for each dietary pattern/nutrient and CpG site. The final models were adjusted for age, sex, ever smoking, maternal education, five batch effects from the EPIC data, and five cell-type proportions (% monocytes, granulocytes, natural killer cells, CD8 T cells, and CD4 T cells). Additionally, linear regression analyses were also performed for food groups with factor loading of >0.25 or <−0.25 within the significantly associated dietary patterns (Jansen, arcovitch, et al. 2020). To control for the increased likelihood of false positives when testing multiple hypotheses, Benjamini–Hochberg corrections were applied using the R package limma (Ritchie et al. 2015).

Following CpG site-specific analyses, the Aggregated Cauchy Association Test (ACAT) was used to combine unadjusted p-values for all CpG sites within a gene. This test accounted for the dependencies between p-values, regardless of the magnitude and direction of the effect sizes, to assess whether DNA methylation patterns throughout a gene were associated with dietary patterns and methyl-donor nutrients (Liu and Xie 2020; Liu et al. 2019; Zhang et al. 2021). Benjamini-Hochberg adjusted q-values were calculated for these gene-level analyses.

Sex-stratified analyses were conducted for both individual CpG sites and the gene-based combination tests. Sensitivity analysis was conducted by additionally adjusting the model to include all cell types, which added memory and effector T cells, plasma blasts, naïve CD8 T cells, and naïve CD4 T cells to the final model. Furthermore, separate sensitivity analyses were performed by individually adding each covariate – SES, sedentary behavior, physical activity, pubertal status, and alcohol consumption – to the model. Results were deemed statistically significant with Benjamini-Hochberg adjusted p-values (q-values) ≤0.20, indicating that up to 20% of significant findings could be false positives. This threshold represents a moderately stringent approach to controlling the false discovery rate (FDR). Additional analyses were conducted to examine the relationship between dietary intake and circadian gene expression. Spearman’s rank correlation was conducted to examine the correlation between the DNA methylation of significant CpGs and relative abundance of the respective annotated circadian gene, with statistical significance set at p < 0.05. Linear regression was performed to explore the relationship between each dietary intake and circadian gene expression using the DESeq2 package in R, which fitted a generalized linear model to the RNA-seq count data assuming a negative binomial distribution (Love et al. 2014). These analyses included the same covariates as the main analysis while additionally adjusting for the time of test to control for potential temporal confounding. Statistically significant was set at a Bonferroni-adjusted p-value threshold of ≤ 0.20. Analyses were performed using R (version 4.3.2, R Foundation for Statistical Computing, Vienna, Austria).

Results

Mean (SD) age of the analytic sample was 14.5 (2.12) y, and 47% were male (Table 1). Male adolescents had a significantly higher energy intake compared to females. Although not statistically significant, male adolescents had higher energy-adjusted dietary methionine, vitamin B6, and vitamin B12 intakes compared to female adolescents. The Plant-based & lean proteins and the Breakfast patterns were positively correlated, while the Meat & starchy foods pattern was negatively correlated, with dietary intake of methyl-donor nutrients. On average, fasted blood samples were collected at 9:21 AM (ranged 7:33 AM to 12:31PM), approximately 1.25 hours after participants’ typical weekday wake time (average 8:05AM).

Table 1.

Participant characteristics.

Characteristics+ All (n = 526) Male (n = 247) Female (n = 279) p–value
Age (y) 14.5 (2.12) 14.5 (2.05) 14.49 (2.18) 0.946
Household socioeconomic status 0.272
 Upper class 34 (6.5) 20 (8.1) 14 (5.02)
 Upper middle or middle class 249 (47.3) 121 (48.99) 128 (45.88)
 Lower middle or low class 189 (35.9) 85 (34.41) 104 (37.28)
 Lowest class 54 (10.3) 21 (8.5) 33 (11.83)
Ever smoking status 0.591
 Yes 132 (25.1) 67 (27.13) 65 (23.3)
 No 390 (74.1) 178 (72.06) 212 (75.99)
 Missing 4 (0.8) 2 (0.81) 2 (0.72)
Maternal education 0.203
 None or elementary 44 (8.4) 14 (5.67) 30 (10.75)
 Middle school 177 (33.7) 82 (33.2) 95 (34.05)
 Technical or high school 205 (39) 102 (41.3) 103 (36.92)
 College or higher 99 (18.8) 48 (19.43) 51 (18.28)
 Missing 1 (0.2) 1 (0.4) 0 (0)
Energy intake (kcal/day) 2272.77 (874.31) 2523.29 (886.76) 2050.99 (820.77) <0.001 *
Dietary patterns
 Plant-based & lean protein 1.79 (1.01) 1.83 (1.1) 1.75 (0.91) 0.408
 Meat & starchy 2.09 (1.01) 2.14 (0.98) 2.05 (1.03) 0.288
 Breakfast 2.71 (0.99) 2.75 (0.94) 2.67 (1.03) 0.359
Methyl-donor nutrients
 Folate intake (mcg/day) 2.71 (0.99) 2.75 (0.94) 2.67 (1.03) 0.128
 Methionine intake (g/day) 0 (0) 0 (0) 0 (0) 0.337
 Riboflavin intake (mg/day) 247.35 (66.98) 242.63 (61.6) 251.53 (71.26) 0.267
 Vitamin B6 intake (mg/day) 1.28 (0.4) 1.3 (0.46) 1.26 (0.34) 0.592
 Vitamin B12 intake (mg/day) 1.33 (0.39) 1.31 (0.39) 1.35 (0.39) 0.547
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SD = Standard deviation; MVPA = Moderate and vigorous physical activity; n = 491 with physical activity variables; n = 508 with sleep variables; Chi-square test (categorical variables) and simple linear regression (continuous variables) were used to compare participant characteristics between sex.

+

Participant characteristics expressed as mean (SD) or n (%).

*

Statistically significance at p < 0.05.

We examined the associations between dietary patterns and intake of methyl-donor nutrients with 707 CpG sites within 18 circadian and circadian-related genes, adjusting for age, sex, ever smoking, maternal education, energy intake, five batch effects, and five cell-type proportions (% monocytes, granulocytes, natural killer cells, CD8 T cells, and CD4 T cells) (Table 2). The Meat & starchy foods pattern was positively associated with DNA methylation at cg19170589 [β = 5.34 × 10−3, standard error (SE) = 1.44 × 10−3, q = 0.159] within the island region of RORA, and was negatively associated with DNA methylation at cg15048607 (β = −6.46 × 10−3, SE = 1.83 × 10−3, q = 0.159) annotated to CLOCK. After removing a potential outlier, these associations became non-significant. Riboflavin and vitamin B6 intakes were both negatively associated with DNA methylation at cg06337557 (β = −15.01 × 10−3, SE = 4.17 × 10−3, q = 0.162, and β = −13.07 × 10−3, SE = 3.68 × 10−3, q = 0.180, respectively), a CpG site annotated to the north shore region of MTNR1B. Additionally, riboflavin intake was also negatively associated with DNA methylation at cg02076826 (β = −6.53 × 10−3, SE = 1.85 × 10−3, q = 0.162) within RORA, and vitamin B6 intake was positively associated with DNA methylation at cg09615953 (β = 3.73 × 10−3, SE = 1.07 × 10−3, q = 0.180) within the north shore region of PER3. Sex-stratified analysis showed that in males, the Breakfast pattern was negatively associated with DNA methylation at cg13146553 (β = −5.01 × 10−3, SE = 1.34 × 10−3, q = 0.158) annotated to RORA, and riboflavin intake was positively associated with DNA methylation at cg06487986 (β = 5.22 × 10−3, SE = 1.32 × 10−3, q = 0.076), a CpG site within the island region of PER3 (Table 3). Figure S1 shows the methylation profiles of circadian genes annotated to these significant CpGs. No significant associations between dietary patterns and intake of methyl-donor nutrients with 707 CpG sites within 18 circadian and circadian-related genes were found in female adolescents. Overall, no significant correlation was observed between DNA methylation of the significant CpGs with the RNA-seq expression data of the annotated circadian genes (all p > 0.05). Two CpG sites, cg15048607 and cg02076826, were close to being significantly correlated with CLOCK (p = 0.141) and RORA (p = 0.129), respectively, although these associations did not reach statistical significance. At an FDR threshold of 0.20, significant negative associations were observed in male adolescents when looking at the relationship between dietary intake and circadian gene RNA-seq (Supplemental Table S1). Specifically, a higher Breakfast pattern was associated with a 18.89% (SE = 0.103, Bonferroni-adjusted p-value = 0.078) decrease in TIMELESS expression and a higher vitamin B12 intake was associated with an 8.84% (SE = 0.050, Bonferroni-adjusted p-value = 0.052) decrease in CRY1 expression.

Table 2.

Associations between dietary patterns and intake of methyl-donor nutrients and DNA methylation of circadian genes, all participants.

Probe Chr Position Relation to island Strand UCSC gene category Gene Beta SE p–value q–value
Meat & starchy foods
 cg19170589 chr15 60883569 Island + Body RORA 5.34 × 10−3 1.44 × 10−3 2.33 × 104 0.159
 cg15048607 chr4 56310021 OpenSea + Body CLOCK −6.46 × 10−3 1.83 × 10−3 4.50 × 104 0.159
Riboflavin
 cg06337557 chr11 92702373 N_Shore − TSS1500 MTNR1B −1.50 × 10−2 4.17 × 10−3 3.41 × 104 0.162
 cg02076826 chr15 61477299 OpenSea − Body RORA −6.53 × 10−3 1.85 × 10−3 4.58 × 104 0.162
Vitamin B6
 cg09615953 chr1 7886633 N_Shore + Body PER3 3.73 × 10−3 1.07 × 10−3 5.09 × 104 0.180
 cg06337557 chr11 92702373 N_Shore − TSS1500 MTNR1B −1.31 × 10−2 3.68 × 10−3 4.03 × 104 0.180
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Chr, chromosome; SE, standard error.

Table only showed significantly associated CpG sites at q ≤ 0.20.

Models adjusted for age, sex, ever smoking, maternal education, batch effects, % granulocytes, monocytes, natural killer cells, CD8 T cells, and CD4 T cells.

Table 3.

Associations between dietary patterns and intake of methyl-donor nutrients and DNA methylation of circadian genes, males only.

Probe Chr Position Relation to island Strand UCSC gene category Gene Beta SE p–value q–value
Breakfast
 cg13146553 chr15 61343563 OpenSea + Body RORA −5.01 × 10−3 1.34 × 10−3 2.23 × 10–4 0.158
Riboflavin
 cg06487986 chr1 7843564 Island − TSS1500 PER3 5.22 × 10−3 1.32 × 10−3 1.07 × 10–4 0.076
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Chr, chromosome; SE, standard error.

Table only showed significantly associated CpG sites at q ≤ 0.20. No significantly associated CpG sites in female.

Models adjusted for age, ever smoking, maternal education, batch effects, % granulocytes, monocytes, natural killer cells, CD8 T cells, and CD4 T cells.

The ACAT analysis (Table 4) identified significant associations (direction of association not provided with this test) between dietary intake and two circadian genes (PER3, MTNR1A, and MTNR1B). Specifically, dietary intake of riboflavin (p = 0.006, q = 0.111) and vitamin B6 (p = 0.007, q = 0.133) were significantly associated with MTNR1B, while vitamin B6 was also associated with PER3 (p = 0.015, q = 0.137) and MTNR1A (p = 0.027, q = 0.162). When stratified by sex, only riboflavin intake was significantly associated with PER3 (p = 0.004, q = 0.066) in male adolescents, while no significant associations were seen among female adolescents.

Table 4.

Significantly associated circadian genes with dietary patterns and intake of methyl-donor nutrients identified via the acat, all participants and stratified by sex.

PER3
MTNR1A
MTNR1B
p-value q-value p-value q-value p-value q-value
Riboflavin
All 0.077 0.261 0.530 0.596 0.006 0.111 *
Male 0.004 0.066 * 0.866 0.935 0.905 0.935
Female 0.880 0.943 0.242 0.484 0.012 0.209
Vitamin B6
All 0.015 0.137 * 0.027 0.162 * 0.007 0.133 *
Male 0.797 0.989 0.865 0.989 0.064 0.961
Female 0.062 0.559 0.175 0.758 0.957 0.957
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*

False discovery rate adjustment using Benjamini-Hochberg method significant at q ≤ 0.20.

Model adjusted for age, sex, ever smoking status, maternal education, batch effects, % granulocytes, monocytes, natural killer cells, CD8 T cells, CD4 T cells.

When we examined food groups with factor loadings >0.25 and <−0.25 defined in the Meat & starchy foods pattern, significant associations were observed between the chips, sugar sweetened beverages, processed meat, and soups with DNA methylation of circadian gene CpG sites, with mixed directions of association (Supplemental Table S2). Interestingly, the positive association previously observed between the Meat & starchy foods pattern and DNA methylation at cg19170589 were also evident with intake of chips (β = 4.27 × 10−2, SE = 8.52 × 10−3, q = 0.001). In contrast, the high-fat dairy, sweets and desserts, potatoes and fried plantains, Mexican foods, and the refined grains food groups did not show significant associations. In the sex-specific analysis, significant associations were found with the chips, processed meat, potatoes and fried plantains, refined grains, and corn tortillas food groups among males. For example, intake of potatoes and fried plantains was negatively associated with DNA methylation at cg15048607 (β = −6.93 × 10−2, SE = 1.96 × 10−2, q = 0.165) in male adolescents. Conversely, no significant associations were seen with sugar-sweetened beverages, high-fat dairy, sweets and desserts, Mexican foods, legumes, and pork food groups. Furthermore, food groups with factor loadings >0.25 and <−0.25 in the Breakfast pattern among male adolescents were examined. Significant associations were found between the refined grains, milk, and sweetened milk food groups with DNA methylation at circadian gene CpG sites, with the associations differing in direction. Specifically, sweetened milk intake was negatively associated with DNA methylation at cg09659319 (β = −4.19 × 10−3, SE = 1.16 × 10−3, q = 0.128), the same CpG site that was significantly associated with the Breakfast pattern, among male adolescents. No significant associations were observed with the food groups for mayonnaise or margarine and for eggs. The ACAT results (Supplemental Table S3) identified six genes (RORA, RORC, CRY1, PER3, TIMELESS, and NPAS4) to be associated with food groups in Meat & starchy foods pattern, specifically in the chips, sugar sweetened beverages, and processed meat food groups. In males, two genes (CRY1 and PER1) were identified to be associated with the sweetened milk food group. No other food groups in the Breakfast pattern showed statistical significance.

Sensitivity analysis revealed consistent associations in the model adjusted for pubertal status. However, when additionally adjusting for moderate-to–vigorous physical activity and sedentary behavior, the associations with riboflavin intake and sex-stratified associations lost statistical significance. Adjusting for SES instead of maternal education also nullified the associations with the Meat & starchy foods pattern, riboflavin intake, and vitamin B6 intake. When the models were adjusted for all cell types (additionally adjusting for memory and effector T cells, plasma-blasts, naïve CD8 T cells, and naïve CD4 T cells), an additional significant association emerged between riboflavin intake and DNA methylation at cg22290067, another CpG site within RORA, while the associations in vitamin B6 became non-statistically significant. The direction and magnitude of the beta estimates remained consistent across the sensitivity analysis models.

Discussion

We observed several notable associations between dietary intake, particularly methyl-donor nutrients, and DNA methylation of circadian and circadian-related genes within this cohort of 526 Mexican adolescents. Riboflavin intake showed an inverse association with a CpG site within RORA and vitamin B6 intake showed a positive association with a CpG site within PER3. Additionally, both riboflavin and vitamin B6 intakes were inversely associated with the same CpG site within MTNR1B. In contrast to the pilot study and previous research, which showed associations between plant-based dietary patterns and circadian gene methylation, our study did not detect significant links with the Plant-based & lean proteins pattern. A second insight of the study was that more pronounced associations were found among males. Specifically, the Breakfast pattern was associated with DNA methylation at CpG sites within RORA, and Riboflavin intake was positively associated with DNA methylation at a CpG site within PER3 among males only. Gene-based combination tests supported the primary findings and highlighted the most robust associations, showing significant associations between riboflavin and vitamin B6 intake with MTNR1B genes. Moreover, riboflavin intake was associated with PER3 among male adolescents and no significant associations were observed in female adolescents. Of note, post hoc analysis revealed that DNA methylation of the studied circadian genes was not related to mid-morning gene expression of the corresponding genes.

Prior research primarily explored the impact of dietary intake, specifically macronutrients, on DNA methylation sites across the genome or global DNA methylation patterns rather than a more specific, hypothesis-driven approach regarding circadian rhythm regulation. Several studies in adults have demonstrated that low intake of fruits and vegetables, low adherence to Mediterranean diets, and folate deficiency were associated with global hypomethylation, as measured by LINE-1 DNA methylation (Agodi et al. 2015; Barchitta et al. 2018; Zhang et al. 2012). Blood global hypomethylation has been linked to overall genomic instability and adverse health effects (Besselink et al. 2023; Zhong et al. 2016). One study, which involved 250 adolescents (118 boys and 132 girls) aged 10–18 y, found that selected components of the diet were linked to LINE-1 DNA methylation from blood leukocytes (Y. Wu et al. 2019). Specifically, high-fiber vegetables were positively associated with global DNA methylation levels, whereas lean protein food items were negatively associated. Moreover, a randomized controlled trial implementing a 12-week intervention to increase fruit and vegetable intake and physical activity in adults aged 18–65 y was related to several differentially methylated regions based on epigenome-wide DNA methylation from blood samples (Hibler et al. 2019). Few studies have examined the association of childhood dietary intake with DNA methylation. For instance, a study on young children aged 2–3 y living in urban slum communities in Bangladesh reported that fruit and vegetable intake was associated with lower global DNA methylation in whole blood samples, while energy, protein, and carbohydrate intake exhibited contrasting effects, showing higher global DNA methylation levels in this population (Iqbal et al. 2019). Another study involving overweight or obese adolescents undergoing a 10-week dietary and physical activity intervention for weight loss found that those achieving better weight loss had higher global DNA methylation, based on 97 CpG sites from whole blood samples analyzed with the Infinium Human Methylation 27k BeadChip (Moleres et al. 2013). This study also reported significant differential DNA methylation at several individual CpG sites (Moleres et al. 2013). Similarly, epigenome-wide associations between dietary fat intake and whole blood DNA methylation in Greek preadolescents in the fifth and sixth grade revealed that both the quality and quantity of fat intake were associated with specific CpG sites, although the direction of these associations was mixed (Voisin et al. 2015). Furthermore, a meta-analysis showed positive associations between dietary patterns high in glycemic index/load and blood DNA methylation at numerous CpG sites in 1187 overweight or obese children and adolescents aged 4.5–17 y (Ott et al. 2023). Another study, which collected saliva sample from 113 children aged 6–10 y, reported a significant positive correlation between macronutrient intake and DNA methylation of obesity related genes (Patel et al. 2023).

To date, few studies have reported associations with DNA methylation of circadian genes. The only previous work to show plausible associations between dietary patterns and circadian genes among adolescents is our own pilot work. In the prior study of 142 adolescents, we found that the Breakfast pattern (with eggs, milk & refined grain) had the clearest evidence of relationships with circadian genes, with associations across all 12 genes. Particularly, inverse associations were found between the Breakfast pattern and PER2, CRY2, RORA, and NR1D1 DNA methylation (Jansen et al. 2021). In the current study, we observed an inverse relationship between the Breakfast pattern and DNA methylation of a CpG site on RORA, but only among male adolescents. The discrepancies could be due to the larger sample size in the present study, which allowed us to conduct in-depth sex-stratified analysis.

Our study found an inverse relationship between the Breakfast pattern with gene expression of TIMELESS in males, even though there were no associations with DNA methylation of TIMELESS at individual CpG sites. Hypomethylation at CpG sites is more often linked to increased expression, however, this may not always be the case. The specific region of the gene that is methylated and other layers of epigenetic regulation, including histone modifications and non-coding RNAs, can also influence this relationship. Although we observed differential methylation in circadian genes, the absence of correlation between DNA methylation and gene expression suggests that methylation alone does not explain variation in circadian gene expression in peripheral blood. It is more likely that other regulatory mechanisms contribute to circadian gene expression patterns. Taken together, these findings suggest a potential link between dietary patterns and circadian gene DNA methylation and expression, but given the relaxed statistical threshold, further studies are needed to establish biological relevance and clarify the implications for phenotype or downstream biological processes.

Not only the composition but also the timing of dietary patterns may impact circadian clock regulation and its subsequent effects on glucose and lipid metabolism. Indeed, the timing of food consumption plays a pivotal role in synchronizing the peripheral circadian rhythms (Jakubowicz et al. 2017; Wehrens et al. 2017). Irregular meal timing, particularly frequent breakfast skipping, can disrupt circadian rhythms (Chawla et al. 2021). Discordance between meal timing schedule has been previously associated with disruption in the expression of circadian genes (Jakubowicz et al. 2021). The observed associations in our study with the Breakfast pattern could potentially be explained by the timing of that meal. This study did not examine meal timing or breakfast skipping in the analysis, which limited our understanding of their specific impacts on DNA methylation.

Composition of meals may also impact peripheral circadian rhythms. Changes in dietary patterns, particularly levels of macronutrient composition, can affect the amplitude of circadian oscillations in peripheral organs, potentially uncoupling these rhythms from the central circadian clock. For instance, a previous study showed that switching from a high-carbohydrate and low-fat to a low-carbohydrate and high-fat diet can modulate circadian gene expression in peripheral tissues without shifting the timing of peak expression or changes in cortisol levels, suggesting that while diet plays an important role in regulating circadian rhythms in peripheral tissues, it does not exert the same control over centrally driven processes, such as the hormonal rhythms regulated by the suprachiasmatic nucleus in the brain (Pivovarova et al. 2015). While our study cannot directly assess tissue-specific DNA methylation of circadian genes due to its evaluation of blood samples, it still offers valuable insight into the potential broader, systemic influence of dietary intake. Dietary patterns that were high-fat and high-sugar intake have been found to alter clock gene expression (Oosterman et al. 2015), in line with the associations observed in our study between the Meat & starchy foods pattern and DNA methylation at CpG sites within RORA and CLOCK genes. Consuming a Meat & starchy foods pattern, which included intake of foods high in fat such as processed meat, beef, protein, butter, and spread, was associated with higher social jetlag among adolescents (Jansen, Baylin, et al. 2020). The associations observed in the present study with the Meat & starchy foods pattern appeared to be primarily driven by the intake of chips and the potato and fried plantains food groups. Other components of this pattern, specifically meat and dietary fat intakes have been linked to changes in sleep duration and quality across the lifespan (Grandner et al. 2010; Lana et al. 2019; Martinez et al. 2016; Tatone-Tokuda et al. 2012). Fatty acid composition in the diet has also been suggested to influence blood DNA methylation pattern of circadian genes collected from white blood cells (Milagro et al. 2012). Monounsaturated fatty acid and olive oil intake was negatively associated, while polyunsaturated fatty acid intake was positively associated with methylation of CLOCK (Milagro et al. 2012). Another study showed that dietary intake of monounsaturated and saturated fatty acids may modulate the impact of genetic variants of CLOCK, based on DNA isolated from blood samples, on insulin resistance and obesity (Garaulet et al. 2009).

Our study observed no association between the Plant-based & lean proteins pattern and DNA methylation of circadian genes, in contrast to our pilot study involving 142 adolescents, which identified 12 associations across 7 circadian genes (Jansen et al. 2021). The present finding also differs from prior studies that demonstrated associations between a healthy dietary pattern with differential DNA methylation in circadian genes in adults (Milagro et al. 2012; Ramos-Lopez et al. 2018; Samblas et al. 2016). A study on adult women following at Mediterranean-type diet showed an association with DNA methylation of CpG sites on CLOCK collected from white blood cells (Milagro et al. 2012). Similarly, in a weight-loss intervention combining a Mediterranean diet and physical activity regimen, a positive association was found between energy and carbohydrate intakes with whole blood DNA methylation of CpG sites in BMAL1, a core circadian gene involved in the regulation of other circadian genes (Samblas et al. 2016). Previous research has implicated specific dietary components and macronutrient distribution in the regulation of circadian genes (Ribas-Latre and Eckel-Mahan 2016; Sato and Sassone-Corsi 2022), influencing the synchronization between the internal clock and biological functions (Garaulet and Gómez-Abellán 2014). Polyamines, commonly found in fruits, vegetables, cheese, and meat products, was shown to modulate circadian gene expression in mice (Zwighaft et al. 2015). Furthermore, dietary polyamines, polyphenols, flavonoid compounds, and cruciferous vegetables rich in Mediterranean diet may reverse adverse epigenetic status (Huang et al. 2019; Soda 2022). It is possible that the lack of significant associations found in our study could be due to these beneficial dietary components not being adequately represented in the “healthy” Plant-based & lean proteins pattern within this population of adolescents.

Dietary methyl donors may influence DNA methylation across the genome. Previous investigations on the impact of methyl-donor nutrients on DNA methylation revealed different directions of association (Montrose et al. 2017; Patel et al. 2023; Perng et al. 2012; Taylor et al. 2018). For instance, a positive association was found between dietary folate and LINE-1 DNA methylation from buccal cell samples, while no significant associations were observed for methionine, riboflavin, and vitamin B12 intakes (Montrose et al. 2017). Additionally, vitamin B6 intake was inversely related to methylation at CpG promoter site IFNγ−186 in children with asthma (Montrose et al. 2017). One study reported several significant positive correlations between intake of methyl donor nutrients, including folate, riboflavin, vitamin B12, and vitamin B6, with DNA methylation of obesity-related genes, determined using a candidate gene approach, from saliva samples of 113 children aged 6–10 y (Patel et al. 2023). Additionally, serum levels of metabolites and vitamins B involved in one-carbon metabolism was associated with blood lymphocyte DNA methylation of multiple CpG sites in five targeted genes related to a variety of human malignancies, identified based on the candidate gene approach among adults aged 35–70 y (Vineis et al. 2011). Specifically, a positive association was seen with folate intake among all participants and never smokers, while a negative association was observed between methionine, vitamin B6 and vitamin B12 intakes with DNA methylation at several candidate genes among former and current smokers (Vineis et al. 2011). One randomized controlled trial of 31 older adults in the United Kingdom showed that folic acid supplementation for 10 weeks significantly increased blood leukocyte and rectal mucosal DNA methylation levels by 31% and 25%, respectively, compared to no change in the placebo group (Pufulete et al. 2005). However, some studies did not find significant associations between methyl-donor nutrients and global DNA methylation levels (Perng et al. 2012; Taylor et al. 2018). Among 568 children aged 5–12 y in Colombia, no significant association was observed between erythrocyte folate and plasma vitamin B12 and blood LINE-1 DNA methylation (Perng et al. 2012). Intake of folate, methionine, and vitamins B2, B6, and B12 during the first 3 years of life were not significantly associated with DNA methylation at fourth year collected from buccal cell samples (Taylor et al. 2018). Additionally, in an Australian intervention study on adults aged 18–32 y, folate and vitamin B12 supplementation over a course of 12 weeks did not significantly change lymphocyte DNA methylation level collected from whole blood samples (Fenech et al. 1998).

Methyl-donor nutrients may modulate enzymatic activities and the availability of cofactors needed in one-carbon metabolism (Anderson et al. 2012; Mason 2003), which can regulate DNA methylation of circadian-related genes (Sato and Sassone-Corsi 2022). Although our cross-sectional design limited the ability to infer causal relationships, the pathway from dietary intake to DNA methylation of circadian genes presents a biologically plausible directionality. In this study, we specifically observed that the methyl-donors riboflavin and vitamin B6 were most strongly related to circadian gene DNA methylation. Contrary to our initial expectation of a positive relationship, our results revealed inverse associations between riboflavin intake and two DNA methylation of CpG sites, cg02076826 and cg06337557, as well as between vitamin B6 intakes with DNA methylation of a CpG site, cg06337557. Given that methyl-donor nutrients are required in the methylation processes via one carbon metabolism (Anderson et al. 2012; Mason 2003), we originally hypothesized that increased intake of methyl-donor nutrients would be associated with higher DNA methylation of circadian genes. In contrast, we did observe some associations in the expected direction, showing a positive association between vitamin B6 intake and methylation at cg0961595, and between riboflavin intake in males and methylation at cg0648798, both located on the PER3 gene. These mixed results highlight the complexity of the role methyl-donor nutrients may play in circadian gene regulation, which further marked the need for future research to explore this relationship.

Vitamin B6 and riboflavin were notably both related to a CpG site located on MTNR1B. Despite the small effect sizes at individual CpG sites, these findings could still reflect subtle yet meaningful molecular changes with possible implications for understanding circadian regulation, though the functional relevance remains uncertain. Vitamin B6 serves as a crucial coenzyme in various metabolic pathways, including the biosynthesis of melatonin, a hormone involved in the regulation of circadian rhythms and sleep patterns (Muñoz-Hoyos et al. 1996). Research showed that lower vitamin B6 intake was associated with poor sleep quality and abnormal sleep duration (Ge et al. 2022; Huang et al. 2013). Vitamin B6 supplementation, combined with melatonin and medicinal plants, was found to improve mild and moderate insomnia (Lemoine et al. 2019). While our observed finding cannot confirm a direct role, they raise the possibility that vitamin B6 intake may also influence how melatonin interacted with its receptors to regulate sleep and circadian rhythm beyond its enzymatic role in melatonin production. Riboflavin, another important nutrient identified in our analysis, plays a pivotal role in vitamin B6 activation and tryptophan metabolism (Mahabadi et al. 2023). Riboflavin may be crucial for melatonin synthesis as it supports the activation of vitamin B6, necessary for converting tryptophan into melatonin; alternatively, riboflavin is also required for another tryptophan metabolism pathway that may potentially divert tryptophan away from melatonin synthesis. Although the connection between riboflavin intake and the regulation of circadian genes requires further elucidation, its involvement in cellular processes may indirectly affect epigenetic regulation (Chamberlain et al. 2018; Dominguez-Salas et al. 2014).

In our study, the associations between dietary intake and DNA methylation in circadian genes were predominantly observed in male adolescents, aligning with findings from the exploratory sex-stratified analysis in the pilot study, which suggested potential sex-specific differential methylation patterns (Jansen et al. 2021). Research has indicated that the delay in circadian phase occurs during adolescence in a sex-specific manner (Randler et al. 2017). Specifically, this shift toward a later preferred sleep timing begins about a year earlier in girls than in boys, which coincides with their earlier onset of puberty (Roenneberg et al. 2004). Additionally, this study found that girls reached their peak delay in chronotype sooner than boys and the extent of this delay varied between sexes, with boys typically showing a later maximum (Roenneberg et al. 2004). In general, boys have a later chronotype and sleep timing than girls (Adan et al. 2012), and this difference continues into late adolescence and adulthood (Randler et al. 2017). While changes in hormone regulation during adolescence likely influence the circadian rhythms differently by sex (Bailey and Silver 2014; Zhang et al. 2022), eating behaviors and dietary patterns may also play a role (Abdella et al. 2019; Grosjean et al. 2023). Additionally, in a study examining sex differences in blood DNA methylation, excluding CpG sites on sex chromosomes, found that boys had lower global DNA methylation compared to girls, and this difference increased with age until late adolescence (Han et al. 2019). The slower onset of puberty and different baseline level of DNA methylation in boys may have allowed for a more detectable relationship between diet and circadian gene regulation.

This is one of few studies to examine the relationship between dietary intake and DNA methylation of circadian genes in adolescents. Building upon a pilot investigation, this study expanded on both the sample size, dietary intake of methyl-donor nutrients, and the scope of circadian genes analyzed (Jansen et al. 2021). The use of gene-level associations provided a comprehensive view of circadian gene methylation patterns and increased sensitivity to detect subtle shifts in methylation. The ACAT offered an efficient approach for conducting gene-level analysis while accounting for correlated p-values. However, as a combination method that does not rely on permutation testing to assess the minimum p-value under the null hypothesis, the ACAT does not provide an effect size for the relationship. Additional predictive models are required to determine the magnitude and direction of estimates for the associations at the gene level.

The study findings must be considered in light of several important limitations. One limitation of our study is that we applied a less stringent FDR threshold, which increases the potential risk of false positives compared to the more commonly used cutoff. While a relaxed threshold was chosen to increase the sensitivity of detecting potential associations, this approach may result in a higher likelihood of type I errors. As such, the findings may be limited in generalizability and may warrant further validation in larger or more diverse populations. Another limitation of this study is that it primarily focused on the linear relationship between dietary intake and DNA methylation of circadian genes, which may not fully capture non-linear relationships and potential interactions between variables. Additionally, the cross-sectional design of our analysis limited our ability to infer causal relationships or observe changes over time, with the possibility of reverse causality potentially biasing interpretations. Future longitudinal studies would offer a more comprehensive understanding of the dynamic interplay between dietary intake and DNA methylation of circadian genes. Samples were collected at a single mid-morning time point, which precludes the assessment of potential diurnal variation in DNA methylation. However, this limitation is likely more relevant for the gene expression analysis, since DNA methylation is generally considered stable over short-time periods in peripheral blood (Flanagan et al. 2015). Future studies with repeated sampling across the circadian cycle are warranted to investigate possible time-of–day effects. Moreover, the use of whole blood samples may not fully reflect organ-specific alterations within the complex peripheral circadian systems. While DNA methylation of blood samples can capture systemic changes, it does not provide insight into epigenetic modifications occurring in tissues directly involved in metabolic processes. Thus, our findings do not reflect the specific effects of dietary patterns on circadian rhythms of these tissues. While previous validated FFQs were used to gather dietary information, the self-reported nature of the data may still be subject to measurement errors and inaccuracies. We assessed several dietary patterns, methyl-donor nutrients, and food groups in this study, but these likely only capture a portion of the broader range of dietary influences on DNA methylation of circadian genes in this population. For example, our analysis does not account for the individual macronutrients and bioactive compounds found in foods such as dietary polyamines, polyphenols, flavonoid and caffeine. Another important variable to examine includes meal timing. The use of principal component analysis to derive dietary patterns may also oversimplify the complex relationships between specific dietary components and epigenetic modifications. Our study population consisted of adolescents, who are undergoing a developmental stage characterized by significant hormonal and circadian changes. These physiological transitions may influence DNA methylation and gene expression profiles, which likely contributed to the biological variability in our results and may hinder the generalizability of our findings to other age groups.

Conclusion

This study provided valuable insight into the potential relationship between dietary intake and regulation of circadian and circadian-related genes through DNA methylation among adolescents, revealing notable sex-specific differences. We identified several methyl-donor nutrients (riboflavin and vitamin B6 intakes) that may play a role in modulating core clocks function as well as melatonin synthesis and signaling pathways. Our findings raise the possibility that both timing and composition of dietary intake could impact epigenetic modification of circadian genes, although additional study of the timing of eating is needed. Future research should explore the mediation pathways and focus on capturing the temporal relationships between dietary intake, DNA methylation of circadian genes, and cardiometabolic risk factors using a longitudinal study design.

Supplementary Material

Supp 1

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07420528.2025.2532796.

Acknowledgments

We gratefully acknowledge the mothers and children who participated in the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) and American British Cowdray Medical Center (ABC) for providing facilities for this research.

Funding

National Heart, Lung, and Blood Institute Dr. Jansen was supported by the NIH/NHLBI grant [T32HL110952] (Sleep and Genetics T32). The ELEMENT study is funded through NIEHS [P01ES022844], with support from the National Institute of Public Health in Mexico and the ABC Hospital.

Footnotes

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The datasets used for this study are not publicly available for the protection of human subjects, however, interested parties may submit a reasonable data request to the ELEMENT PI, Peterson (karenep@umich.edu) for review by the ELEMENT committee.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp 1
NIHMS2102323-supplement-Supp_1.docx (553KB, docx)

Data Availability Statement

The datasets used for this study are not publicly available for the protection of human subjects, however, interested parties may submit a reasonable data request to the ELEMENT PI, Peterson (karenep@umich.edu) for review by the ELEMENT committee.

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