Prenatal exposure to maternal cigarette smoke (PEMCS) is one of the most common insults to the developing fetus and has consistently emerged as an important risk factor for excess weight in the offspring. However, no consensus exists on the mechanism of action or duration of impact. This study seeks to further examine the role of PEMCS on overweight status of children up to age 10. Mother and child pairs (
Concern is mounting over the increase in prevalence and severity of overweight and obesity in children worldwide. Increases in overweight and obesity rates have been observed among both sexes, and across all socioeconomic groups with the strongest and most substantial increases in the developed world [
Childhood excess weight is ultimately a result of an energy imbalance between intake and expenditure [
Prenatal exposure to maternal cigarette smoking (PEMCS) is one of the most common insults to intrauterine life [
The
The QLSCD collects information about both children and their parents using structured self-completed questionnaires and face-to-face interviews with mothers and fathers (
Childhood weight status at ten years of age was the outcome of interest. Height (in meters) and weight (in kilograms) were measured by trained staff at the child’s place of residence, using a detailed protocol and standard instruments (standard scale and measuring tape). Measurements falling between two major units were rounded down. Children were classified as being: “underweight/normal weight” or “overweight/obese” using the sex- and age-specific BMI cut-offs defined by the International Obesity Task Force (IOTF) [
The main predictor for the analysis was whether a child respondent was exposed to tobacco smoke in-utero. This variable was self-reported by the mother when the child was 5 months old.
The study team received legal access to all participating families’ medical records for a period of 90 days after the mothers signed an authorization form created by the Ministry of Health and Social Services. Birth weight was extracted from each child’s delivery record from the birth hospital and recorded as a continuous variable. This variable was then categorized based on standard and clinically meaningful cut-points for analyses: low birth weight (<2.5 kg), normal birth weight (≥2.5 and ≤4 kg), and high birth weight (>4 kg).
Catch-up growth was derived as the difference between the mother-reported weight of the child at 5 months of age and the birth weight obtained from medical records. The continuous catch-up growth variable was converted into tertiles for analysis.
Potential predictors of childhood overweight that were identified from published literature and available in the QLSCD database were considered for inclusion in final models. These variables included those related to birth and early life factors (whether the birth was premature, the birth rank, the sex, and APGAR score of the baby, whether the baby suffered from a chronic disease at 5 months, and the duration of breastfeeding), maternal characteristics (age at child’s birth, highest level of education, immigrant status, postnatal smoking habits, and weight status), child behaviour lifestyle factors (energy intake, relative physical activity, and sedentary behaviour), and family demographic and socioeconomic factors (household income, single-parent or two-parent home, and geographic living area). Covariate data were obtained from different cycles of the longitudinal study, based on data collection time points and response rates at each cycle. Data from the cycle deemed most epidemiologically relevant for each covariate were used whenever possible.
All statistical analyses were conducted using SAS version 9.2 (SAS Institute; Cary, NC). The statistical significance level for all analyses was set at an alpha value of 0.05. The Chi-squared test of independence and univariate logistic regression was used to examine crude associations between the outcome and main predictor variables (including possible mediating variables), between the outcome and covariates, and between the main predictor variables and covariates.
Automated stepwise logistic regression was used to create final models with the entry value set at 0.20 and retention value set at 0.05 for all models. The main predictor of interest (PEMCS) was forced into all models. Candidate covariates entered in automated regression models were chosen based on an association with the main predictor, an association with the outcome or based on an
Of 2120 family participants recruited into the QLSCD in 1998, 1280 children were still being followed at age 10, and 1183 (55.8% of the original sample) had no item-missing data for key variables (PEMCS, measured height and weight, birth weight, and catch-up growth). Children included in this analysis were similar to those excluded on available variables (data not shown).
Using the IOTF definitions for overweight, 25% of respondent children included in the analysis were overweight at age 10, with a mean BMI of 16.87 and 22.88 for the normal weight and overweight groups, respectively. Table
Descriptive characteristics of children included in the analysis by overweight or obesity status at age 10 (
Cycle collected | Overweight or obese children ( |
Normal weight children ( |
|
---|---|---|---|
|
|
||
Birth and Early Life | |||
5 months | PEMCS | ||
No | 209 (68.5%) | 692 (78.8%)* | |
Yes | 96 (31.5%) | 186 (21.2%) | |
Medical records | Birth weight | ||
>4 kg | 45 (14.9%) | 91 (10.3%) | |
≤2.5 kg and ≤4 kg | 249 (81.8%) | 762 (86.8%) | |
<2.5 kg | 11 (3.3%) | 25 (2.9%) | |
Medical records | Premature birth (<37 weeks) | ||
No | 285 (93.4%) | 846 (96.4%)* | |
Yes | 20 (6.6%) | 32 (3.6%) | |
Medical records | APGAR score |
||
Other | 265 (87.0%) | 809 (92.1%)* | |
High risk (0–6) | 40 (13.0%) | 69 (7.9%) | |
Medical records | Birth rank | ||
First | 126 (41.4%) | 407 (46.3%) | |
Second | 130 (42.7%) | 333 (37.9%) | |
≥Third | 48 (15.9%) | 139 (15.8%) | |
Medical records | Sex | ||
Female | 160 (52.3%) | 467 (53.2%) | |
Male | 145 (47.7%) | 411 (46.8%) | |
5 months | Catch-up growth | ||
1st tertile | 101 (33.1%) | 316 (36.0%) | |
2nd tertile | 101 (33.1%) | 310 (35.3%) | |
3rd tertile | 103 (33.8%) | 252 (28.7%) | |
5 months | Chronic disease |
||
No | 261 (85.4%) | 751 (85.6%) | |
Yes | 44 (14.6%) | 127 (14.4%) | |
17 months | Duration of breastfeeding (exclusive) | ||
≥3 months | 86 (28.2%) | 234 (26.7%) | |
Other | 133 (43.7%) | 421 (47.9%) | |
Never | 86 (28.1%) | 223 (25.4%) | |
| |||
Maternal | |||
5 months | Age at child’s birth (years) | ||
≤29 | 60 (19.5%) | 185 (21.1%) | |
30–34 | 92 (30.2%) | 285 (32.5%) | |
35–39 | 108 (35.4%) | 288 (32.8%) | |
≥40 | 45 (14.9%) | 120 (13.6%) | |
10 years | Level of education | ||
≥Secondary school diploma | 270 (88.4%) | 786 (89.5%) | |
<Secondary school diploma | 35 (11.6%) | 92 (10.5%) | |
10 years | Immigrant Status | ||
Nonimmigrant | 279 (91.4%) | 818 (93.2%) | |
Immigrant | 26 (8.6%) | 60 (6.8%) | |
10 years | Postnatal smoking habits | ||
Non-smoker | 229 (75.1%) | 751 (85.6%)* | |
Smoker | 76 (24.9%) | 127 (14.4%) | |
17 months | Weight status | ||
Normal weight | 163 (53.4%) | 666 (75.8%)* | |
Overweight/obese | 142 (46.6%) | 212 (24.2%) | |
| |||
Child Behaviour and Lifestyle | |||
4 years | Energy intake (Quintiles) | ||
Other (1–4) | 218 (71.4%) | 743 (84.6%)* | |
High (5) | 87 (28.6%) | 135 (15.4%) | |
6 years | Physical activity (compared to other children) | ||
Same | 214 (70.1%) | 576 (65.6%) | |
Higher/much higher | 91 (29.9%) | 302 (34.4%) | |
6 years | Sedentary behaviour ( |
||
No | 285 (93.4%) | 822 (93.7%) | |
Yes | 20 (6.6%) | 56 (6.3%) | |
| |||
Demographic and Socioeconomic | |||
10 years | Household income ($) | ||
<30,000 | 34 (11.3%) | 61 (7.0%)* | |
30,000–49,999 | 98 (32.2%) | 215 (24.5%) | |
50,000–79,999 | 95 (31.2%) | 318 (36.2%) | |
≥80,000 | 77 (25.3%) | 284 (32.3%) | |
10 years | Family type | ||
Two parent | 214 (70.2%) | 694 (79.1%)* | |
Single-parent | 91 (29.8%) | 184 (20.9%) | |
10 years | Geographical living area | ||
Rural | 103 (33.9%) | 319 (36.3%) | |
Urban | 202 (6.1%) | 559 (3.7%) |
*
Denotes a statistically significant difference of covariate proportion between overweight and nonoverweight participants (
Source: Québec Longitudinal Study of Child Development (QLSCD) 1998–2010, Québec Institute of Statistics.
At the bivariable level, PEMCS was significantly and positively associated with being overweight at age 10 (
From the multivariable logistic regression analysis, the adjusted association between PEMCS and overweight at age 10 was positive and statistically significant (OR: 1.70; 95% CI: 1.20–2.43) (Table
Odds ratios (or) and 95% confidence intervals (CI) of PEMCS and relevant covariates included in the final model on overweight status (IOTF) at age 10.
Covariate | Category | Unadjusted OR | 95% CI | Adjusted OR |
95% CI |
---|---|---|---|---|---|
PEMCS | No | 1.00 | — | 1.00 | — |
Yes | 1.71 | [1.23, 2.19] | 1.70 | [1.20, 2.43] | |
APGAR score | Other | 1.00 | — | 1.00 | — |
High risk | 1.75 | [1.15–2.66] | 1.80 | [1.09, 2.98] | |
Mother’s immigrant status | Non immigrant | 1.00 | — | 1.00 | — |
Immigrant | 1.29 | [0.80, 2.08] | 1.80 | [1.00, 3.24] | |
Mother’s weight status | Normal weight | 1.00 | 1.00 | ||
Overweight | 2.71 | [2.06, 3.58] | 2.89 | [2.10, 3.99] | |
Family type | Two parent | 1.00 | — | 1.00 | — |
Single-parent | 1.60 | [1.15, 2.22] | 1.62 | [1.08, 2.45] | |
Energy intake | Other (1–4) | 1.00 | — | 1.00 | — |
High (5) | 2.19 | [1.57, 3.05] | 2.17 | [1.51, 3.13] |
Source: Québec Longitudinal Study of Child Development (QLSCD) 1998–2010, Québec Institute of Statistics.
In the multivariable examination of possible mediation of the relationship between PEMCS and overweight status at age 10 (Table
The Odds ratios (OR) and 95% confidence intervals (CI) for the association between overweight (IOTF) at age 10 and PEMCS with adjustment for possible mediators.
Base model (model 1) |
Adjusted for birth weight (model 2) |
Adjusted for catch-up growth (model 3) |
Adjusted for both (model 4) |
|
---|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
PEMCS | ||||
No | 1.00 | 1.00 | 1.00 | 1.00 |
Yes | 1.70 (1.19–2.43) | 1.76 (1.23–2.51) | 1.64 (1.15–2.33) | 1.73 (1.21, 2.48) |
Source: Québec Longitudinal Study of Child Development (QLSCD) 1998–2010, Québec Institute of Statistics.
Despite the well-documented deleterious effects of PEMCS, it remains one of the most common insults to the developing fetus. The epidemiological evidence demonstrating an association between PEMCS and increased risk for excess weight is strong and consistent, but the underlying mechanisms remain largely speculative. Our study sought to evaluate the relationship between PEMCS and the risk of overweight or obesity of children and to examine the possible mediating role of birth weight and catch-up growth. Since PEMCS has consistently emerged as an important risk factor for low birth weight, it has been proposed as a possible mediator of the PEMCS-overweight relationship. Babies of low birth weight often experience a rapid catch-up growth phase during infancy or childhood, and this has been proposed as a potential pathway to link PEMCS to excess weight. To our knowledge, our study is the first to empirically investigate the hypothesis of a combined mediation effect of low birth weight and catch-up growth.
PEMCS was found to be a significant and independent predictor of childhood overweight at 10 years of age among Québec children even after adjusting for several important social and biological factors. Although our study demonstrated a positive association between PEMCS and both low birth weight and catch-up growth, neither potential mediator was related to overweight at age 10 in our sample. Furthermore, no attenuation of the PEMCS-overweight status association occurred when birth weight, catch-up growth, or both were included in the multivariable model. Thus, our findings do not support the hypothesis that low birth weight and/or catch-up growth are mediators of the PEMCS-childhood overweight relationship.
That PEMCS remained a significant risk factor for excess childhood adiposity independent of a wide range of common correlates of both the exposure, and the outcome supports the conclusions of recent systematic reviews [
Given that we found no strong evidence of mediation involving birth weight or catch-up growth, the mechanism through which PEMCS may lead to excess weight among offspring remains unclear. Possible alternative mechanisms exist at the hypothalamic or fat cell level. These include altered appetite behaviour due to alterations of cholinergic neurotransmitter systems [
It may be argued that the relationship between PEMCS and excess weight in offspring is not causal at all, but due to an unaccounted confounding factor, such as postnatal exposure to smoking. However, previous studies have suggested that paternal smoking can explain little [
Our findings need to be considered while acknowledging certain limitations. The primary outcome of this study was weight status of children based on established international cut-offs for BMI. The limitations of BMI are very well known [
The developmental origins of excess weight and the notion of priming chronic disease early in life are complex. Prospective longitudinal studies with repeated measures of heights and weights are needed to further quantify and compare the effects of early life risks. Regardless of the mechanism of action, if the evidence continues to support a causal role for PEMCS in contributing to childhood overweight and obesity, this represents an important opportunity for prevention. PEMCS is a key modifiable risk factor for a number of adverse pregnancy outcomes, highlighting its importance as a target for preventive action. Given the tracking of relative weight status from childhood through to adult life, targeting overweight and obesity early in life in turn may have lifelong impacts on physical health and quality of life.
Data were collected by the Québec Institute of Statistics for the Longitudinal Study of the Child Development of Québec. The authors would also like to acknowledge both Megan Carter and Dr. Tim Ramsay. J. Gravel was a recipient of a Frederick Banting and Charles Best Canada Graduate Scholarship from the Canadian Institute of Health Research (CIHR) at the time this work was done.