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. 2020 Oct 4;16(8):894–907. doi: 10.1080/15592294.2020.1827719

Adolescent sleep timing and dietary patterns in relation to DNA methylation of core circadian genes: a pilot study of Mexican youth

Erica C Jansen a,b,✉, Dana Dolinoy a,c, Karen E Peterson a, Louise M O’Brien b, Ronald D Chervin b, Alejandra Cantoral d, Martha María Tellez-Rojo e, Maritsa Solano-Gonzalez e, Jaclyn Goodrich c
PMCID: PMC8331002  PMID: 33016191

ABSTRACT

Mistimed sleep/wake and eating patterns put shift workers at increased risk for cardiometabolic disease, and epigenetic modification of circadian genes has been proposed as a mechanism. Although not as extreme as shift workers, adolescents often have delayed sleep timing and irregular eating patterns. The aim was to assess whether sleep midpoints – median of bed and wake time – and dietary patterns in adolescents were associated with DNA methylation of circadian genes. The study population included 142 Mexican youth (average age of 14.0 (SD = 2.0) years, 49% male). Average sleep midpoint over weekdays was estimated with actigraphy. Diet was assessed with a semi-quantitative food frequency questionnaire, and three dietary patterns were derived from principal component analysis, a Plant-based & lean proteins pattern, a Meat & starchy pattern, and an Eggs, milk & refined grain pattern. DNA methylation was quantified in blood leukocytes with the Infinium MethylationEPIC BeadChip, and data from 548 CpG sites within 12 circadian genes were examined. Linear regression analyses, adjusted for sex, age, and % monocytes, showed that later sleep timing was associated with higher DNA methylation of several circadian genes, notably with RORB, PER1, CRY2, and NR1D1. Each of the dietary patterns examined was also related to circadian gene DNA methylation, but the Eggs, milk & refined grain pattern (‘breakfast’ pattern) had the clearest evidence of relationships with circadian genes, with inverse associations (lower DNA methylation) across all 12 genes. Findings suggest that timing-related sleep and eating behaviours among adolescents could result in epigenetic modification of clock genes.

KEYWORDS: Clock genes, actigraphy, circadian misalignment, epigenetics

Introduction

Circadian disruption refers to a misalignment in timing of daily activities, including eating and sleeping, with the internal circadian rhythm. Shift workers display the most extreme example of circadian disruption, but there are a range of circadian disruptions present in non-shift work populations. Adolescents are one population who may be particularly susceptible to circadian disruption, since there are multiple factors that delay bedtimes (e.g., social media and screen use [1]) and increase variability in sleep timing [2] (e.g., sleeping in late on weekends, napping during the day). Eating patterns may also contribute to misalignment of daily activities among adolescents, as the pubertal transition often coincides with changes in eating behaviours, particularly with alterations in timing (e.g., late-night eating [3]) and stability [4,5] (e.g., irregular snacking rather than regular meals).

Research among shift workers consistently shows that sleeping and eating at non-optimal times is associated with cardiometabolic disease risk [6,7]. Moreover, mounting evidence suggests that circadian disruption observed within non-shift work populations, including adolescents, may also increase risk for cardiometabolic disease. For example, sleep timing (a typical proxy for circadian disruption), independent of sleep duration, was associated with higher metabolic syndrome risk among girls in a cohort of adolescents from the US [8]. In addition, a study of adolescents from Morelos, Mexico showed that both later (after 11 PM) and earlier (before 9 PM) bedtimes, as compared to those between 9 and 10 PM, were associated with higher incidence of elevated blood pressure over a 14-month period [9].

One proposed mechanism to explain a link between disrupted circadian rhythms and poor cardiometabolic health is through altered expression of circadian genes. Circadian, or clock genes, are responsible for maintaining 24-h rhythmicity of sleep and wake processes, including the diurnal rhythms of metabolism [10]. One pathway by which gene expression can be altered by environmental cues such as sleep and diet is through the epigenetic mechanism of DNA methylation [11,12], i.e., the addition of a methyl group to the 5ʹ-carbon of cytosine in a Cytosine-phosphate-Guanine (CpG) dinucleotide. Recent evidence shows that shift workers in comparison to those on day shifts have differential DNA methylation patterns of certain circadian genes [13,14], which in turn is likely related to differences in gene expression and function [15]. Nonetheless, the degree to which circadian disruption could alter DNA methylation of circadian genes within adolescent populations remains unknown. Adolescence represents an important developmental window where the epigenome may be especially vulnerable to environmental and behavioural influence. Further, uncovering associations between sleep timing and dietary patterns with circadian genes could have important implications for later disease risk, particularly cardiometabolic health, as circadian genes play an important role in metabolism [16]. Within a pilot study of 142 Mexican adolescents, the primary aim was to assess whether weekday sleep midpoint, defined as the median of bed and wake time, was associated with DNA methylation at multiple CpG sites of 12 selected core circadian genes, quantified via the Infinium MethylationEPIC. These genes were selected for study due to their known function in regulating circadian rhythms. A secondary aim was to examine associations between dietary patterns and circadian gene methylation.

Results

Mean (SD) age of the sample was 14.0 (2.0) years, and 49% were male. Mean (SD) weekday midpoint was 3:41 AM (1.3 h). Later sleep midpoint was associated with older age and longer sleep duration (Table 1). Dietary pattern scores were not statistically significantly associated with midpoints, although adolescents with later midpoints tended to have lower intake of the Plant-based and lean proteins pattern and the Eggs, milk & refined grain pattern.

Table 1.

Characteristics of the 142 Mexican adolescents

Sociodemographic and lifestyle characteristics Midpoint <4 AM, mean (SD) Midpoint ≥4 AM, mean (SD) P value
N 87 55  
Age 13.6 (1.9) 14.5 (1.9) 0.006
Sex (%)     0.35
Male 65.2 34.8  
Female 57.5 42.5  
BMI for age z score 0.51 (1.20) 0.57 (1.36) 0.76
Mother’s education (years) 11.0 (2.9) 11.1 (2.9) 0.80
Physical activity, moderate/vigorous minutes/day 84.1 (27.2) 82.4 (32.1) 0.73
Total energy intake (kcals) 2514 (1062) 2279 (873) 0.17
Dietary pattern scores      
Diet pattern 1: Plant-based and lean proteins 0.21 (1.28) 0.07 (1.33) 0.54
Diet pattern 2: Meat & starchy foods 0.06 (1.08) 0.03 (0.87) 0.88
Diet pattern 3: Eggs, milk & refined grain 0.14 (0.72) −0.07 (0.98) 0.14
Weekday sleep duration (min) 497 (59) 542 (59) <0.0001
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Overall, we examined associations with DNA methylation at 548 CpG sites within the 12 selected circadian genes. The statistically significant associations (at P < 0.05) between individual loci and the sleep midpoint and dietary pattern exposures are shown in Table 2.

Table 2.

Associations between sleep and dietary patterns and DNA methylation of circadian genes in Mexican adolescents, P < 0.05

Probe Chromosome Position Gene Relation to island Gene region feature category Beta values1 P value
Sleep Midpoint (median of bed and wake time)
cg15603424 chr11 13300592 BMAL1 Island 5ʹUTR 0.000820 0.00300*
cg11591729 chr4 56411904 CLOCK N_Shore 1stExon 0.000857 0.0318
cg14618246 chr11 45888058 CRY2 OpenSea Body 0.00169 0.0253
cg25904177 chr11 45884565 CRY2 OpenSea Body −0.00178 0.0307
cg25736892 chr11 45888911 CRY2 OpenSea Body −0.00147 0.0450
cg13476336 chr17 38255694 NR1D1 Island Body 0.00112 0.00564
cg06474323 chr17 38257020 NR1D1 Island TSS200 0.000217 0.0185
cg02923856 chr3 23989124 NR1D2 S_Shore 5ʹUTR 0.00383 0.0302
cg10669351 chr3 24007477 NR1D2 OpenSea Body 0.00301 0.0305
cg03301481 chr17 8052075 PER1 N_Shelf Body −0.00291 0.00360*
cg13433366 chr17 8057058 PER1 S_Shore TSS1500 0.00292 0.0263
cg25250717 chr15 61144420 RORA OpenSea Body −0.00207 0.0150
cg23965982 chr15 61055848 RORA OpenSea Body 0.00286 0.0154
cg18136062 chr15 61520931 RORA Island Body 0.000990 0.0156
cg09859112 chr15 61520370 RORA N_Shore Body 0.00170 0.0217
cg07701191 chr15 61520424 RORA Island Body 0.000502 0.0223
cg12061096 chr15 61028241 RORA OpenSea Body 0.00320 0.0260
cg02775377 chr15 61476554 RORA OpenSea Body 0.00232 0.0272
cg22536351 chr15 61044565 RORA OpenSea Body 0.00246 0.0388
cg20525725 chr15 61497556 RORA OpenSea Body −0.00145 0.0488
cg13301933 chr15 61521439 RORA Island 5ʹUTR 0.000819 0.0126
cg26087678 chr15 60884119 RORA Island Body 0.00328 0.0309
cg08700690 chr15 60884630 RORA Island Body 0.000523 0.0455
cg14170313 chr9 77112896 RORB Island 1stExon 0.00109 0.00116*
Diet pattern 1: Plant-based and lean proteins
cg06116839 chr11 13376988 BMAL1 OpenSea Body −0.00125 0.0340
cg19429538 chr4 56303887 CLOCK OpenSea Body −0.00184 0.0400
cg02359589 chr17 8045663 PER1 OpenSea Body −0.00225 0.0495
cg07719617 chr2 239169696 PER2 OpenSea Body −0.00209 0.0102
cg21462970 chr2 239161488 PER2 OpenSea Body −0.00118 0.0148
cg06246651 chr2 239187798 PER2 OpenSea 5ʹUTR −0.00325 0.0390
cg13469320 chr2 239162122 PER2 OpenSea Body −0.000987 0.0474
cg04917262 chr1 7868469 PER3 OpenSea Body −0.00414 0.000601*
cg11641053 chr1 7844184 PER3 Island TSS1500 −0.000426 0.0293
cg13146107 chr15 61122353 RORA OpenSea Body −0.00115 0.0286
cg05912053 chr15 60919520 RORA OpenSea Body −0.00191 0.0265
cg20667664 chr17 38254448 NR1D1 N_Shore Body −0.00323 0.0207
Diet pattern 2: Meat & starchy foods
cg16259330 chr11 13299070 BMAL1 Island TSS1500 0.00109 0.00395*
cg21078679 chr11 13377829 BMAL1 OpenSea Body −0.00216 0.0237
cg03327818 chr11 13370408 BMAL1 OpenSea 5ʹUTR 0.00219 0.0271
cg27298069 chr4 56412099 CLOCK Island TSS200 −0.000441 0.0145
cg16345521 chr12 107486801 CRY1 Island 1stExon 0.000713 0.00535
cg26125082 chr11 45868418 CRY2 N_Shore TSS1500 0.00942 0.0229
cg20855344 chr3 2398090 NR1D2 N_Shore TSS1500 −0.00301 0.0332
cg08506046 chr3 23987101 NR1D2 Island TSS1500 0.00105 0.0369
cg21093368 chr17 8055834 PER1 Island TSS200 −0.00144 0.0459
cg03067929 chr15 61191921 RORA OpenSea Body 0.00607 0.0253
cg26065596 chr15 61448727 RORA OpenSea Body −0.00198 0.0351
cg03461216 chr15 61197849 RORA OpenSea Body −0.00278 0.0380
cg04324336 chr15 60941269 RORA OpenSea Body 0.00218 0.0431
cg19170589 chr15 60883569 RORA Island Body 0.00189 0.000658*
cg14331163 chr9 77111663 RORB N_Shore TSS1500 0.00321 0.0471
Diet pattern 3: Eggs, milk & refined grain
cg22322535 chr11 13299888 BMAL1 Island 5ʹUTR −0.00118 0.0184
cg13250711 chr11 13298675 BMAL1 N_Shore TSS1500 −0.00139 0.0211
cg07032258 chr11 13357037 BMAL1 OpenSea 5ʹUTR −0.00448 0.0328
cg08367205 chr11 13298296 BMAL1 N_Shore TSS1500 −0.00102 0.0356
cg18579796 chr11 13371358 BMAL1 OpenSea 5ʹUTR 0.00285 0.00229*
cg14666553 chr11 13374140 BMAL1 OpenSea 5ʹUTR 0.00198 0.0125
cg16839955 chr11 13298683 BMAL1 N_Shore TSS1500 −0.00119 0.0164
cg21860398 chr11 13379485 BMAL1 OpenSea Body −0.00271 0.0212
cg16340122 chr11 13377690 BMAL1 OpenSea Body 0.00389 0.0447
cg02683371 chr4 56298893 CLOCK OpenSea 3ʹUTR 0.00204 0.0441
cg24000108 chr4 56376331 CLOCK OpenSea 5ʹUTR 0.00373 0.0174
cg12893431 chr4 56315641 CLOCK OpenSea Body 0.00450 0.0196
cg24004414 chr4 56369919 CLOCK OpenSea 5ʹUTR 0.00674 0.0232
cg15048607 chr4 56310021 CLOCK OpenSea Body 0.00526 0.0262
cg19429538 chr4 56303887 CLOCK OpenSea Body 0.00288 0.0394
cg08407670 chr4 56303880 CLOCK OpenSea Body 0.00273 0.0442
cg26520722 chr12 107486229 CRY1 N_Shore Body −0.00240 0.00536
cg08756366 chr12 107444216 CRY1 OpenSea Body 0.00308 0.00749
cg20879606 chr11 45868932 CRY2 Island Body −0.00181 0.00831
cg07524456 chr11 45868501 CRY2 N_Shore TSS1500 −0.00941 0.0321
cg14858951 chr17 38257028 NR1D1 Island TSS200 −0.000435 0.0134
cg01059731 chr17 38255663 NR1D1 Island Body −0.00230 0.0152
cg13476336 chr17 38255694 NR1D1 Island Body −0.00130 0.0205
cg20667664 chr17 38254448 NR1D1 N_Shore Body 0.00471 0.0303
cg04534726 chr3 23987467 NR1D2 Island TSS200 −0.000452 0.00999
cg17680792 chr3 23986459 NR1D2 N_Shore TSS1500 −0.000750 0.0282
cg19399789 chr3 23987441 NR1D2 Island TSS200 −0.000492 0.00869
cg06103301 chr3 23987730 NR1D2 Island 5ʹUTR −0.000306 0.00991
cg10028884 chr17 8054829 PER1 Island 5ʹUTR −0.00111 0.0166
cg03301481 chr17 8052075 PER1 N_Shelf Body 0.00303 0.0299
cg20503576 chr17 8055884 PER1 S_Shore TSS200 −0.00199 0.0440
cg13469320 chr2 239162122 PER2 OpenSea Body 0.00233 0.00234*
cg12308675 chr2 239169537 PER2 OpenSea Body −0.00298 0.00290
cg02976543 chr2 239197607 PER2 Island TSS1500 −0.00616 0.0138
cg06174546 chr2 239171720 PER2 OpenSea Body 0.00152 0.0247
cg00811891 chr1 7904830 PER3 OpenSea 3ʹUTR 0.00204 0.0409
cg11129701 chr1 7862851 PER3 OpenSea 5ʹUTR −0.00203 0.0449
cg22148904 chr1 7862234 PER3 OpenSea 5ʹUTR 0.00392 0.0498
cg27391826 chr15 61395262 RORA OpenSea Body 0.00616 0.00516
cg08574592 chr15 61520873 RORA Island Body −0.00199 0.0112
cg18760360 chr15 60953215 RORA OpenSea Body 0.00409 0.0121
cg02076826 chr15 61477299 RORA OpenSea Body −0.00395 0.0174
cg20822028 chr15 61038077 RORA OpenSea Body −0.00256 0.0182
cg27024408 chr15 61327619 RORA OpenSea Body 0.00285 0.0184
cg18730873 chr15 61462794 RORA OpenSea Body 0.00276 0.0232
cg27077827 chr15 60943536 RORA OpenSea Body −0.00272 0.0311
cg13146553 chr15 61343563 RORA OpenSea Body −0.00444 0.0320
cg12061096 chr15 61028241 RORA OpenSea Body −0.00416 0.0367
cg05673940 chr15 61334658 RORA OpenSea Body −0.00241 0.0390
cg12643950 chr15 61210235 RORA OpenSea Body −0.00273 0.0401
cg00237177 chr15 61233587 RORA OpenSea Body 0.00134 0.0444
cg21756476 chr15 61182765 RORA OpenSea Body 0.00716 0.0491
cg11227596 chr15 61029537 RORA OpenSea Body −0.00269 0.0495
cg01691639 chr15 61521458 RORA Island 5ʹUTR −0.00204 0.00293*
cg05334993 chr15 60888221 RORA S_Shelf Body 0.00246 0.0158
cg19170589 chr15 60883569 RORA Island Body −0.00165 0.0142
cg18064474 chr15 60885021 RORA Island Body −0.000368 0.0499
cg04600961 chr15 60787182 RORA OpenSea 3ʹUTR 0.00681 0.0381
cg14736496 chr15 60920638 RORA OpenSea TSS1500 0.00668 0.00822
cg09094797 chr9 77230369 RORB OpenSea Body −0.00658 0.0469
cg09363447 chr1 151787446 RORC OpenSea Body 0.00528 0.00180*
cg25206113 chr1 151788262 RORC OpenSea Body 0.00679 0.0404
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1Adjusted for sex, age, and % monocyte.

*Meets Bonferroni-corrected P value threshold (P < 0.004).

Sleep midpoint and DNA methylation results

There were 24 associations with P < 0.05, representing loci across 8 different genes, and there were 3 associations at the Bonferroni-corrected P value of <0.004. These included a positive association between midpoint and cg14170313, a CpG site within the island region of RORB (P = 0.001), a positive association with cg15603424, a CpG site within an island of BMAL1 at the 5ʹUTR (P = 0.003), and an inverse association with cg03301481, a site found within the gene body of PER1 (P = 0.0036).

Due to the fact that some circadian genes had a higher coverage (RORA, in particular, had 192 CpG sites while the remaining genes had between 19 and 64), we next calculated the proportion of loci within a particular gene that had statistically significant associations (P < 0.05). Several genes were found to have >5% of CpG sites with statistically significant associations (Table 3). To illustrate, later midpoint was associated with differential methylation of 11% of the CpG sites measured within PER1 and 9% with both CRY2 and NR1D1; and the majority of these associations were in the positive direction.

Table 3.

Associations between sleep and dietary patterns and DNA methylation of circadian genes in Mexican adolescents, overall description

  Number of probes in gene Proportion of probes with association P < 0.05 Number of probes in islands Proportion of island probes with P < 0.05 Direction of island probe association
(majority)
Sleep Midpoint (median of bed and wake time)      
BMAL 64 0.016 9 0.11 +
CLOCK 43 0.023 13 0  
CRY1 28 0 11 0  
CRY2 32 0.094 8 0  
NR1D1 22 0.091 12 0.17 +
NR1D2 26 0.077 10 0  
PER1 19 0.11 5 0  
PER2 39 0 6 0  
PER3 34 0 15 0  
RORA 192 0.063 28 0.18 +
RORB 26 0.038 4 0.25 +
RORC 23 0 0 0  
Diet pattern 1: Plant-based and lean proteins    
BMAL1 64 0.016 9 0  
CRY1 28 0 11 0  
CRY2 32 0 8 0  
CLOCK 43 0.023 13 0  
NR1D1 21 0.048 11 0  
NR1D2 26 0 10 0  
PER1 19 0.053 5 0.2 -
PER2 39 0.103 6 0  
PER3 34 0.059 15 0.07 -
RORA 192 0.010 28 0.04 -
RORB 26 0 4 0  
RORC 23 0 0 0  
Diet pattern 2: Meat & starchy foods    
BMAL1 64 0.047 9 0.11 +
CLOCK 43 0.023 13 0.08 -
CRY1 28 0.036 11 0.09 +
CRY2 32 0.031 8 0  
NR1D1 21 0 11 0  
NR1D2 26 0.077 10 0.1 +
PER1 19 0.053 5 0.2 -
PER2 39 0 6 0  
PER3 34 0 15 0  
RORA 192 0.026 28 0.04 +
RORB 19 0.053 4 0  
RORC 23 0 0 0  
Diet pattern 3: Eggs, milk & refined grain  
CLOCK 43 0.163 13 0  
BMAL1 64 0.141 9 0.11 -
CRY1 28 0.071 11 0  
CRY2 32 0.063 8 0.13 -
NR1D1 21 0.190 11 0.27 -
NR1D2 26 0.154 10 0.3 -
PER1 19 0.158 5 0.2 -
PER2 39 0.103 7 0.17 -
PER3 34 0.088 15 0  
RORA 192 0.005 28 0.14 -
RORB 26 0.038 4 0  
RORC 23 0.087 0 0  
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Next, we averaged DNA methylation levels across CpG sites within annotated CpG islands (some genes had coverage at multiple CpG islands). For sleep midpoint, there were positive associations between DNA methylation of RORA island 3 (β = 0.018; P = 0.045) and for RORB (β = 0.04; P = 0.003). In general, most associations between sleep midpoint and island regions were positively correlated (Table 4).

Table 4.

Associations between sleep and dietary patterns and DNA methylation of circadian gene CpG islands in adolescents

  All
 Boys
 Girls
  Beta coefficient1 P value Beta coefficient2 P value Beta coefficient2 P value
Sleep Midpoint (median of bed and wake time)        
PER1 .0140337 0.240 −.0102469 0.550 .0345147 0.042
PER2 .0040428 0.615 −.0017354 0.893 .0081995 0.421
PER3 island 1 −.0081581 0.664 −.0257108 0.286 .0083659 0.773
PER3 island 2 −.001583 0.848 .0043872 0.734 −.0082874 0.445
PER3 island 3 .0774699 0.096 .0523266 0.464 .1028792 0.099
CRY2 .0029527 0.711 .0088452 0.400 −.0039388 0.742
BMAL1 .0117632 0.140 .0107429 0.327 .0114565 0.331
CLOCK .0069998 0.298 .0026318 0.766 .0101371 0.320
RORA island 1 .0095559 0.204 .0031786 0.754 .0131733 0.233
RORA island 2 .0074266 0.442 −.0092649 0.503 .0215126 0.119
RORA island 3 .0175065 0.045 .0039397 0.768 .0281399 0.014
RORB .0353199 0.003 .0372594 0.050 .0330129 0.030
NR1D1 island 1 .0127016 0.244 .0116515 0.471 .0151853 0.317
NR1D1 island 2 .0080007 0.360 .0180043 0.134 −.0024247 0.846
NR1D2 .0110481 0.104 .0072648 0.448 .0134684 0.172
Diet pattern 1: Plant-based and lean proteins  
PER1 .0008977 0.023 .00123 0.007 .0002354 0.751
PER2 .0000904 0.791 −.0000792 0.857 .0005708 0.305
PER3 island 1 .0004076 0.408 .0003283 0.517 .0006691 0.498
PER3 island 2 .0000113 0.978 −.0001049 0.830 .0001876 0.807
PER3 island 3 −.0002882 0.939 −.002409 0.649 .0035876 0.520
CRY2 .0000126 0.935 .0001874 0.296 −.0003008 0.281
BMAL1 −.0001708 0.330 .0001164 0.562 −.0007621 0.019
CLOCK .0000122 0.893 .000103 0.314 −.0001479 0.386
RORA island 1 −.0000955 0.693 .0000768 0.783 −.0003675 0.400
RORA island 2 −.0001234 0.339 .0000478 0.760 −.0004734 0.039
RORA island 3 .0000297 0.864 .0002662 0.216 −.0003976 0.176
RORB −.0003219 0.282 −.000339 0.387 −.0003028 0.544
NR1D1 island 1 .0002256 0.509 .0006367 0.127 −.0006509 0.281
NR1D1 island 2 −.00004 0.766 .0000442 0.779 −.0001438 0.542
NR1D2 .0000639 0.514 .0000652 0.547 .0000771 0.681
Diet pattern 2: Meat & starchy foods  
PER1 .0001683 0.745 .0007263 0.373 −.0001832 0.792
PER2 −.0004792 0.277 −.0003113 0.686 −.0007297 0.161
PER3 island 1 .000674 0.291 .0011211 0.203 .0003541 0.703
PER3 island 2 −.0005741 0.285 −.0008578 0.314 −.0003475 0.629
PER3 island 3 −.0002193 0.964 −.0025248 0.785 .0015882 0.762
CRY2 −.0000955 0.632 .0001579 0.616 −.0003013 0.250
BMAL1 .000112 0.623 .0003808 0.277 −.0000548 0.860
CLOCK .0000891 0.450 .0003705 0.036 −.0001049 0.513
RORA island 1 .0002087 0.506 .0003642 0.455 4.67e-06 0.991
RORA island 2 .0000125 0.940 .0003688 0.175 −.0002097 0.336
RORA island 3 −.0000466 0.836 .0002262 0.550 −.0002784 0.314
RORB .0003113 0.422 −.0002669 0.698 .0006562 0.158
NR1D1 island 1 −.0001372 0.757 .0001722 0.815 −.0002654 0.641
NR1D1 island 2 −.0000136 0.938 .0002066 0.452 −.0002197 0.320
NR1D2 .0000413 0.745 .0001992 0.291 −.0000729 0.679
Diet pattern 3: Eggs, milk & refined grain  
PER1 −.0010701 0.083 −.0013847 0.082 −.0006233 0.533
PER2 −.0012047 0.022 −.0004974 0.511 −.0019366 0.008
PER3 island 1 −.0002299 0.764 .0001473 0.866 −.0007279 0.585
PER3 island 2 −.0006967 0.280 −.001359 0.102 3.43e-07 1.000
PER3 island 3 −.0031526 0.588 −.0033161 0.716 −.0035995 0.632
CRY2 −.0005672 0.017 −.0006632 0.029 −.0003686 0.328
BMAL1 −.0005028 0.064 −.0006093 0.075 −.000339 0.447
CLOCK −.0001487 0.293 −.0001681 0.340 −.000082 0.722
RORA island 1 −.0009331 0.012 −.0006093 0.202 −.0011282 0.053
RORA island 2 −.0002716 0.175 −.0006511 0.013 .0002449 0.435
RORA island 3 −.0006814 0.010 −.000516 0.163 −.0007874 0.045
RORB −.0004259 0.360 −.0003936 0.560 −.000444 0.509
NR1D1 island 1 −.0010925 0.038 −.0017573 0.013 −.0004323 0.597
NR1D1 island 2 −.0003233 0.120 −.000532 0.046 .0000399 0.900
NR1D2 −.0002782 0.066 −.0002651 0.152 −.0002584 0.305
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1Adjusted for sex, age, and % monocytes.

2Adjusted for age and % monocytes.

To ascertain whether any of the sleep midpoint findings could be attributed to sleep duration (since sleep midpoint and sleep duration were positively correlated), we ran sensitivity analyses using sleep duration as the outcome. We found essentially no overlap in the associations, indicating that sleep midpoint results were independent of sleep duration.

Diet pattern results

For Plant-based and lean proteins pattern, there were 12 associations at P < 0.05 across 7 genes with 1 at the Bonferroni-corrected threshold; the Meat & starchy foods diet pattern had 15 associations at P < 0.05 across 7 genes and 2 at the Bonferroni-corrected threshold; and the Eggs, milk & refined grain pattern had 62 associations at P < 0.05 from all 12 genes, and 4 at the Bonferroni-corrected threshold. Overall, the two most statistically significant associations (smallest P values) were for cg04917262 (found in the gene body of PER3) and the Plant-based and lean proteins diet pattern (P = 0.0006); and for cg19170589 (found in an island region of RORA) with the Meat & starchy foods diet pattern (P = 0.0007). Focusing in on the loci found in islands, higher adherence to the Plant-based and lean proteins diet pattern was associated with differential methylation in 10% of the PER2 CpG island loci, associations that were generally in the inverse direction. Multiple genes were highlighted in the Eggs, milk & refined grain pattern: BMAL1, NR1D1, NR1D2, PER1, and PER2 each had higher than 10% of CpG sites that were associated at P < 0.05, and in an inverse direction. Of these, 19% of the loci within NR1D1 were associated with the Eggs, milk & refined grain pattern.

Next, examining the associations between dietary patterns and the averaged CpG sites within island regions (Table 4), we found one positive association between the Plant-based and lean proteins diet pattern and PER1. There were no associations for Meat & starchy foods pattern. For the Eggs, milk & refined grain pattern, there were inverse associations for PER2, CRY2, RORA island 1, RORA island 3, and NR1D1 island 1. In general, most associations between the Eggs, milk & refined grain and island regions were inversely correlated, while directions of associations with the other dietary patterns were mixed.

Post-hoc analyses

Supplemental exploratory sex-stratified analyses revealed that many of the associations between sleep/dietary exposures and circadian gene CpG islands varied by sex (Table 4). For example, associations with sleep midpoint were more evident among girls. For dietary patterns, some associations (e.g., island of PER1 with the Plant-based and lean proteins pattern) were more evident among boys while others were more apparent among girls (e.g., island of PER2 with the Eggs, milk & refined grain pattern). Nonetheless, these analyses were underpowered.

Finally, we conducted post-hoc analysis among a subsample of participants who had RNA-seq data (Supplemental Table 1). We found weak inverse correlations, although not statistically significant, between island DNA methylation and gene expression in PER2 (−0.13), PER3 island 1 (−0.11), and RORB (−0.12). We found weak positive correlations between island DNA methylation and gene expression in BMAL1 (0.19), RORA island 3 (0.36), NR1D1 island 2 (0.14), and NR1D2 (0.27). The only correlation that was statistically significant was the positive correlation with RORA island 3.

Discussion

Within this cohort of Mexican adolescents, delayed sleep timing was generally associated with higher DNA methylation of core clock genes, whereas higher intake of a dietary pattern composed of breakfast foods was associated with lower methylation of core clock genes. In particular, actigraphy-assessed sleep timing was associated with DNA methylation of several core circadian genes, notably with RORB, PER1, CRY2, and NR1D1. Each of the three dietary patterns examined was also related to DNA methylation patterns, but the Eggs, milk & refined grain pattern (i.e., breakfast pattern) had the clearest evidence of relationships with circadian genes, with associations across all 12 genes. Within the CpG island regions, there were inverse associations between the Eggs, milk & refined grain pattern and PER2, CRY2, RORA, and NR1D1.

Prior studies examining the role of sleep on circadian gene DNA methylation patterns are rare, although a few adult studies exist. In line with the direction of the present findings, DNA methylation of core clock genes (candidate gene analysis) showed that sleep deprivation among nurses and midwives was associated with higher DNA methylation of PER2 [17], although it was associated with lower methylation of CRY2. The study did not examine the role of sleep timing, although sleep deprivation is likely correlated with misaligned sleep times, especially in this population of workers who often work night shifts. Another study of 124 health-care workers found higher methylation of PER3 (at three loci within the gene body) in nightshift workers compared to dayshift workers [14]. An experimental investigation of sleep deprivation and circadian gene methylation and expression among 15 young men found that in response to sleep deprivation, there was higher methylation of CRY1 and PER1 in adipose tissue, but no differentially methylated sites in skeletal tissue [18]. Differences in DNA methylation across tissues may help explain inconsistencies between studies that use leukocyte DNA and DNA from other tissues.

Associations between the Eggs, milk & refined grain pattern and circadian gene DNA methylation were particularly notable as they were more numerous than associations with sleep or the other dietary patterns. Furthermore, these associations were consistently inverse, which was opposite the direction observed for sleep timing. There may be a few explanations as to why the associations with this dietary pattern were robust and consistent. The Eggs, milk & refined grain pattern contains breakfast foods, and thus could be a proxy for the regular consumption of breakfast. The daily timing of eating, which begins with breakfast, is an important circadian regulator and may also have effects on metabolic health [19,20]. The nutrients abundant within foods comprising the Eggs, milk & refined grain dietary pattern may also play a role. Eggs and fortified grains are sources of folate, a nutrient that can play a role in maintaining DNA methylation profiles following mitosis [21,22]. In addition, eggs and dairy contain nutrients that feed the one-carbon metabolism pathway which is also important for maintaining DNA methylation, including vitamin B12, choline, and methionine [23]. Associations with the other two dietary patterns, the Plant-based and lean proteins and the Meat & starchy pattern, were much fewer in number. Yet, the direction of the associations with the Plant-based and lean proteins pattern, also rich in methyl donors such as folate and vitamin B12, was in the same direction as the Eggs, milk & refined grain pattern (inverse), while the associations with the Meat & starchy pattern were more mixed.

Overall, there was evidence that delayed sleep timing and diet patterns (in particular the breakfast pattern) were associated with DNA methylation of circadian genes in adolescents. The fact that these associations could be detected in a healthy population of adolescents is enlightening, because it indicates that even relatively constrained variability in sleep times and dietary patterns (as opposed to complete circadian shifts found in shift work populations) are associated with differences in circadian gene DNA methylation patterns. Due to the cross-sectional study design, it cannot be determined whether lifestyle behaviours preceded DNA methylation or vice versa. However, these findings provide preliminary support for a potential pathway whereby delayed sleep timing and regular consumption of breakfast foods are related to DNA methylation of circadian genes, which could ultimately affect gene expression and phenotypic outcomes such as BMI. Increasing evidence exists for associations between circadian gene methylation and BMI [24,25], although no studies have yet linked sleep, diet, DNA methylation, and BMI in a longitudinal manner. Another unique finding is that the associations may differ by sex. Of note was that associations with sleep midpoint were more pronounced (larger effect sizes and/or higher precision) among girls. Although speculative, it is worth mentioning that later midpoints among girls could be reflective of chronic insomnia, a sleep disorder that is much more prevalent among females than males [26], and which might have independent associations with DNA methylation [27].

In exploratory post-hoc analysis, we examined whether DNA methylation of these circadian genes was associated with gene expression in a subset of the participants. While this analysis was statistically underpowered, we observed correlations in the expected inverse direction for PER2, PER3 island 1, and RORB. In contrast, we also observed some positive correlations, with the strongest for RORA island 3 (the only statistically significant correlation) and NR1D2. These results provide some preliminary evidence that DNA methylation of these circadian genes could affect gene expression, although the magnitudes of these correlations are relatively low. The inconsistent directions of the correlations could also have to do with the fact that not all of the CpG islands were located within promoter regions (e.g., RORA island 3 was found in the gene body).

Strengths of the study include the use of objective data (from actigraphy) to characterize weekday sleep timing, and the ability to examine multiple important circadian genes. This pilot analysis had a limited sample size; thus, findings warrant replication in larger study populations and in populations from different ethnic backgrounds. Given that the study sample was reflective of a low to middle-SES population in Mexico City, the findings may also not be broadly generalizable to the entire Mexican population. Further, there may be unmeasured confounders, such as mental health and stress-related factors, which can be associated both with DNA methylation and with disrupted sleep and eating patterns [28]. The ability to complete fully powered sex-stratified analyses would also elucidate whether findings indeed differed for boys versus girls (as they appeared in exploratory analyses). The fact that the samples were leukocytes from whole blood also means we could not investigate DNA methylation within other tissues of interest for cardiometabolic health and/or sleep regulation such as brain, liver, or adipose tissue which were not feasible to collect in our study population. Finally, there was not access to chronotype information (whether adolescents were more ‘morning’ types or ‘evening’ types).

In summary, within a sample of Mexican adolescents, there were cross-sectional associations between sleep timing and eating patterns with DNA methylation of core circadian genes. Future work that links sleep, diet, and DNA methylation with longitudinal change in metabolic outcomes will be pivotal to understanding any potential effects of misaligned sleep and eating on long-term metabolic health in adolescents.

Methods

Study population

The study sample included adolescent participants from 2 of 3 sequentially enrolled cohorts of the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) study [29]. Between 1997 and 2004, 1012 mother/child dyads were recruited from prenatal clinics of the Mexican Social Security Institute in Mexico City, which serves low- to middle-income populations formally employed in the private sector. At baseline, mothers reported sociodemographic and health information. Children were followed from birth approximately every 6–12 months to 5 years of age and then periodically over mid-childhood and adolescence to gain information on growth and relevant environmental and nutritional exposures. In 2015, a subset of 550 participants from the original birth cohorts 2 and 3, who were in the midst of pubertal transition (ages 9 to 17 years), were selected to participate in a follow-up visit. The present analysis includes adolescents who took part in this 2015 follow-up visit (n = 550), held in a research facility of the ABC hospital of Mexico City. Within a pilot sample of 145 adolescents (selected for analysis based on availability of biospecimens and completeness of prior visits), epigenome-wide analyses were conducted with blood leukocyte DNA taken during the follow-up visit. Of these, 142 adolescents also had 7-day actigraphy collected for sleep estimation, taken in the week following the in-clinic visit. The institutional review boards at the Mexico National Institute of Public Health and the University of Michigan approved the research protocols. Informed consent was obtained from parents for all participants in addition to participant assent.

Sleep timing

At the end of the clinical follow-up, adolescents were given an actigraph (ActiGraph GT3X+; ActiGraph LLC, Pensacola, FL) to wear on their non-dominant wrist continuously for 7 days. Nightly sleep parameters were estimated from the actigraphic data with the use of a fusedLASSO (least absolute shrinkage and selection operator)-based calculator package developed in R (R Foundation for Statistical Computing, Vienna) [30]. The primary exposure of the study was average weekday midpoint. Midpoint refers to the median of sleep onset and wake time (reported in decimal hours), and a later sleep midpoint is considered a proxy for circadian misalignment.

Diet patterns

During the adolescent follow-up visit, a trained interviewer administered a 116-item semi-quantitative food frequency questionnaire to the adolescents to obtain information on typical consumption habits. The questionnaire asked adolescents to recall how often they had consumed one serving of a standard portion size of each food item over the previous week [31]. Photographs of food items were provided as a visual aid. Response options ranged from never to ≥6 times per day. We converted the raw response values (1–9) into servings/day. Dietary patterns were identified in a previous analysis using principal component analysis [32]. Each adolescent receives a score representing adherence to the dietary patterns, with higher scores representing higher similarity to the pattern. Three dietary patterns were identified in this cohort; the first pattern, called the Plant-based & lean proteins pattern, was characterized by high intake frequency of vegetables, fruit, soup, fish, water and unsweetened drinks, and high-fat dairy. The second pattern, called Meat & starchy, was marked by high intake frequency of ‘Western’ processed foods including chips, refined grains, sugar-sweetened beverages, processed meat, and high-fat dairy, as well as consumption more in line with ‘traditional’ foods such as the Mexican food group (e.g., tacos and quesadillas), potatoes and fried plantains, soup, legumes, and corn tortillas. The third pattern, the Eggs, milk & refined grain pattern, was characterized by high intake frequency of refined grains, milk, sweetened milk, mayonnaise or margarine, and eggs.

DNA methylation

Whole blood was collected via venipuncture and stored in EDTA-preservative at −80°C prior to processing. DNA was isolated from blood leukocytes via the Flexigene kit (Qiagen). For epigenome-wide analysis of DNA methylation, blood leukocyte DNA samples were first treated with sodium bisulphite, which converts unmethylated cytosines to uracils leaving methylated cytosines unchanged [33]. DNA methylation profiles were then assessed with the Infinium MethylationEPIC BeadChip (Illumina). The BeadChip interrogates methylation at >850,000 cytosine-guanine dinucleotides (CpG sites) in gene promoters, enhancers, within gene bodies, and in intergenic regions [34]. Bisulphite converted samples were randomized across chips and chip positions, hybridized to BeadChips, and signals read and processed by experienced personnel in the University of Michigan Advanced Genomics Core. Data produced by the BeadChip were processed according to a pipeline designed for this technology and previously utilized[35]. Briefly, raw data consisting of average betas, the proportion of methylated cytosines at each site, were read into R Project for Statistical Computing using minfi [36]. Background correction, dye bias correction, and functional normalization to remove unwanted technical variation estimated from control probes included on each chip were performed [37,38]. Poorly detected probes (in at least 5% of samples), probes known to be cross-reactive [39], and probes with polymorphisms in the CpG site or the single base extension site were excluded. Cell type proportions from the blood leukocyte samples were estimated based on data from tissue-specific differentially methylated regions included on the BeadChip [40,41]. Final data consisted of beta values which are representative of the proportion methylated (from 0 to 1) at each CpG site. Data were extracted from all probes annotated of 12 circadian genes, including BMAL1, CLOCK, CRY1, CRY2, NR1D1, NR1D2, PER1, PER2, PER3, RORA, RORB, and RORC, with 548 total CpG sites (average of 46 CpG sites/gene, with a range of 19 to 192). These genes play an important role in establishing circadian rhythms [42]. BMAL1 and CLOCK genes form the positive limb of a feedback loop and are expressed at night, while PER and CRY genes form the negative feedback loop and are expressed during the day. ROR genes and NR1D1/NR1D2 are nuclear receptors that are part of the stabilizing loop for the core feedback loop. Mutations in these genes can result in altered circadian phases and abnormal sleep patterning [43].

Gene expression analysis via RNA-sequencing

For a subset of participants with adequate RNA quality and quantity (n = 72), next-generation sequencing of RNA (‘RNA-Seq’) was completed to obtain relative expression data for all genes, and results were utilized from the 12 circadian genes of interest to this study. Following collection of whole blood into EDTA-containing tubes, white blood cells were extracted, preserved in RNALater, and stored frozen (−80°C) until further processing. RNA was isolated via the All-Prep kit (Qiagen). Quality and quantity were assessed via a Bioanalyzer Tapestation (Agilent). Libraries were prepared with Universal Plus mRNA-Seq with Human globin AnyDeplete (NuGEN Technologies, Inc.) which removes globin transcripts that are highly abundant in blood samples. Library preparation and sequencing were performed at the University of Michigan Advanced Genomics Core. Paired-end 50 cycle sequencing on an Illumina HiSeq 4000 was performed. Quality of the raw reads data for each sample was checked using FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/; version 0.11.3). The Tuxedo Suite software package was used for alignment [44–46]. Briefly, reads were aligned to the reference mRNA transcriptome (hg19) (http://genome.ucsc.edu/) using TopHat (version 2.0.13) and Bowtie2 (version 2.2.1.) followed by a second round of post-alignment quality control in FastQC. One sample was dropped due to low alignment rates. All samples used in downstream analysis (n = 71) had at least 16.8 million good quality aligned reads. Prior to analysis, read counts were normalized by the trimmed mean of M-values method [47].

Covariates

Other covariates included in the present analysis were sex, age, BMI-for-age Z scores, maternal education, physical activity and percentage of monocytes (as a proportion of total leukocytes, described above). Trained research assistants measured height in cm (Tonelli E120 A) and weight in kg (InBody230). BMI Z scores (BMIz) accounting for age and sex were calculated based on the World Health Organization reference [48]. Maternal education was reported by mothers at the original cohort enrolment visit and was categorized as <9 years, 9 to 11 years, 12 years, or >12 years of schooling. Physical activity was estimated from the accelerometers as average minutes of moderate/vigorous activity per day as previously described [49].

Statistical analysis

Means ± SD of sociodemographic and lifestyle characteristics were first estimated according to earlier versus later sleep midpoint (split at the median). Next, linear regression analyses were used to evaluate the associations between midpoint and each circadian gene CpG site, adjusting for age, sex, and % monocyte. Adjustment for other covariates did not affect results and thus were not retained. Sensitivity analyses with sleep duration as the outcome were also conducted, to determine whether results were similar for sleep midpoint and duration. Similar regression models were run for each dietary pattern score (all dietary patterns in separate models). Next, DNA methylation at annotated CpG island regions within these genes was examined. To do so, data from all of the CpG sites within a specific island region (some genes had more than one island region) were averaged (see Supplemental Table 1 for location of island sites and description of the location, i.e., promoter region versus gene body). Linear regression analysis was then conducted in the same manner as for the individual CpG sites, but with the averaged island regions as the outcomes. For these models, exploratory sex-stratified analyses were also run.

Finally, whether the % DNA methylation in the island regions corresponded to gene expression (RNA-seq) was examined. Using a subset of participants (N = 71) that also had RNA-seq data, the correlations between the % methylation of the islands with RNA-seq data of the corresponding genes were obtained.

Due to the exploratory nature of the analyses, any findings at P < 0.05 are reported. A Bonferroni-corrected P-value threshold of P < 0.004 (0.05 divided by 12 genes) was also applied. All analyses were performed in Stata 14.0.

Supplementary Material

Supplemental Material
Click here for additional data file. (21.2KB, docx)

Acknowledgments

We gratefully acknowledge the ELEMENT research staff and the American British Cowdray Medical Center (ABC) in Mexico for providing research facilities.

Funding Statement

The ELEMENT study was supported by the US Environmental Protection Agency (US EPA) grant RD83543601 and National Institute for Environmental Health Sciences (NIEHS) grants P01 ES02284401, and P30 ES017885. Dr Jansen reports support from the National Institutes of Health/National Heart, Lung, and Blood Institute grant T32HL110952 during the conduct of the study. The epigenetic analyses were supported by the NIEHS grant 1U2C ES026553, and we acknowledge support from the Bioinformatics Core and the Advanced Genomics Core of the University of Michigan Medical School’s Biomedical Research Core Facilities.

Disclosure statement

Dr Chervin has served on member boards or advisory board for the American Academy of Sleep Medicine, Associated Professional Sleep Societies, International Pediatric Sleep Association, Sweet Dreamzzz, and the Pajama Program; and received royalties from UpToDate and Cambridge University Press. Authors report no conflicts of interest.

Supplementary material

Supplemental data for this article can be accessed here.

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