Edited by: Bernhard Schaller, University of Zurich, Switzerland
Reviewed by: Kedra Wallace, University of Mississippi Medical Center, United States; Pamela Schuetze, University at Buffalo, United States
This article was submitted to Autonomic Neuroscience, a section of the journal Frontiers in Physiology
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Negative associations of prenatal tobacco and alcohol exposure (PTE and PAE) on birth outcomes and childhood development have been well documented, but less is known about underlying mechanisms. A possible pathway for the adverse fetal outcomes associated with PTE and PAE is the alteration of fetal autonomic nervous system development. This study assessed PTE and PAE effects on measures of fetal autonomic regulation, as quantified by heart rate (HR), heart rate variability (SD-HR), movement, and HR-movement coupling in a population of fetuses at ≥ 34 weeks gestational age. Participants are a subset of the Safe Passage Study, a prospective cohort study that enrolled pregnant women from clinical sites in Cape Town, South Africa, and the Northern Plains region, United States. PAE was defined by six levels: no alcohol, low quit early, high quit early, low continuous, moderate continuous, and high continuous; while PTE by 4 levels: no smoking, quit early, low continuous, and moderate/high continuous. Linear regression analyses of autonomic measures were employed controlling for fetal sex, gestational age at assessment, site, maternal education, household crowding, and depression. Analyses were also stratified by sleep state (1F and 2F) and site (South Africa,
Many deleterious effects of alcohol consumption during pregnancy on fetal development, birth outcomes, and subsequent childhood development are well-documented (
A possible marker of effects of alcohol and smoking on fetal development is autonomic nervous system (ANS) activity as assessed through measures of fetal heart rate (HR) and heart rate variability (HRV). HRV is the variation in the heart’s beat-to-beat intervals and it is regulated by the ANS, through the synergistic activity of the parasympathetic and sympathetic branches (
Studies investigating the effects of PAE on fetal HRV have obtained mixed results: some, performed during maternal intoxication, showed reduced fetal HRV (
In sum, research to date has primarily investigated acute effects of high levels of PAE or PTE on fetal HR or HRV. Additionally, the effects of acute and chronic maternal smoking and alcohol consumption on FHR and fetal HRV have been investigated independently of one another. This leaves a significant gap in the literature for understanding the dual effects of chronic low and moderate alcohol and tobacco use during pregnancy on function and development of fetal autonomic nervous system. The dataset analyzed in this report is a subset of the Safe Passage Study conducted by the Prenatal Alcohol and SIDS and Stillbirth (PASS) Network (
From 2007 until 2015, the Safe Passage Study followed the outcomes of ∼12,000 pregnancies among women from two comprehensive clinical site (CCS), one in the Cape Town area, South Africa, and one in the Northern Plains, United States. The United States site is comprised of five clinical sites in North Dakota and South Dakota, including two sites on American Indian Reservations. In South Africa, recruitment occurred from Bishop Lavis and Belhar residential areas within Cape Town, which serve mainly the multiracial population (South African multiracial ethnic group (multiracial group), which have ancestry from more than one of the various populations inhabiting the region, including Khoisan, Bantu, European, Austronesian, and East Asian or South Asian). Screening and enrollment occurred at prenatal clinics affiliated with each CCS between 6 weeks gestation up to, but not including, delivery. Ethical approval was obtained from Stellenbosch University, Sanford Health, the Indian Health Service, and New York State Psychiatric Institute. Written informed consent to record fetal HR was part of the consent for the main study. Maternal and infant charts were abstracted to obtain demographic and relevant clinical information. We excluded participants with maternal health conditions known to affects our outcome measures (gestational diabetes, preeclampsia, hypertension), psychiatric medication use during pregnancy (SSRI’s, antidepressants, classic antipsychotics, atypical antipsychotics, mood stabilizers, stimulants, antianxiety medications, or anticonvulsants), any recreational drug use during pregnancy, multiple births, and congenital anomalies.
The protocol used to obtain detailed information about quantity and timing of prenatal exposure to alcohol and smoking is presented in
We then used clustering techniques to characterize multiple patterns of maternal drinking and smoking behaviors (
Fetal assessments were performed at 34–38 weeks gestation (Mean ± SD = 35.4 ± 1.2 weeks). Assessments were completed between 9 am and 4 pm and lasted approximately 50 min. Mothers were seated in a reclining chair or were lying supine with a 15° lateral tilt and fitted with the recording equipment. Mothers were undisturbed for the first 20 min of data collection and then answered questions on alcohol and smoking habits, recreational drug use and depression during the remaining 30 min. Fetal HR and movement data were collected using a single wide-array Doppler transducer placed on the maternal abdomen connected to a Toitu MT-320 or a MT-516 model Doppler actocardiograph (Toitu Company, Ltd., Toyko, Japan). FHR and FMOV signals were digitized at 20 Hz using a custom-built physiological data acquisition hardware and software system (DATACQ, Medelex, Inc) interfaced to a laptop computer. Specific details on the acquisition protocol can be found in previous articles (
Mean HR and standard deviation (SD) of HR were computed for each epoch, using only the non–interpolated values. The median fetal movement was computed for each accepted fetal HR epoch except in cases where the fetal movement signal exceeded the range of the Toitu fetal movement amplifier or was not present. These cases comprised 2.5% of all records and were due to equipment failure or user error. In addition, the cross-correlation of fetal HR and movement (heart rate/movement coupling) and the lag (seconds) between movement and fetal HR derived from the cross-correlation function were computed for each accepted 4-min fetal HR epoch. The fetal HR and movement signals were first low-pass filtered between 0.002 and 0.05 Hz using a 400-point FIR filter. The fetal movement signal was z–transformed and the fetal HR was further processed by subtracting the mean from a local regression of 6 s and negative fetal HR values were set to zero (
Linear regression analyses were used to estimate the associations between exposure categories and HR and HRV and movement parameters. We fit separate models for HR mean, SD, and the cross-correlation of fetal HR and movement parameters as outcomes. All models included sex and gestational age at assessment as covariates (
In total, 11,929 mother-infant pairs were enrolled in the Safe Passage Study. We performed fetal HR assessments on 9240. Because of their known association with outcomes (
Mean age at enrollment was 25.8 ± 5.7 years (Mean ± SD) and most participants (95.1%) had at least some high school education, and roughly half of the participants were employed. The population was composed of individuals who self-identified as white, multiracial, American Indians/Alaska natives or Other/unknown races.
A total of 51.9% of the women drank and 42.7% smoked at some point during pregnancy. Of the smokers, 14.9%, 24.1%, and 3.8% were grouped into high/moderate, low continuous and quit early groups, respectively. For alcohol 4.6%, 9.1%, 8.6%, 5.4%, and 24.3% were grouped into high continuous, moderate continuous, low continuous, and high quit early and low quit early groups, respectively.
Fetuses were assessed on average at 35.2 (± 1.3 SD) weeks of gestation. They were successively born at 39.4 (± 1.4 SD) weeks gestational age.
Maternal and infant demographics and prenatal exposure information.
South Africa and Northers Plain ( |
South Africa ( |
Northers Plain ( |
|
Maternal age | 25.80 ± 5.72 | 24.99 ± 5.89 | 27.11 ± 5.15 |
317 (4.9%) | 289 (7.2%) | 28 (1.1%) | |
3995 (46.1%) | 2638 (65.5%) | 357 (14.5%) | |
1324 (20.4%) | 915 (22.7%) | 409 (16.6%) | |
1849 (28.5%) | 177 (4.4%) | 1672 (67.8%) | |
2550 (39.3%) | 2087 (51.9%) | 463 (18.8%) | |
3925 (60.5%) | 1923 (47.8%) | 2002 (81.2%) | |
2973 (45.8%) | 2323 (57.7%) | 650 (26.4%) | |
2954 (45.5%) | 1266 (31.5%) | 1688 (68.5%) | |
Crowding index | 1.23 ± 0.89 | 1.55 ± 0.89 | 0.71 ± 0.59 |
689 (10.6%) | 0 | 689 (27.9%) | |
4010 (61.8%) | 4010 (99.6%) | 0 | |
1594 (24.6%) | 0 | 1594 (64.6%) | |
198 (3.1%) | 15 (0.4%) | 183 (7.4%) | |
Edinburgh depression scale | 9.73 ± 6.44 | 12.57 ± 5.92 | 5.09 ± 4.18 |
3717 (57.3%) | 1721 (42.8%) | 1996 (80.9%) | |
244 (3.8%) | 111 (2.8%) | 133 (5.4%) | |
1565 (24.1%) | 1335 (33.2%) | 230 (9.3%) | |
965 (14.9%) | 858 (21.3%) | 107 (4.3%) | |
3120 (48.07%) | 1881 (46.73%) | 1239 (50.24%) | |
1575 (24.26%) | 721 (17.91%) | 854 (34.63%) | |
348 (5.36%) | 161 (4.00%) | 187 (7.58%) | |
560 (8.63%) | 517 (12.84%) | 43 (1.74%) | |
590 (9.09%) | 472 (11.73%) | 118 (4.79%) | |
298 (4.59%) | 273 (6.79%) | 25 (1.02%) | |
GA at assessment (days) | 35.21 ± 1.26 | 34.88 ± 7.05 | 35.75 ± 1.42 |
GA at birth (weeks) | 39.39 ± 1.41 | 39.36 ± 1.47 | 39.43 ± 1.32 |
3221 (49.6%) | 1981 (49.2%) | 1240 (50.3%) | |
3270 (50.4%) | 2044 (50.8%) | 1226 (49.7%) |
Cross Tabulation of smoking and drinking groups in the overall population, South Africa population, and Northern Plains population.
Alcohol exposure | |||||||
None | Low quit early | High quit early | Low continuous | Moderate continuous | High continuous | ||
Smoking exposure | 2029 | 1082 | 198 | 206 | 147 | 55 | |
95 | 79 | 23 | 20 | 17 | 10 | ||
638 | 277 | 76 | 226 | 248 | 100 | ||
358 | 137 | 51 | 108 | 178 | 133 | ||
Smoking exposure | 1007 | 364 | 58 | 172 | 75 | 45 | |
41 | 35 | 7 | 19 | 8 | 1 | ||
526 | 219 | 53 | 221 | 220 | 96 | ||
307 | 103 | 43 | 105 | 169 | 131 | ||
Smoking exposure | 1022 | 718 | 140 | 34 | 72 | 10 | |
54 | 44 | 16 | 1 | 9 | 9 | ||
112 | 58 | 23 | 5 | 28 | 4 | ||
51 | 34 | 8 | 3 | 9 | 2 |
Number of drinks and binge events by trimester per alcohol group in the overall population, South Africa population, and Northern Plains population.
Alcohol exposure | ||||||
None | Low quit early | High quit early | Low continuous | Moderate continuous | High continuous | |
Total # drinks trimester 1 | 0.040 ± 0.003 | 6.014 ± 0.109 | 19.683 ± 0.379 | 2.049 ± 0.151 | 25.130 ± 0.995 | 60.671 ± 4.505 |
Total # drinks trimester 2 | 0.018 ± 0.002 | 0.105 ± 0.130 | 0.300 ± 0.047 | 3.530 ± 0.130 | 7.501 ± 0.359 | 35.239 ± 2.921 |
Total # drinks trimester 3 | 0.011 ± 0.001 | 0.297 ± 0.004 | 0.146 ± 0.028 | 0.872 ± 0.054 | 2.918 ± 0.163 | 17.257 ± 1.776 |
Total # drinks in pregnancy | 0.068 ± 0.004 | 6.148 ± 0.113 | 20.089 ± 0.386 | 6.448 ± 0.212 | 35.558 ± 0.851 | 113.167 ± 5.891 |
Total # binge events trimester 1 | 0.00 ± 0.00 | 0.43 ± 0.01 | 2.16 ± 0.03 | 0.11 ± 0.01 | 2.18 ± 0.10 | 5.71 ± 0.37 |
Total # binge events trimester 2 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.26 ± 0.02 | 0.66 ± 0.04 | 3.79 ± 0.32 |
Total # binge events trimester 3 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.23 ± 0.02 | 1.67 ± 0.17 |
Total # binge events in pregnancy | 0.00 ± 0.00 | 0.43 ± 0.01 | 2.16 ± 0.03 | 0.37 ± 0.03 | 3.07 ± 0.09 | 11.17 ± 0.53 |
Total # drinks trimester 1 | 0.032 ± 0.003 | 6.056 ± 0.156 | 18.856 ± 0.533 | 1.992 ± 0.156 | 19.996 ± 1.037 | 50.951 ± 3.889 |
Total # drinks trimester 2 | 0.024 ± 0.002 | 0.186 ± 0.026 | 0.476 ± 0.094 | 3.670 ± 0.139 | 9.081 ± 0.407 | 38.02 ± 3.118 |
Total # drinks trimester 3 | 0.013 ± 0.002 | 0.037 ± 0.008 | 0.257 ± 0.055 | 0.825 ± 0.056 | 3.463 ± 0.190 | 18.629 ± 1.913 |
Total # drinks in pregnancy | 0.068 ± 0.004 | 6.280 ± 0.165 | 19.589 ± 0.552 | 6.517 ± 0.222 | 32.541 ± 0.925 | 107.600 ± 5.933 |
Total # binge events trimester 1 | 0.00 ± 0.00 | 0.47 ± 0.02 | 2.17 ± 0.04 | 0.11 ± 0.01 | 1.87 ± 0.10 | 5.21 ± 0.37 |
Total # binge events trimester 2 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.27 ± 0.02 | 0.80 ± 0.04 | 4.11 ± 0.34 |
Total # binge events trimester 3 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.28 ± 0.02 | 1.79 ± 0.17 |
Total # binge events in pregnancy | 0.00 ± 0.00 | 0.47 ± 0.01 | 2.17 ± 0.03 | 0.39 ± 0.03 | 2.59 ± 0.10 | 11.12 ± 0.57 |
Total # drinks trimester 1 | 0.052 ± 0.005 | 5.978 ± 0.152 | 20.340 ± 0.531 | 2.734 ± 0.613 | 45.667 ± 1.756 | 166.817 ± 24.181 |
Total # drinks trimester 2 | 0.008 ± 0.002 | 0.036 ± 0.007 | 0.743 ± 0.026 | 1.453 ± 0.330 | 1.220 ± 0.393 | 4.857 ± 3.628 |
Total # drinks trimester 3 | 0.007 ± 0.002 | 0.023 ± 0.005 | 0.049 ± 0.020 | 1.431 ± 0.188 | 0.737 ± 0.182 | 2.273 ± 1.397 |
Total # drinks in pregnancy | 0.068 ± 0.004 | 6.280 ± 0.165 | 19.589 ± 0.552 | 6.517 ± 0.222 | 32.541 ± 0.925 | 173.947 ± 23.854 |
Total # binge events trimester 1 | 0.00 ± 0.00 | 0.39 ± 0.02 | 2.15 ± 0.04 | 0.07 ± 0.04 | 3.43 ± 0.20 | 11.12 ± 1.24 |
Total # binge events trimester 2 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.07 ± 0.04 | 0.03 ± 0.02 | 0.32 ± 0.25 |
Total # binge events trimester 3 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.28 ± 0.02 | 1.79 ± 0.17 |
Total # binge events in pregnancy | 0.00 ± 0.00 | 0.39 ± 0.01 | 2.10 ± 0.44 | 0.14 ± 0.06 | 3.57 ± 0.20 | 11.76 ± 1.18 |
Number of cigarettes per week by trimester per smoking group in the overall population, South Africa population, and Northern Plains population.
Smoking exposure | ||||
None | Quit early | Low continuous | Moderate/high continuous | |
Average # cigarettes/week in trimester 1 | 0.011 ± 0.0012 | 8.38 ± 0.57 | 15.57 ± 0.24 | 47.73 ± 0.70 |
Average # cigarettes/week in trimester 2 | 0.0025 ± 0.0005 | 0.74 ± 0.13 | 15.77 ± 0.27 | 50.48 ± 0.81 |
Average # cigarettes/week in trimester 3 | 0.0077 ± 0.0010 | 0.11 ± 0.014 | 14.75 ± 0.26 | 46.94 ± 0.80 |
Average # cigarettes/week in pregnancy | 0.0071 ± 0.0006 | 2.86 ± 0.19 | 15.36 ± 0.22 | 48.38 ± 0.67 |
Average # cigarettes/week in trimester 1 | 0.008 ± 0.002 | 8.715 ± 0.574 | 16.003 ± 0.256 | 46.229 ± 0.732 |
Average # cigarettes/week in trimester 2 | 0.002 ± 0.001 | 0.100 ± 0.232 | 17.007 ± 0.283 | 50.156 ± 0.861 |
Average # cigarettes/week in trimester 3 | 0.006 ± 0.002 | 0.098 ± 0.021 | 15.999 ± 0.275 | 46.739 ± 0.828 |
Average # cigarettes/week in pregnancy | 0.005 ± 0.001 | 2.971 ± 0.252 | 16.337 ± 0.229 | 47.708 ± 0.708 |
Average # cigarettes/week in trimester 1 | 0.0142 ± 0.002 | 8.111 ± 0.829 | 13.079 ± 0.678 | 59.759 ± 2.036 |
Average # cigarettes/week in trimester 2 | 0.003 ± 0.001 | 0.053 ± 0.014 | 8.596 ± 0.678 | 53.044 ± 2.524 |
Average # cigarettes/week in trimester 3 | 0.009 ± 0.001 | 0.115 ± 0.021 | 7.491 ± 0.566 | 48.5883 ± 2.751 |
Average # cigarettes/week in pregnancy | 0.009 ± 0.001 | 2.760 ± 0.276 | 9.722 ± 0.511 | 53.797 ± 2.041 |
Linear regression results from alcohol and smoking exposure predicting Mean HR in 1F.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 1460 | / | / | 1027 | / | / | 433 | / | / |
Alcohol low quit early | 682 | −0.322 (0.375) | 0.390 | 413 | 0.0197 (0.4607) | 0.966 | 269 | −0.9368 (0.6607) | 0.157 |
Alcohol high quit early | 146 | 1.135 (0.698) | 0.104 | 84 | 2.170 (0.8989) | 62 | −0.346 (1.1396) | 0.761 | |
Alcohol low continuous | 298 | −0.217 (0.518) | 0.675 | 284 | 0.0924 (0.5333) | 0.863 | 14 | −0.8672 (2.2564) | 0.701 |
Alcohol moderate continuous | 286 | 0.202 (0.536) | 0.706 | 255 | 0.5537 (0.5722) | 0.333 | 31 | −0.8683 (1.5548) | 0.577 |
Alcohol high continuous | 135 | 1.431 (0.742) | 0.054 | 129 | 2.0443 (0.7601) | 6 | −4.549 (3.4929) | 0.193 | |
No smoking | 1641 | / | / | 950 | / | / | 691 | / | / |
Smoking quit early | 96 | 1.909 (0.845) | 53 | 0.9037 (1.115) | 0.418 | 43 | 3.3568 (1.3482) | ||
Smoking low continuous | 766 | −0.413 (0.390) | 0.290 | 722 | −0.6005 (0.4068) | 0.140 | 44 | −0.103 (1.3928) | 0.941 |
Smoking moderate/high continuous | 504 | −0.682 (0.450) | 0.130 | 467 | −1.0197 (0.4743) | 37 | 1.0713 (1.4572) | 0.4624 |
Linear regression results from alcohol and smoking exposure predicting Mean HR in 2F.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 2774 | / | / | 1767 | / | / | 1007 | / | / |
Alcohol low quit early | 1362 | −0.043 (0.254) | 0.867 | 682 | 0.367 (0.342) | 0.283 | 680 | −0.5566 (0.3832) | 0.147 |
Alcohol high quit early | 291 | 0.128 (0.467) | 0.783 | 150 | 1.1246 (0.643) | 0.0804 | 141 | −0.9834 (0.6819) | 0.149 |
Alcohol low continuous | 530 | −0.135 (0.367) | 0.714 | 496 | 0.1759 (0.387) | 0.6498 | 34 | −0.8102 (1.32) | 0.539 |
Alcohol moderate continuous | 553 | 0.067 (0.370) | 0.857 | 457 | 0.4089 (0.4085) | 0.3169 | 76 | −0.6608 (0.9086) | 0.467 |
Alcohol high continuous | 275 | 0.580 (0.492) | 0.239 | 257 | 1.089 (0.518) | 18 | −2.147 (1.803) | 0.234 | |
No smoking | 3300 | / | / | 1634 | / | / | 1666 | / | / |
Smoking quit early | 188 | 0.395 (0.569) | 0.488 | 100 | −0.433 (0.779) | 0.578 | 88 | 1.271 (0.849) | 0.135 |
Smoking low continuous | 1394 | −0.844 (0.271) | 1257 | −1.0343 (0.295) | 137 | −0.38 (0.7184) | 0.597 | ||
Smoking moderate/high continuous | 883 | −1.252 (0.318) | 818 | −1.510 (0.341) | 65 | −0.0829 (0.9792) | 0.933 |
Linear regression results from alcohol and smoking exposure predicting HR-SD in 1F.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 1460 | / | / | 1027 | / | / | 433 | / | / |
Alcohol low quit early | 682 | 0.0203 (0.034) | 0.550 | 413 | 0.0413 (0.0421) | 0.327 | 269 | −0.0256 (0.0585) | 0.661 |
Alcohol high quit early | 146 | 0.0827 (0.0632) | 0.191 | 84 | 0.1158 (0.0822) | 0.159 | 62 | 0.0233 (0.1008) | 0.818 |
Alcohol low continuous | 298 | 0.0711 (0.0469) | 0.130 | 284 | 0.0903 (0.0488) | 0.0641 | 14 | 0.0125 (0.1997) | 0.950 |
Alcohol moderate continuous | 286 | 0.0973 (0.0486) | 255 | 0.1336 (0.0523) | 31 | −0.084 (0.1376) | 0.542 | ||
Alcohol high continuous | 135 | −0.0706 (0.0672) | 0.294 | 129 | −0.0392 (0.0695) | 0.5725 | 6 | −0.2214 (0.3091) | 0.474 |
No smoking | 1641 | / | / | 950 | / | / | 691 | / | / |
Smoking quit early | 96 | 0.0022 (0.0765) | 0.977 | 53 | −0.0655 (0.102) | 0.521 | 43 | 0.0865 (0.1193) | 0.469 |
Smoking low continuous | 766 | 0.0197 (0.0353) | 0.577 | 722 | 0.0004 (0.0372) | 0.991 | 44 | 0.0653 (0.1232) | 0.596 |
Smoking moderate/high continuous | 504 | −0.0236 (0.0407) | 0.563 | 467 | −0.0673 (0.0434) | 0.121 | 37 | 0.2735 (0.1289) |
Linear regression results from alcohol and smoking exposure predicting HR−SD in 2F.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 2774 | / | / | 1767 | / | / | 1007 | / | / |
Alcohol low quit early | 1362 | 0.00545 (0.0442) | 0.902 | 682 | −0.0001 (0.0555) | 0.998 | 680 | 0.0407 (0.0740) | 0.582 |
Alcohol high quit early | 291 | 0.06202 (0.08102) | 0.444 | 150 | −0.0939 (0.1045) | 0.369 | 141 | 0.2575 (0.1317) | 0.051 |
Alcohol low continuous | 530 | 0.00782 (0.0636) | 0.902 | 496 | −0.0324 (0.0629) | 0.607 | 34 | 0.4756 (0.255) | 0.062 |
Alcohol moderate continuous | 553 | −0.0146 (0.0642) | 0.820 | 457 | −0.0486 (0.0664) | 0.464 | 76 | 0.1333 (0.1755) | 0.448 |
Alcohol high continuous | 275 | 0.0195 (0.0855) | 0.820 | 257 | 0.0024 (0.0842) | 0.977 | 18 | 0.0236 (0.3483) | 0.946 |
No smoking | 3300 | / | / | 1634 | / | / | 1666 | / | / |
Smoking quit early | 188 | −0.00164 (0.0988) | 0.987 | 100 | −0.0544 (0.126) | 0.667 | 88 | 0.0171 (0.1641) | 0.917 |
Smoking low continuous | 1394 | 0.0216 (0.0470) | 0.646 | 1257 | 0.0147 (0.0479) | 0.759 | 137 | 0.1492 (0.1388) | 0.283 |
Smoking moderate/high continuous | 883 | −0.0868 (0.0553) | 0.116 | 818 | −0.04703 (0.0555) | 0.397 | 65 | −0.3987 (0.1892) |
Linear regression results from alcohol and smoking exposure predicting mean fetal movement in 1F.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 1460 | / | / | 1027 | / | / | 433 | / | / |
Alcohol low quit early | 682 | −0.0384 (0.0524) | 0.464 | 413 | 0.0116 (0.0673) | 0.864 | 269 | −0.1247 (0.0833) | 0.135 |
Alcohol high quit early | 146 | 0.1518 (0.0981) | 0.122 | 84 | 0.135 (0.1319) | 0.306 | 62 | 0.239 (0.1912) | 0.212 |
Alcohol low continuous | 298 | 0.0442 (0.0722) | 0.541 | 284 | 0.0756 (0.0780) | 0.332 | 14 | −0.3511 (0.2723) | 0.198 |
Alcohol moderate continuous | 286 | 0.0230 (0.0748) | 0.758 | 255 | 0.0291 (0.0836) | 0.728 | 31 | 0.0899 (0.2796) | 0.748 |
Alcohol high continuous | 135 | 0.0847 (0.1036) | 0.413 | 129 | 0.0865 (0.1113) | 0.437 | 6 | 0.6305 (0.7516) | 0.402 |
No smoking | 1641 | / | / | 950 | / | / | 691 | / | / |
Smoking quit early | 96 | 0.0822 (0.1175) | 0.485 | 53 | 0.131 (0.1627) | 0.421 | 43 | 0.003 (0.163) | 0.985 |
Smoking low continuous | 766 | −0.0501 (0.0544) | 0.358 | 722 | −0.0391 (0.0594) | 0.511 | 44 | −0.0847 (0.1726) | 0.624 |
Smoking moderate/high continuous | 504 | −0.1358 (0.0627) | 467 | −0.1085 (0.0694) | 0.118 | 37 | −0.3421 (0.1761) | 0.052 |
Linear regression results from alcohol and smoking exposure predicting mean fetal movement in 2F.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 2774 | / | / | 1767 | / | / | 1007 | / | / |
Alcohol low quit early | 1362 | −0.0861 (0.0392) | 682 | −0.0176 (0.0527) | 0.738 | 680 | −0.172 (0.06) | ||
Alcohol high quit early | 291 | 0.0486 (0.0723) | 0.502 | 150 | 0.1008 (0.0993) | 0.310 | 141 | −0.049 (0.1329) | 0.713 |
Alcohol low continuous | 530 | −0.0641 (0.0562) | 0.254 | 496 | −0.0341 (0.0597) | 0.568 | 34 | −0.1731 (0.2017) | 0.391 |
Alcohol moderate continuous | 553 | 0.0318 (0.0566) | 0.574 | 457 | 0.0596 (0.063) | 0.344 | 76 | −0.0904 (0.1901) | 0.635 |
Alcohol high continuous | 275 | 0.0112 (0.0754) | 0.137 | 257 | 0.1433 (0.0799) | 0.073 | 18 | −0.1994 (0.4537) | 0.66 |
No smoking | 3300 | / | / | 1634 | / | / | 1666 | / | / |
Smoking quit early | 188 | 0.1095 (0.087) | 0.208 | 100 | 0.2179 (0.1199) | 0.069 | 88 | −0.0455 (0.128) | 0.722 |
Smoking low continuous | 1394 | −0.0258 (0.0415) | 0.535 | 1257 | −0.0172 (0.0455) | 0.705 | 137 | −0.0624 (0.1102) | 0.571 |
Smoking moderate/high continuous | 883 | −0.1258 (0.0487) | 818 | −0.1047 (0.0526) | 65 | −0.2535 (0.1476) | 0.086 |
Linear regression results from alcohol and smoking exposure predicting fetal movement/HR cross-correlation Lag.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 2812 | / | / | 1709 | / | / | 1103 | / | / |
Alcohol low quit early | 1473 | −0.0399 (0.0793) | 0.615 | 677 | −0.129 (0.114) | 0.257 | 760 | 0.0549 (0.1078) | 0.611 |
Alcohol high quit early | 310 | 0.0081 (0.144) | 0.955 | 147 | −0.331 (0.215) | 0.124 | 163 | 0.3180 (0.1884) | 0.092 |
Alcohol low continuous | 506 | −0.2655 (0.1188) | 472 | −0.288 (0.131) | 34 | −0.5247 (0.3903) | 0.179 | ||
Alcohol moderate continuous | 538 | 0.142 (0.116) | 0.222 | 428 | 0.122 (0.139) | 0.378 | 110 | 0.038 (0.226) | 0.867 |
Alcohol high continuous | 270 | −0.0959 (0.1575) | 0.543 | 248 | −0.146 (0.174) | 0.400 | 22 | −0.2326 (0.4862) | 0.632 |
No smoking | 3372 | / | / | 1587 | / | / | 1785 | / | / |
Smoking quit early | 222 | 0.1859 (0.1675) | 0.267 | 105 | 0.0722 (0.252) | 0.774 | 117 | 0.3031 (0.2203) | 0.169 |
Smoking low continuous | 1406 | −0.0445 (0.0848) | 0.600 | 1210 | −0.0015 (0.0989) | 0.987 | 196 | −0.1401 (0.181) | 0.439 |
Smoking moderate/high continuous | 873 | 0.0497 (0.1005) | 0.621 | 779 | 0.129 (0.115) | 0.260 | 94 | −0.3993 (0.2439) | 0.102 |
Linear regression results from alcohol and smoking exposure predicting HR/fetal movement cross-correlation.
Exposure category | Both sites | South Africa | Northern Plains | ||||||
N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | N | Effect size Mean difference (SE) | ||||
No alcohol | 2812 | / | / | 1709 | / | / | 1103 | / | / |
Alcohol low quit early | 1473 | 0.0031 (0.0035) | 0.375 | 677 | 0.0035 (0.0048) | 0.471 | 760 | 0.0028 (0.0052) | 0.590 |
Alcohol high quit early | 310 | −0.0022 (0.0064) | 0.727 | 147 | −0.0171 (0.0091) | 0.061 | 163 | 0.0109 (0.0091) | 0.229 |
Alcohol low continuous | 506 | 0.0081 (0.0053) | 0.127 | 472 | 0.0064 (0.0056) | 0.255 | 34 | 0.0098 (0.0188) | 0.604 |
Alcohol moderate continuous | 538 | 0.000061 (0.0052) | 0.991 | 428 | −0.0042 (0.0059) | 0.481 | 110 | 0.0122 (0.0109) | 0.261 |
Alcohol high continuous | 270 | −0.0032 (0.00701) | 0.647 | 248 | −0.0028 (0.0074) | 0.704 | 22 | −0.0269 (0.0234) | 0.251 |
No smoking | 3372 | / | / | 1587 | / | / | 1785 | / | / |
Smoking quit early | 222 | −0.0109 (0.0075) | 0.142 | 105 | −0.0148 (0.0107) | 0.166 | 117 | −0.0049 (0.0106) | 0.646 |
Smoking low continuous | 1406 | −0.00205 (0.00377) | 0.587 | 1210 | 0.00095 (0.0042) | 0.821 | 196 | −0.0132 (0.0087 | 0.132 |
Smoking moderate/high continuous | 873 | 0.00203 (0.00447) | 0.650 | 779 | 0.0026 (0.0049) | 0.593 | 94 | 0.0071 (0.0118) | 0.547 |
In the analysis with
Sex, GA at assessment and site were all significantly related to HR-SD in both fetal states. In state 1F males had higher HR-SD than females (β = −0.06 ± 0.026,
Site was significantly associated with fetal movement in state 1F with mean levels of movement greater in the South Africa cohort (β = −0.19 ± 0.08,
For fetal HR/movement cross-correlation, GA at assessment and site were significant predictor (higher values with increasing GA β = 0.0008 ± 0.0002,
In
In the
In summary, expected findings of sex on autonomic regulation were observed, with females having higher HR and lower HR-SD. GA at assessment was also significant in many associations that are consistent with the literature, with HR decreasing and variability increasing with GA (
In the analyses considering both
Estimated marginal means from linear regression models of mean fetal HR in 2F, shown for the overall population (black), South Africa (red), and Northern Plains (blue).
Smoking was also significantly associated with fetal movement in both states 1F and 2F. The moderate/high continuous group showed lower fetal movement compared to the non-smokers (1F: decrease of 0.14 ± 0.06 a.u.,
Estimated marginal means from linear regression models of mean fetal movement, shown for the overall population by fetal behavioral sleep state (green 1F and red 2F).
In
Regarding the association of smoking and fetal movement, in South Africa subjects there was as significant reduction in fetal movement in the moderate/high group only in 2F was observed (decrease of 0.10 ± 0.05 a.u.,
In the
In the dataset with both sites combined, we found a significant association between PAE and the HR-SD in 1F, with an increase for the moderate continuous group by 0.10 ± 0.05 BPM (
The fetal movement/HR cross-correlation lag of the low continuous group was 0.27 s shorter than the non-drinkers (β = −0.27 ± 0.12,
In the
Estimated marginal means from linear regression models of mean fetal HR, shown for the SA population by fetal behavioral sleep state (green 1F and red 2F).
The low continuous group showed shorter cross-correlation lag times between movement and change in HR than the non-drinkers (β = −0.29 ± 0.13,
In the
Several studies have reported associations of smoking and drinking during pregnancy with negative gestational outcomes and health in offspring (
In this study, PTE was associated with a decrease in mean HR in fetal state 2F, both in the overall population and in the sub-analysis on the SA population. These effects appeared to be dependent on dose, in that the mean decreases in HR were greatest in the fetuses of mothers who smoked at the highest levels and were not significant in fetuses of women who quit smoking during the first trimester. There were no significant PTE exposure effects in state 2F in the Northern Plans cohort; however, the number of subjects in the highest exposure group in the NP was only 65 as compared to 818 in SA. In state 1F, overall, there a significant
Combining both sites there were no significant effects of PTE on HR-SD. However, in the NP, the moderate-high continuous groups showed an increase in HR-SD in 1F and a decrease in 2F. The fact that PTE was associated with divergent effects in the two fetal sleep states and only in the NP site was unexpected. However, this effect might represent less differentiated state dependent autonomic activity in some populations. In both sleep states in the combined data set PTE was also associated with a significant reduction in fetal movement in the most highly exposed group. This association was significant or approaching significance at both sites.
Tobacco cigarette smoke contains several substances which can potentially be harmful to the fetus, of these, nicotine is the most studied. Nicotine enters in the mother’s bloodstream quickly and easily crosses the placenta into the fetal bloodstream (
Results from our study address the chronic effect of smoking exposure during pregnancy, which seems to go in the opposite direction of most acute studies, with a decrease in mean HR usually interpreted as a result of parasympathetic activation or sympathetic inhibition (or a combination of the two). There are few studies assessing fetuses chronically exposed to cigarette smoke. In one small study (
Importantly, from a public health perspective, HR parameters of fetuses whose mothers quit smoking by the end of first trimester were not significantly different from those of non-smokers. This is in line with epidemiological findings showing that risk of stillbirth to mothers who stopped smoking during the first trimester was comparable to the risk among women who were non-smokers during the entire pregnancy (
In this study there were few significant findings regarding the associations of alcohol and fetal physiology. While differences in findings between sites for the high continuous group could be due to different distributions of participants across exposure groups, it is worth noting that similar results were not observed in the high quit early groups, which had similar number of subjects in the two sites. One possible interpretation for the different site findings, is differential rates of alcohol metabolization, potentially related to body mass index (BMI). Mothers’ diet can affect the fraction of body mass composed by adipose tissue, which is relevant since ingested alcohol distributes through the body water differently between lean and fat body mass (
To our knowledge, no previous studies have investigated the effects of chronic alcohol consumption during pregnancy on ANS function. Nonetheless, a few studies have investigated other aspects of fetal neurobehavior, such as behavioral states, and spontaneous and elicited startles. Hepper et al. showed that alcohol consumption delayed the decrease in the incidence of fetal startles observed with normal development. Regarding elicited startles, they found instead that fetuses exposed to alcohol were less likely to startle in response to sound than fetuses of non-drinkers (
PAE and PTE are risk factors for adverse fetal and neonatal outcomes such as intra uterine growth restriction, SIDS, and these same outcomes have been associated with altered ANS profiles (
Limitations of this study include the possible under-reporting of PTE and PAE due to the use of self-report measure and the lack of information on acute smoking in recordings from mothers in the low, moderate and high continuous group. We do not know the precise interval between the last cigarette that the mothers smoked and the fetal assessment. Nonetheless, given the typical time required to transport the participant and prepare for the study protocol, it is highly unlikely that women smoked a cigarette in the hour before fetal monitoring. Another limitation is the lack of precise information on time of the day of assessments, which could affect HR since fetuses start to show circadian autonomic regulation during the third trimester. In addition, part of the fetal data collection occurred while mothers were responding to questionnaires, which could have affected maternal and fetal HR regulation. Lastly, our data could reflect a potential selection bias, since the effect of alcohol and smoking on fetal autonomic parameters were not investigated in adverse pregnancy outcomes such as early delivery or fetal demise.
In conclusion, this investigation addresses a significant gap in the literature on the association smoking and drinking during pregnancy with fetal autonomic regulation. To our knowledge, this study is unique both due to the size of the cohort and the comprehensive characterization of patterns of PTE and PAE, summarized in data driven exposure groups, taking into account both timing and magnitude of exposure. We believe these results can contribute to identifying biomarkers and potentially understanding the mechanisms underlying risk for adverse outcomes.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The studies involving human participants were reviewed and approved by Health Research Ethics Committee of Stellenbosch University, Sanford Health’s Institutional Review Board, New York State Psychiatric Institute of Institutional Review Board, and Indian Health Service Institutional Review Board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
ML, LS, NP, AS, MM, WF, HO, and AE contributed to the conception and design of the study. LS, CP, CF, JA, LB, LTB, and CG contributed to the acquisition. ML, JN, LS, AS, NP, and MN contributed to the analysis of data. All authors significantly contributed to the interpretation of the data and drafting the article.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The following researchers comprised the PASS Network: PASS Steering Committee Chair (University of Texas Medical Branch): Gary DV Hankins, MD. Data Coordinating & Analysis Center (DM-STAT, Inc.): PI: Kimberly A Dukes, Ph.D.; Co-PI: Lisa M Sullivan, Ph.D.; Biostatistics: Tara Tripp, MA; Fay Robinson, MPH; Cheri Raffo, MPH; Project Management/Regulatory Affairs: Julie M Petersen, BA; Rebecca A Young, MPH; Statistical Programming/Data Management: Cindy Mai, BA; Elena Grillo, MBA BS, BBA; Data Management/Information Technology: Travis Baker, BS; Patti Folan; Gregory Toland, MS; Michael Carmen, MS. Developmental Biology & Pathology Center (Children’s Hospital Boston): PI: Hannah C Kinney, MD; Assistant Director: Robin L Haynes, Ph.D.; Co-investigators: Rebecca D Folkerth, MD; Ingrid A Holm, MD; Theonia Boyd, MD; David S Paterson, Ph.D.; Hanno Steen, Ph.D.; Kyriacos Markianos, Ph.D.; Drucilla Roberts, MD; Kevin G Broadbelt, Ph.D.; Richard G Goldstein, MD; Laura L. Nelsen, MD; Jacob Cotton, BS; Perri Jacobs, BS. Comprehensive Clinical Site Northern Plains (Sanford Research): PI: Amy J Elliott, Ph.D.; Co-PI: Larry Burd, Ph.D.; Co-investigators: Jyoti Angal, MPH; Jessica Gromer, RN; H Eugene Hoyme, MD; Margaret Jackson, BA; Luke Mack, MA; Bradley B Randall, MD; Mary Ann Sens, MD; Deborah Tobacco, MA; Peter Van Eerden, MD. Comprehensive Clinical Site South Africa (Stellenbosch University): PI: Hendrik Odendaal, MBChB, FRCOG, MD; Co-PI: Colleen Wright, MD, FRCPath, Ph.D.; CoInvestigators: Lut Geerts, MD, MRCOG; Greetje de Jong, MBChB, MMed, MD; Pawel Schubert, FCPath (SA) MMed; Shabbir Wadee, MMed; Johan Dempers, FCFor Path (SA); Elsie Burger, FCFor Path (SA), MMed Forens Path; Janetta Harbron, Ph.D.; Co-investigator & Project Manager: Coen Groenewald, MBChB, MMed, FCOG, M Comm. Physiology Assessment Center (Columbia University): Co-PIs: William Fifer, Ph.D.; Michael Myers, Ph.D.; Coinvestigators: Joseph Isler, Ph.D.; Yvonne Sininger, Ph.D.; Project Management: J David Nugent, MA; Carmen Condon, BA; Data Analysis: Margaret C Shair, BA; Tracy Thai, MA. NIH Project Scientists: Marian Willinger, Ph.D. (NICHD); Dale Hereld, MD, Ph.D. (NIAAA); Howard J Hoffman, MA (NIDCD); Chuan-Ming Li, MD, Ph.D. (NIDCD).
The Supplementary Material for this article can be found online at:
Study flowchart.
Missing data imputation.
Alcohol and smoking cluster analysis.
Data processing.
Fetal state coding.