Parents’ smoking onset before conception as related to body mass index and fat mass in adult offspring: Findings from the RHINESSA generation study

Gerd Toril Mørkve Knudsen; Shyamali Dharmage; Christer Janson; Michael J Abramson; Bryndís Benediktsdóttir; Andrei Malinovschi; Svein Magne Skulstad; Randi Jacobsen Bertelsen; Francisco Gomez Real; Vivi Schlünssen; Nils Oskar Jõgi; José Luis Sánchez-Ramos; Mathias Holm; Judith Garcia-Aymerich; Bertil Forsberg; Cecilie Svanes; Ane Johannessen

doi:10.1371/journal.pone.0235632

. 2020 Jul 6;15(7):e0235632. doi: 10.1371/journal.pone.0235632

Parents’ smoking onset before conception as related to body mass index and fat mass in adult offspring: Findings from the RHINESSA generation study

Gerd Toril Mørkve Knudsen ^1,^2,^*, Shyamali Dharmage ³, Christer Janson ⁴, Michael J Abramson ⁵, Bryndís Benediktsdóttir ^6,⁷, Andrei Malinovschi ⁸, Svein Magne Skulstad ², Randi Jacobsen Bertelsen ^1,⁹, Francisco Gomez Real ¹, Vivi Schlünssen ^10,¹¹, Nils Oskar Jõgi ^1,^2,¹², José Luis Sánchez-Ramos ¹³, Mathias Holm ¹⁴, Judith Garcia-Aymerich ^15,^16,¹⁷, Bertil Forsberg ¹⁸, Cecilie Svanes ^2,^19,^‡, Ane Johannessen ^19,^‡

Editor: Seana Gall²⁰

¹Department of Clinical Science, University of Bergen, Bergen, Norway

²Department of Occupational Medicine, Haukeland University Hospital, Bergen, Norway

³School of Population and Global Health, The University of Melbourne, Carlton, Australia

⁴Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden

⁵School of Public Health & Preventive Medicine, Monash University, Melbourne, Australia

⁶Faculty of Medicine, University of Iceland, Reykjavik, Iceland

⁷Department of Sleep Medicine, Landspitali, Reykjavik, Iceland

⁸Department of Medical Sciences: Clinical Physiology, Uppsala University, Uppsala, Sweden

⁹Oral Health Center of Expertise in Western Norway, Hordaland, Bergen, Norway

¹⁰Department of Public Health, Work, Environment and Health, Danish Ramazzini Centre, Aarhus University Denmark, Aarhus, Denmark

¹¹National Research Centre for the Working Environment, Copenhagen, Denmark

¹²Lung Clinic, Tartu University Hospital, Tartu, Estonia

¹³Department of Nursing, Huelva University, Huelva, Spain

¹⁴Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden

¹⁵ISGlobal, Barcelona, Spain

¹⁶Universitat Pompeu Fabra (UPF), Barcelona, Spain

¹⁷CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

¹⁸Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

¹⁹Centre for International Health, Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway

²⁰University of Tasmania, AUSTRALIA

Competing Interests: The authors have declared that no competing interests exist.

‡ These authors are joint senior authors on this work.

^✉

* E-mail: gerd.knudsen@uib.no

Roles

Gerd Toril Mørkve Knudsen: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Shyamali Dharmage: Data curation, Funding acquisition, Investigation, Methodology, Supervision, Writing – review & editing

Christer Janson: Data curation, Funding acquisition, Writing – review & editing

Michael J Abramson: Data curation, Writing – review & editing

Bryndís Benediktsdóttir: Data curation, Funding acquisition, Writing – review & editing

Andrei Malinovschi: Data curation, Writing – review & editing

Svein Magne Skulstad: Supervision, Writing – review & editing

Randi Jacobsen Bertelsen: Data curation, Funding acquisition, Writing – review & editing

Francisco Gomez Real: Data curation, Funding acquisition, Writing – review & editing

Vivi Schlünssen: Data curation, Funding acquisition, Writing – review & editing

Nils Oskar Jõgi: Data curation, Writing – review & editing

José Luis Sánchez-Ramos: Data curation, Funding acquisition, Writing – review & editing

Mathias Holm: Data curation, Writing – review & editing

Judith Garcia-Aymerich: Data curation, Methodology, Writing – review & editing

Bertil Forsberg: Data curation, Writing – review & editing

Cecilie Svanes: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Ane Johannessen: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Seana Gall: Editor

PMCID: PMC7337347 PMID: 32628720

Abstract

Emerging evidence suggests that parents’ preconception exposures may influence offspring health. We aimed to investigate maternal and paternal smoking onset in specific time windows in relation to offspring body mass index (BMI) and fat mass index (FMI). We investigated fathers (n = 2111) and mothers (n = 2569) aged 39–65 years, of the population based RHINE and ECRHS studies, and their offspring aged 18–49 years (n = 6487, mean age 29.6 years) who participated in the RHINESSA study. BMI was calculated from self-reported height and weight, and FMI was estimated from bioelectrical impedance measures in a subsample. Associations with parental smoking were analysed with generalized linear regression adjusting for parental education and clustering by study centre and family. Interactions between offspring sex were analysed, as was mediation by parental pack years, parental BMI, offspring smoking and offspring birthweight. Fathers’ smoking onset before conception of the offspring (onset ≥15 years) was associated with higher BMI in the offspring when adult (β 0.551, 95%CI: 0.174–0.929, p = 0.004). Mothers’ preconception and postnatal smoking onset was associated with higher offspring BMI (onset <15 years: β1.161, 95%CI 0.378–1.944; onset ≥15 years: β0.720, 95%CI 0.293–1.147; onset after offspring birth: β2.257, 95%CI 1.220–3.294). However, mediation analysis indicated that these effects were fully mediated by parents’ postnatal pack years, and partially mediated by parents’ BMI and offspring smoking. Regarding FMI, sons of smoking fathers also had higher fat mass (onset <15 years β1.604, 95%CI 0.269–2.939; onset ≥15 years β2.590, 95%CI 0.544–4.636; and onset after birth β2.736, 95%CI 0.621–4.851). There was no association between maternal smoking and offspring fat mass. We found that parents’ smoking before conception was associated with higher BMI in offspring when they reached adulthood, but that these effects were mediated through parents’ pack years, suggesting that cumulative smoking exposure during offspring’s childhood may elicit long lasting effects on offspring BMI.

Background

Maternal smoking during pregnancy plays a significant role in increased risk of obesity and metabolic disorders in the offspring [1–4]. Nicotine and other tobacco constituents cross the placenta, and impair foetal growth [5, 6], which together with determinants such as low birthweight and subsequent rapid postnatal weight gain have been associated with risk of adiposity later in life [4]. Several epidemiological studies also report independent effects of paternal smoking (during pregnancy or postnatal life) associated with greater offspring BMI, body fat distribution and increased risk of overweight in children [7–12]. However, obesity is a complex multifactorial condition with a wide range of determinants, which besides environmental factors, also include behavioural and genetic components.

Recent evidence suggests that the germline cells of the parents might have critical exposure-sensitive periods for triggering epigenetic responses that can affect subsequent offspring’s metabolic health and risk of becoming obese [13–15], thus suggesting an epigenetic basis of variation in BMI levels and fat mass. Observations from the Överkalix and ALSPAC cohorts showed that excess food supply and smoking during mid-childhood and pre-pubertal years were associated with metabolic and cardiovascular health, and risk of becoming obese in subsequent generation(s) [16–19]. These findings remain to be successfully replicated, and there exists a possibility of residual confounding due to unmeasured family factors, especially due to the social patterning and inequalities related to smoking behaviour [20, 21]. However, other epidemiological studies have reported adverse offspring outcomes related to paternal exposures in pre-puberty/puberty. Analyses of the RHINESSA, RHINE and ECRHS cohorts found that asthma was more common in offspring with fathers who were obese in puberty [22], as well as in offspring with fathers who smoked in adolescent years [23, 24].

With regard to sex-specific patterns, some studies report no sex differences in offspring BMI in relation to parental smoking [9, 25–27]. Other epidemiological [7, 8, 28] and experimental studies [29–32] indicate more pronounced effects among female offspring. In contrast, the ALSPAC study, reported associations between paternal smoking and increased risk of obesity to be significant only in the sons [16, 19]. Whether sexual dimorphism may be involved in parental transmission of smoking effects on offspring BMI, thus needs further investigation.

The aims of the present study were firstly, to investigate parental smoking onset in specific time windows (onset before 15 years; from age 15 and before conception; after offspring birth) in relation to offspring BMI and, in a subsample, fat mass. Secondly, we aimed to explore whether effects of preconception and early life parental smoking on offspring overweight was modified by sex of the offspring, and mediated by parental pack years of smoking, parental BMI, offspring smoking and, in a subsample, offspring birthweight.

Methods

Study design and population

We investigated onset of parental smoking in relation to adult offspring BMI, using information from two generations. Data concerning the parent population were obtained from the population-based studies Respiratory Health in Northern Europe study (RHINE, www.rhine.nu) and the European Community Respiratory Health Survey (ECRHS, www.ecrhs.org). Information regarding their offspring were collected in the RHINESSA study (www.rhinessa.net). Medical research committees in each study centre approved the study protocols according to national legislation, and each participant gave written informed consent prior to participation (S1 File).

Parent population

The parent sample comprised subjects originating from the ECRHS postal survey in 1990–94. The participants from seven Northern European study centres (Reykjavik in Iceland, Bergen in Norway, Umea, Uppsala and Gothenburg in Sweden, Aarhus in Denmark, and Tartu in Estonia) were followed up in the RHINE questionnaire study, 10 and 20 years after this baseline survey. At each study wave, postal questionnaire information was collected on lifestyle habits, sociocultural factors, and environmental factors such as childhood and adult exposure to tobacco smoke. A sub-sample was invited for clinical investigation and interview in the ECRHS follow-up studies after 10 and 20 years. For parents in two Spanish centres (Albacete and Huelva) and one Australian centre (Melbourne), information from ECRHS was harmonized with the RHINE data. The questionnaire forms used in ECRHS and RHINE can be found at http://www.ecrhs.org/Quests/ECRHSIImainquestionnaire.pdf and http://rhine.nu/pdf/rhine%20Norway.pdf/ http://rhine.nu/pdf/ECRHS%20II%20Norway.pdf.

A flowchart of the study population is provided in Fig 1.

Offspring population

The RHINESSA study (www.rhinessa.net) includes adult offspring (> 18 years) of parents from seven RHINE study centres in Denmark, Iceland, Norway, Sweden and Estonia, and two Spanish (Huelva and Albacete) and one Australian (Melbourne) ECRHS centres. The offspring answered web-based and/or postal questionnaires in 2013–2015, which were harmonized with the RHINE protocols. Sub-samples of offspring who had parents with available clinical information, were invited for clinical investigation and interview, following standardized protocols harmonized with the ECRHS protocols. The questionnaire form used in the RHINESSA can be found at https://helse-bergen.no/seksjon/RHINESSA/Documents/RHINESSA%20Screening%20questionnaires%20adult%20offspring.pdf.

Exposure: Parental smoking

Parental smoking onset was defined from the questions: i. “Are you a smoker?” ii. “Are you an ex-smoker?” iii. “If yes “How old were you when you started smoking?” iiii. “Smoked for … years.” iv. “Stopped smoking in [year]”. Ever-smokers were categorised according to age at smoking initiation (<15 years/≥15 years), and whether smoking started before conception (≥2 years before offspring birth year) or after the offspring was born (≥1 year after offspring birth year). Thus, we constructed a four-level exposure variable with the mutually exclusive categories: never smoked, started smoking before age 15 years, started smoking between age 15 years and conception (preconception), and started smoking after offspring birth (postnatal). Parent-offspring pairs for which parents started smoking during the two-year interval around pregnancy and conception (up to 15 months before conception and up to 1 year after birth of the child) were excluded from the analysis (n = 92).

Outcomes: Offspring body mass index and fat mass index

Body mass index (BMI) was calculated from self-reported height and weight [weight (kg)/height (m)²]. Body composition and fat mass were estimated from bioelectrical impedance analysis measured using Bodystat 1500 MDD (https://www.bodystat.com/medical/). Fat mass index (FMI) was calculated as fat mass (kg)/height (m)².

Potential confounders and mediators

Parental/offspring education was used as a proxy for socioeconomic status and categorised as lower (primary school), intermediate (secondary school) or higher (college or university). Parental pack years pre-conception/ from birth until age 18 years were calculated by multiplying the number of 20-packs of cigarettes smoked per day by the number of years the person had smoked up to ≥2 years before offspring birth year/ up to the offspring’s eighteenth birth year. Parental BMI was calculated from self-reported height and weight at RHINE III. Offspring smoking was defined as ever smoking (current/ex-smokers) or never smoking based on the questions i. “Do you smoke?” ii. “Did you smoke previously?”. Offspring birthweight were obtained from national registry data for a subsample of 813 mother-offspring pairs.

Statistical analysis

Maternal and paternal lines were analysed separately. Generalized linear regressions were used to analyse the associations between parental smoking in specific time windows and offspring BMI (and FMI in a subsample of 240), with adjustment for parental education. Two-dimensional clustering accounted for study centre and family. We set the Heteroscedasticity Consistent Covariance Matrix (HCCM), to version HC1, which made a degree of freedom correction that inflated each residual by the factor $\sqrt{N / (N - K)}$ .

We tested for interactions between offspring sex and parental smoking onset on offspring BMI; the significance level for interaction effects was set to 0.05. We generated regression models and table/figure outputs by use of the ‘jtool’ package [33]. We considered other covariates, such as parental age, offspring education, the other parent’s smoking habits, and BMI (data on the parent who did not participate in RHINE/ECRHS were obtained from the offspring themselves), to be included in the statistical model, as shown in S1 Fig. However, we did not find these factors likely to confound the relationship between parental smoking onset and offspring BMI, and therefore did not include them in the final models.

We constructed mediation models [34, 35] to investigate whether significant associations between parental smoking onset and offspring BMI were influenced by the following mediators: i. parental pack years, ii. parental BMI, iii. offspring smoking (never-smoked / ever smoked), and iv. offspring birthweight (only available for a subsample of offspring). To investigate whether effects differed by gender, we tested for effect modification by offspring sex. We conducted mediation analysis with the R package “Medflex”[36], embedded within the counterfactual framework, as this provided means to infer and interpret direct and indirect effect estimates in a nonlinear setting. Thus, the total effect of an exposure was decomposed into a natural direct effect (the part of the exposure effect not mediated by a given set of potential mediators) and natural indirect effect (the part of the exposure effect mediated by a given set of potential mediators). We followed the imputation-based approach for expanding and imputing the data and fitted a working model for the outcome mean. We fitted separate natural effect models, specified with robust standard errors based on the sandwich estimator. We generated confidence interval plots to visualise the effect estimates and their uncertainty.

We performed all analyses using R version 3.5.2, downloaded at the Comprehensive R Archive Network (CRAN) at http://www.R-project.org/.

Results

Of unique fathers, 10% started smoking before age 15 years, 40% started smoking from age 15 years, and 2% started smoking after offspring birth. In the maternal line, 11% started smoking <15 years, 39% started smoking ≥15 years, and 3% started smoking after offspring birth. Fathers and mothers who started smoking prior to conception had higher current BMI and less education compared to never smoking parents (S1A and S1B Table). In both the paternal (n = 2111) and maternal (n = 2569) lines, daughters had higher education, lower current BMI, and higher FMI, and started smoking earlier compared to sons (Table 1A and 1B). In the maternal line, daughters had lower birthweight. Offspring of smoking parents had higher BMI, more frequently smoked themselves and had smoked more years, compared to offspring of never smoking parents. Sons with fathers who started smoking from age 15 but before conception also had higher FMI than sons with never smoking fathers.

Table 1.

A. Characteristics of 2111 fathers with 2939 sons and daughters. B. Characteristics of 2569 mothers with 3548 sons and daughters.

A
	Sons	Daughters	P-value
	N = 1255 (43)	N = 1684 (57)
Paternal characteristics
Age years, mean ± SD	55.1 ± 6.2	55.0 ± 6.0	p = 0.26
Range	39–65	39–65
BMI kg/m², mean ± SD	26.9 ± 3.8	26.8 ± 3.7	p = 0.32
Range	16.5–53.3	16.8–53.7
Educational level, n (%)
Primary	186 (15)	267 (16)	p = 0.70
Secondary	466 (37)	617 (37)
University/College	588 (47)	792 (47)
Smoking status, n (%)
Never smoked	616 (49)	783 (47)	p = 0.20
Preconception <15smoking onset	126 (10)	179 (11)
Preconception ≥15 smoking onset	482 (38)	696 (41)
Postconception smoking onset	31 (3)	26 (2)
Years smoked, mean ± SD	12.0 ± 15.4	12.4 ± 15.0	p = 0.33
Range	0–59	0–52
Packyears up to offspring age 18, median	17.4	16.7	p = 0.95
25^th%, 75^th%	8.0, 27.2	9.9, 25.0
Packyears preconception years, median	7.0	7.0	p = 0.95
25^th%, 75^th%	3.8, 12.0	4.0, 11.7
Age smoking onset, mean ± SD	17.6 ± 5.5	17.0 ± 4.5	p = 0.10
Range	6–53	7–50
Offspring characteristics
Age years, mean ± SD	29.5 ± 7.4	29.7 ± 7.3	p = 0.53
Range	18–49	18–50
BMI kg/m², mean ± SD	25.1 ± 4.2	23.8 ± 4.8	p < 0.01
Range	15.8–52.5	14.3–67.2
FMI fat mass kg/m², mean ± SD	4.7 ± 2.9	5.9 ± 2.4	p < 0.01
Range	1.1–11.7	2.5–14.4
Educational level, n (%)
Primary	41 (3)	40 (2)	p < 0.01
Secondary	567 (45)	550 (33)
University/College	644 (51)	1089 (65)
Smoking status, n (%)
Never	886 (71)	1174 (70)	p = 0.43
Ever	363 (29)	503 (30)
Years smoked, mean ± SD	9.4 ± 7.0	9.2 ± 7.0	p = 0.79
Range	0–36	0–33
Age smoking onset, mean ± SD	16.9 ± 2.9	16.2 ± 2.7	p < 0.01
Range	9–28	10–30
B
	Sons	Daughters	p-value
	N = 1522 (43)	N = 2026 (57)
Maternal characteristics
Age years, mean ± SD	54.3 ± 6.6	54.1 ± 6.4	p = 0.27
Range	39–65	39–65
BMI kg/m², mean ± SD	25.5 ± 4.3	25.7 ± 4.6	p = 0.19
Range	14.2–49.3	16.8–65.5
Educational level, n (%)
Primary	197 (13)	361 (18)	p < 0.01
Secondary	542 (36)	659 (33)
University/College	773 (51)	999 (49)
Smoking status, n (%)
Never smoked	732 (48)	965 (48)	p = 0.42
Preconception <15smoking onset	154 (10)	232 (12)
Preconception ≥15 smoking onset	594 (39)	780 (39)
Postconception smoking onset	42 (3)	49 (2)
Years smoked, mean ± SD	11.1 ± 14.3	11.2 ± 14.2	p = 0.79
Range	0–52	0–41
Packyears up to offspring age 18, median	11.5	12.5	p = 0.45
25^th%, 75^th%	5.8, 18.8	6.0, 19.2
Packyears preconception years, median	4.2	5.0	p = 0.01
25^th%, 75^th%	2.5, 7.0	3.0, 8.0
Age smoking onset, mean ± SD	17.3 ± 4.3	17.0 ± 4.0	p = 0.22
Range	9–49	7–44
Offspring characteristics
Age years, mean ± SD	31.0 ± 7.8	30.9 ± 7.7	p = 0.49
Range	18–52	18–52
Birthweight kg, mean ± SD	3.5 ± 0.6	3.4 ± 0.6	p < 0.01
Range	1.1–5.3	0.5–5.3
BMI kg/m², mean ± SD	25.3 ± 3.9	23.8 ± 4.4	p < 0.01
Range	12.7–44.7	14.9–49.0
FMI fat mass kg/m², mean ± SD	4.0 ± 1.7	7.3 ± 4.3	p <0.01
Range	1.0–6.6	3. 0–20.5
Educational level, n (%)
Primary	45 (3)	49 (2)	p < 0.01
Secondary	650 (43)	651 (32)
University/College	826 (54)	1321 (65)
Smoking status, n (%)
Never	1023 (67)	1321 (65)	p = 0.32
Ever	493 (32)	699 (35)
Years smoked, mean ± SD	9.4 ± 7.1	9.7 ± 7.2	p = 0.49
Range	0–37	0–35
Age smoking onset, mean ± SD	16.6 ± 3.1	16.0 ± 2.7	p < 0.01
Range	7–32	10–36

Open in a new tab

Test for sign differences between offspring sex; Wilcoxon Mann Whitney test for continuous variables, chi square and Kruskal Wallis test for categorical variables. Missing paternal values: Age: NA = 37; BMI: NA = 34; Educational level: NA = 23; Packyears. NA = 836. Missing offspring values: Age: NA = 7, FMI: NA = 2812, Educational level: NA = 8; Smoking status: NA = 13; Years smoked: NA = 72; Age smoking onset: NA = 29.

Missing maternal values: Age: NA = 80; BMI: NA = 85; Educational level: NA = 17; Packyears: NA 868. Missing offspring values: Age: NA = 10; FMI: NA = 3440, Educational level: NA = 6; Smoking status: NA = 12; Years smoked: NA = 63; Age smoking onset: NA = 25. Birthweight only available in subsample n = 813 (335 males and 478 females)

Fathers’ smoking onset and offspring BMI and FMI

In unadjusted analyses, father’s preconception smoking, both starting before or from age 15 years, was associated with increased offspring BMI (Fig 2). There was no significant interaction between offspring sex and fathers’ smoking onset with regard to offspring BMI (p = 0.395). With adjustment for father’s education and offspring sex, father’s smoking onset ≥15 years was significantly associated with increased BMI in their adult offspring (Table 2 and Fig 2). However, there was no association between postnatal smoking onset and offspring BMI.

Fig 2 — The figure shows crude regressions and regressions adjusted for fathers’ education and offspring sex. After adjustment for fathers’ education, fathers’ smoking onset ≥ 15 remains significantly associated with increased BMI in offspring.

Table 2. Associations between fathers’ smoking onset and offspring (n = 2916) BMI.

Sons’ and daughter’s BMI
Predictors (kg/m²)	Adj. difference in BMI	95% CI	P
Preconception smoking onset < 15 years of age n = 303	0.486	-0.196–1.169	0.162
Preconception smoking onset ≥ 15 years of age n = 1162	0.551	0.174–0.929	0.004^**
Postnatal smoking onset n = 57	0.763	-0.692–2.217	0.304

Open in a new tab

Estimates from generalized linear regression models with adjustment for offspring sex and fathers’ education. Clustered by family id and study centre. P value significance level: *.05,

**.01, ***.001.

When adjusting for fathers’ education, fathers’ smoking onset ≥15 remains significantly associated with increased BMI in offspring.

In the subsample with data on FMI, father’s preconception and postnatal smoking onset were associated with increased offspring FMI (Table 3 and Fig 3). There were significant differences between sons and daughters, and only sons of fathers’ who started to smoke ≥15 years of age (interaction p = 0.014) or after birth (interaction p = 0.020) had significantly higher FMI compared to sons of never smoking fathers. This trend was not seen among daughters, however, analysis indicated that both sons and daughters of fathers who started to smoke before the age of 15 had higher fat mass (Table 3 and Figs 3 and 4).

Table 3. Associations between fathers’ smoking onset and offspring (n = 129) FMI.

Sons’ and daughter’s FMI
Predictors	Adj. difference in FMI (fat mass kg/m²)	95% CI	P	Interaction sex P
Preconception smoking onset < 15 years of age	1.604	0.269–2.939	0.019 ^**	0.982
Preconception smoking onset ≥ 15 years of age^a	2.590	0.544–4.636	0.013 ^**	0.014 ^**
Postnatal smoking onset^b	2.736	0.621–4.851	0.011 ^**	0.020 ^**

Open in a new tab

^amoking onset ≥15: daughters β: -2.797, CI: (-5.023, -0.571)

^b Postnatal smoking onset: daughters β: -3.041, CI: (-5.599, -0.483)

Estimates from generalized linear regression models with offspring sex as interaction term and adjustment for fathers’ education.

Clustered by family id and study centre. P value significance level: *.05,

**.01, ***.001

Fig 3 — The figure shows crude regressions and regressions adjusted for fathers’ education and offspring sex added as an interaction term. In fully adjusted model, fathers’ smoking onset at all time points (<15, ≥ 15 and after birth) are significantly associated with increased FMI in offspring, but there are significant differences between offspring sex, and only sons of fathers who started to smoke ≥15 years of age (interaction p = 0.014) or after birth (interaction p = 0.020) had significantly higher FMI compared to sons of never smoking fathers.

Fig 4 — Interaction plot, depicting how offspring sex modify the associations between fathers’ ≥15 and postnatal smoking onset and offspring’s FMI.

Mothers’ smoking onset and offspring BMI and FMI

Mother’s smoking starting at all time points were associated with increased BMI in her offspring (Table 4 and Fig 5). There were no significant differences between sons and daughters, except that sons of mothers who started to smoke ≥15 years (interaction p = 0.010) had significantly higher BMI compared to sons of never smoking mothers. There was no such trend among daughters. There was no association with mothers’ preconception and postnatal smoking onset and FMI in her offspring (S2 Table).

Table 4. Associations between mothers’ smoking onset and offspring (n = 3531) BMI.

Sons’ and daughter’s BMI
Predictors	β-coef.	95% CI	P	Interaction sex P
Preconception smoking onset < 15 years of age	1.161	0.378–1.944	0.004 ^**	0.338
Preconception smoking onset ≥ 15 years of age^a	0.720	0.293–1.147	0.001 ^**	0.010 ^**
Postnatal smoking onset	2.257	1.220–3.294	<0.001 ^***	0.952

Open in a new tab

^a Smoking onset ≥15: daughters β: -0.717, CI: (-1.264, -0.170)

Estimates from generalized linear regression with offspring sex as interaction term and adjustment for mothers’ education. Clustered by family id and study centre. P value significance level: *.05,

**.01,

***.001

Fig 5 — The figure shows crude and adjusted regressions, with adjustment for mothers’ education and offspring sex added as interaction term. In fully adjusted model, mothers’ smoking onset at all time points (<15, ≥ 15 and after birth) are significantly associated with increased BMI in offspring, but with significant differences between offspring sex. Only sons of mothers who started to smoke ≥15 years (interaction p = 0.010) had significantly higher BMI compared to sons of never smoking mothers.

Mediation analyses of fathers’ smoking onset and offspring BMI

For the association of father’s smoking onset ≥15 years with offspring BMI, we analysed mediation by fathers’ pack years of smoking, fathers’ BMI, and offspring’s smoking (Table 5 and S2 Fig). Mediation analysis by fathers’ pack years up to offspring age 18 revealed indirect but no direct effect, thus suggesting full mediation of the observed association between fathers’ smoking onset ≥ 15 years and offspring BMI by fathers’ pack years. When restricting analysis to pack years in preconception years only, there was no mediation via fathers’ accumulative smoking.

Table 5. Mediation of the observed association between fathers’ ≥15 smoking onset and offspring BMI.

Causal mediation analysis father offspring
Fathers’ smoking onset	Adj diff. BMI (kg/m²)	Std. error	z value	P value
A) Mediation by fathers’ packyears up to offspring age 18
Preconception smoking onset ≥15
Natural direct effect	0.240	0.318	0.756	0.450
Natural indirect effect	0.482	0.239	2.014	0.044 ^*
Total effect	0.722	0.237	3.047	0.002 ^**
Interaction by offspring sex: 0.209
B) Mediation by fathers’ preconception packyears
Preconception smoking onset ≥15
Natural direct effect	0.677	0.235	2.879	0.004 ^**
Natural indirect effect	- 0.092	0.130	- 0.708	0.479
Total effect	0.585	0.205	2.848	0.004 ^**
Interaction by offspring sex: 0.913
C) Mediation by fathers’ BMI
Preconception smoking onset ≥15
Natural direct effect	0.367	0.170	2.159	0.031 ^*
Natural indirect effect	0.214	0.053	4.058	< 0.001 ^***
Total effect	0.582	0.178	3.264	0.001 ^**
Interaction by offspring sex: 0.528
D) Mediation by offspring smoking status
Preconception smoking onset ≥15
Natural direct effect	0.488	0.180	2.711	0.007 ^**
Natural indirect effect	0.080	0.028	2.900	0.004 ^**
Total effect Interaction by offspring sex: 0.134	0.568	0.177	3.215	0.001^**

Open in a new tab

Effect decomposition on the scale of the linear predictor with standard errors based on the sandwich estimator.

Conditional on fathers’ educational level and offspring sex. P value significance level:

*.05,

**.01,

***.001

Mediation by fathers’ BMI confirmed both a direct effect of fathers’ smoking onset ≥15 years and an indirect effect via fathers’ BMI, suggesting partial mediation by fathers’ BMI.

Similarly, there was partial mediation of the association between fathers’ smoking onset ≥ 15 years and offspring obesity by offspring smoking status with both a direct and an indirect effect.

None of the above observed effects were modified by offspring sex.

Mediation analyses of mothers’ smoking onset and offspring BMI

With regard to the maternal line, there were significant associations of mother’s smoking starting <15 years, ≥15 years, and postnatally, thus, we analysed mediation by mothers’ pack years of smoking, mothers’ BMI and offspring smoking for each of these associations.

Similarly to the mediation analyses in the paternal line, mediation analysis by mothers’ pack years up to offspring’s age 18 revealed presence of an indirect but no direct effect, suggesting full mediation of the observed association between mother’s preconception smoking onset both before and from 15 years, and offspring BMI (onset <15 years: β: 1.059, p <0.001; onset ≥15 years β: 0.833, p <0.001; S3 Table and S3 Fig). There was partial mediation of mothers’ pack years up to offspring’s age 18 on mothers’ postnatal smoking onset and offspring BMI where both indirect (β: 0.276, p = 0.001) and direct (β: 1.950, p <0.001) effects were significant and pointed in the same direction. We did not find any direct or indirect effects via mothers’ preconception pack years (S3 Table and S3 Fig).

Mediation by mothers’ BMI confirmed partial mediation with presence of both a direct effect of mothers’ preconception smoking onset before 15 years of age (β: 0.551, p = 0.026) as well as smoking onset after birth (β: 1.869, p <0.001), and an indirect effect via mothers’ BMI (onset <15: β: 0.334, p <0.001; onset after birth: β: 0.320, p = 0.013). There was no evidence of direct or indirect effects via mothers’ BMI in relation to mothers’ preconception smoking onset ≥15 (S4 Table and S4 Fig).

There was indication of partial mediation by offspring’s own smoking status, as both direct effects of smoking onset before 15 years of age (β: 0.841, p = 0.001) and smoking onset after birth (β: 2.090, p <0.001), as well as indirect effects via offspring’s smoking were present (onset <15 years: β: 0.059, p = 0.016; onset ≥15 years β: 0.031, p = 0.019; onset after birth: β: 0.129, p = 0.013, S5 Table and S5 Fig).

In a subsample with birth weight data, there was no evidence of mediation by offspring birthweight as only a direct effect of mothers’ smoking onset <15 years on offspring BMI were present (S6 Table and S6 Fig).

None of the above observed effects were modified by offspring sex.

Discussion

Father’s smoking starting before conception was associated with higher BMI in his adult offspring. Bioimpedance measurements for a subsample also found that sons of smoking fathers, starting both before conception and during postnatal years, had higher fat mass, thus suggesting a consistent effect on sons’ body composition. Mother’s preconception and postnatal smoking onset was also associated with higher adult BMI in her offspring, but these associations were not supported by fat mass analysis in a subsample. Mediation analyses showed that the observed associations between parents’ preconception smoking onset and offspring BMI were fully mediated via parents’ postnatal pack years. Furthermore, parents BMI and offspring’s own smoking status partially mediated the effects of parents’ smoking onset on offspring BMI.

To our knowledge, this is the first study that has shown consistently higher BMI and fat mass levels in offspring of smoking fathers’ where the offspring has reached adulthood. Our results further suggest that fathers’ smoking may have more pronounced effects on their sons’ fat mass when compared to daughters. A potential sex-specific effect on offspring’s body composition supports previous reports of particularly paternal smoking trajectories to impact on sons’ fat mass and risk of becoming obese [16, 19]. However, in contrast to findings in the ALSPAC study, where only fathers’ smoking in mid-childhood and pre-pubertal years was associated with increased BMI and fat mass in the sons [19], our study indicate that father’s preconception smoking starting both before and from age 15 years were associated with increased fat mass in his adult sons. This was also seen in sons where fathers started to smoke after birth. This may reflect the direct toxicogenic effects cigarette smoke exert on biological processes involved in metabolic health. Previous studies have found germ cells and elevated reactive oxygen species (ROS) to mediate metabolic phenotypes in offspring [29, 31, 37, 38]. Smoking has also been shown to induce both ROS overproduction as well as epigenetic changes to germ cells [29, 39], which adds biological plausibility of paternal smoking to be drivers of complex offspring phenotypes. Although increased adipose tissue does not necessarily translate into metabolic abnormalities, both BMI and FMI are regarded important determinants of metabolic health at the population level [40, 41], and childhood adiposity has been reported to be associated with increased risk of adult type 2 diabetes mellitus [42]. In a recent epigenome-wide association study, we found that adult offspring with smoking fathers had differential methylation in regions related to innate immune system pathways and fatty acid bio-synthesis [43]. These are inflammatory signalling pathways and metabolic signals that have been linked to obesity [44]. However, whether the observed associations between increased BMI and FMI among offspring of smoking fathers relate to metabolic phenotypes needs further investigation. Our study also indicated that parental smoking exposures transmit through the maternal line, as also mothers’ pre- and postnatal smoking onset was related to higher BMI in her adult sons and daughters. However, offspring of smoking mothers did not have a higher fat mass. This may suggest that maternal and paternal smoking trajectories influence their offspring body composition and risk of obesity through different biological mechanisms and pathways.

Through independent mediation analyses, we sought to investigate how parental smoking onset may influence offspring BMI. By including parental pack years as a potential mediator, we aimed to disentangle the effect of parents’ smoking onset, and specifically smoking onset before conception, from an accumulative and sustained smoking exposure during peri-and post- natal life. Our findings show that parents’ smoking onset influence their offspring BMI via pack years smoked during childhood years, up to the offspring’s age 18. This may very well reflect the importance of families’ shared environment and the impact lifestyle-related factors, such as dietary habits and physical activity, exert on BMI levels and risk of obesity [45, 46]. This may also explain why both fathers’ preconception as well as postnatal smoking onset was associated with increased fat mass in their sons, and why we did not find preconception pack years to mediate the association between parents’ smoking onset and offspring BMI.

Furthermore, we found that parents’ BMI, partially mediated the effect of pre- and postnatal smoking onset on offspring BMI. Although this may indicate a genetic contribution in body composition, we also found that offspring’s smoking status partially mediated the effect of parents smoking onset on their adult BMI, where offspring who had or were smoking themselves, tended to have higher BMI in adulthood compared to offspring who had never smoked. As such, our results may reflect the influence of multiple pathways and the complex interplay between genetics, biology, behaviour, and environment, potentially involved in the aetiology of obesity [47, 48]. These multifactorial aspects may also explain why our results contrast from previous studies related to offspring asthma outcomes in the RHINESSA, RHINE and ECRHS cohorts, where the fathers’ pubertal and adolescent years specifically have been shown to constitute an important time window for transmission of paternal lineage exposures [22–24].

Low birthweight due to growth restriction during pregnancy is one factor that has been thought to be on the causal pathway between maternal smoking and offspring’s risk of obesity in later life [4]. We found no evidence that the association between mothers’ smoking onset and offspring BMI was mediated via her sons’ or daughters’ birthweight. However, the present study was not able to distinguish true growth retarded newborns from those being born small due to genetic factors, thus a potential causal role of birthweight on overweight in subsequent years warrants further investigation.

A strength of the present study was that the study population originated from two linked inter-generational study cohorts that enables long-term investigation of exposures, across generations and in adult offspring. Further, we used multinational data following standardized protocols. The study also had clear limitations. The main outcome, offspring BMI, was based on self-reported height and weight which can possibly add bias to our estimates. However, we would expect this potential bias to be non-differential, since offspring of smoking and never smoking parents assessed their height and weight in the same manner. There is no reason to believe that offspring of smoking parents would report height and weight any differently than offspring of non-smoking parents. Moreover, studies assessing the validity of self-reported measurements of anthropometric characteristics, have showed that the correlation between self-reported and technician-measured BMI is high (0.92) [49]. Although BMI does not distinguish between lean and fat mass, it is commonly used to determine overweight in clinical research settings as it is closely related with body fat [50, 51]. In addition, we verified our findings in a sub-sample of sons and daughters with clinical data on fat mass. However, this sub-sample was of limited size, and we did not have sufficient statistical power to conduct mediation analyses of the observed associations between fathers’ smoking onset and offspring fat mass. With regard to smoking exposure, we had information only on the participating parent, and have thus not been able to account for a potential smoking exposure arising from the other parent in the household. Neither do we have detailed information about where the parents smoked (inside house/outside house/other places), thus we have not been able to address levels of cigarette smoke the offspring would have been exposed to. Furthermore, we excluded parent-offspring pairs with missing information on parental smoking (n = 1477), which consequently reduced our sample size. Some of the parental smoking onset categories were also limited in numbers, which potentially could influence the reliability of our results. A multitude of exposures and difference in genetic background exists in population studies, and as the offspring in the present study have reached adulthood, they have been exposed to a variety of environmental factors. However, to be regarded as potential confounders, they would per definition precede both the exposure (parental smoking onset in adolescent and early adult years) and outcome (adult offspring BMI) in time. Thus, this does rule out many factors that traditionally would be included in models assessing associations with BMI in adults. We investigated whether parents’ adult BMI mediated the effect of parental smoking onset on offspring BMI. However, we did not have information on parents BMI in childhood and pre-adolescent years, which potentially can be of importance and a potential confounder as this would precede both the exposure and outcome in time. Moreover, we did not have information regarding adoption in the offspring population, and whether the participating parent was the biological parent. Thus, unmeasured factors may have impacted on our findings. We chose to use a mediation analysis embedded within the counterfactual framework due to its flexibility in handling non- linear parametric models. However, we have not been able to assess the robustness of our findings and investigated whether there are violations to the identification assumptions, especially with regard to all potential variables being independent and accounted for. This should be further investigated.

Conclusion

In this multicentre population-based study of two generations, we found that fathers beginning to smoke before conception was associated with higher BMI in their adult sons and daughters, and that father’s smoking starting in any time window was associated with higher FMI in adult sons. In contrast, mothers’ pre-as well as postnatal smoking onset was associated with higher offspring adult BMI, but not higher fat mass. Independent mediation analysis indicated that parents’ pack years up to offspring’s age 18, but not preconception pack years, fully mediated these effects. This may suggest that an accumulative smoking exposure during offspring’s childhood may be needed in order to elicit long lasting effects on offspring BMI and risk of becoming obese. In addition, we found partial mediation by parents’ BMI and offspring own smoking status, which may further reflect the importance of families’ shared environment and the impact lifestyle-related factors, such as dietary habits and physical activity, exert on BMI levels and risk of obesity. As such, our results support the multifactorial aspects contributing to obesity.

Supporting information

S1 Fig. Directed Acyclic Graph (DAG).

The figure presents covariates considered to be included in the statistical model.

(TIF)

Click here for additional data file.^{(602.2KB, tif)}

S2 Fig. Visualising mediations of the association with fathers’ ≥15 smoking onset on offspring BMI.

Analyses reveal full mediation by fathers’ pack years and partial mediation by fathers’ BMI and offspring’s own smoking status. There is no mediation via fathers’ preconception accumulative smoking.

(TIF)

Click here for additional data file.^{(1.2MB, tif)}

S3 Fig. Visualising mothers’ pack years as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(1.4MB, tif)}

S4 Fig. Visualising mothers’ BMI as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(800.1KB, tif)}

S5 Fig. Visualising offspring’s smoking habits as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(809.7KB, tif)}

S6 Fig. Visualising offspring’s birthweight as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(809KB, tif)}

S1 Table

A. Descriptive table of father offspring cohort grouped by fathers’ smoking onset and stratified by offspring sex. B. Descriptive table of mother offspring cohort grouped by mothers’ smoking onset and stratified by offspring sex. Parents who started smoking prior to conception have higher current BMI and less education compared to never smoking parents. Offspring of smoking parents have higher BMI, more frequently smoke themselves and have smoked more years, compared to offspring of never smoking parents. Sons with fathers who started smoking from age 15 but before conception also have higher FMI than sons with never smoking fathers.

(PDF)

Click here for additional data file.^{(195.4KB, pdf)}

S2 Table. Associations between mothers’ smoking onset and offspring (n = 111) FMI.

The figure shows regression model adjusted for mothers’ education and offspring sex and reveals no association with mothers’ preconception and postnatal smoking onset and FMI in her offspring.

(PDF)

Click here for additional data file.^{(83.2KB, pdf)}

S3 Table. Mothers’ pack years as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

The association between mothers’ preconception smoking onset and offspring BMI is fully mediated by mothers’ postnatal pack years, whereas mothers’ postnatal smoking onset and offspring BMI is partially mediated by mothers’ postnatal packyears. There is no evidence of direct or indirect effects via mothers’ preconception accumulative smoking in relation to mothers’ smoking onset.

(PDF)

Click here for additional data file.^{(103.6KB, pdf)}

S4 Table. Mothers’ BMI as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

The association between mothers’ preconception smoking onset before 15 years of age as well as smoking onset after birth and offspring BMI is partially mediated by mothers’ BMI. There is no evidence of direct or indirect effects via mothers’ BMI in relation to mothers’ preconception smoking onset ≥15.

(PDF)

Click here for additional data file.^{(102.4KB, pdf)}

S5 Table. Offspring’s smoking habits as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

The association between mothers’ preconception smoking onset before 15 years of age as well as smoking onset after birth and offspring BMI is partially mediated by offspring’s own smoking status.

(PDF)

Click here for additional data file.^{(102.5KB, pdf)}

S6 Table. Offspring’s birthweight as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

In a subsample with birth weight data, there is no evidence of mediation by offspring birthweight.

(PDF)

Click here for additional data file.^{(102.4KB, pdf)}

S1 File. Table of ethic committee name and approval number for each study center.

(PDF)

Click here for additional data file.^{(66.6KB, pdf)}

Data Availability

Due to Norwegian ethical and legal restrictions the data underlying this study are available upon request to qualified researchers. Requests for data access can be directed to Haukeland University Hospital, 5021 Bergen, Norway. Att. Head of Department. Dept. of Occupational Medicine, Marit Grønning; email: postmottak@helse-bergen.no; phone: +47 55975000. Org, nr. 983 974 724.

Funding Statement

Co-ordination of the RHINESSA study has received funding from the Research Council of Norway (Grants No. 274767, 214123, 228174, 230827 and 273838), ERC StG project BRuSH #804199, the European Union's Horizon 2020 research and innovation program under grant agreement No. 633212 (the ALEC Study WP2), the Bergen Medical Research Foundation, and the Western Norwegian Regional Health Authorities (Grants No. 912011, 911892 and 911631). Study centres have further received local funding from the following: Bergen: the above grants for study establishment and co-ordination, and, in addition, World University Network (RDF and Sustainability grant), Norwegian Labour Inspection, and the Norwegian Asthma and Allergy Association. Albacete and Huelva: SEPAR. Fondo de Investigación Sanitaria (FIS PS09). Gøteborg, Umeå and Uppsala: the Swedish Lung Foundation, the Swedish Asthma and Allergy Association. Reykjavik: Iceland University. Melbourne: NHMRC, Melbourne University, Tartu: the Estonian Research Council (Grant No. PUT562). Århus: The Danish Wood Foundation (Grant No. 444508795), the Danish Working Environment Authority (Grant No. 20150067134). The RHINE study received funding by Norwegian Research Council, Norwegian Asthma and Allergy Association, Danish Lung Association, Swedish Heart and Lung Foundation, Vårdal Foundation for Health Care Science and Allergy Research, Swedish Asthma and Allergy Association, Swedish Lung Foundation, Icelandic Research Council, and Estonian Science Foundation. The co-ordination of ECRHS was supported by European Union's Horizon 2020 research and innovation program under grant agreement No. 633212 (the ALEC Study), the European Commission frameworks 5 and 7 (ECRHS I and II) and the Medical Research Council (ECRHS III).

References

1.Riedel C, Schonberger K, Yang S, et al. Parental smoking and childhood obesity: higher effect estimates for maternal smoking in pregnancy compared with paternal smoking—a meta-analysis. International journal of epidemiology. 2014;43(5):1593–606. Epub 2014/08/01. 10.1093/ije/dyu150 . [DOI] [PubMed] [Google Scholar]
2.Ino T. Maternal smoking during pregnancy and offspring obesity: meta-analysis. Pediatrics international: official journal of the Japan Pediatric Society. 2010;52(1):94–9. Epub 2009/04/30. 10.1111/j.1442-200X.2009.02883.x . [DOI] [PubMed] [Google Scholar]
3.Ino T, Shibuya T, Saito K, et al. Relationship between body mass index of offspring and maternal smoking during pregnancy. International journal of obesity (2005). 2012;36(4):554–8. Epub 2011/12/21. 10.1038/ijo.2011.255 . [DOI] [PubMed] [Google Scholar]
4.Oken E, Levitan EB, Gillman MW. Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis. International journal of obesity (2005). 2008;32(2):201–10. Epub 2008/02/19. 10.1038/sj.ijo.0803760 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Lisboa PC, de Oliveira E, de Moura EG. Obesity and endocrine dysfunction programmed by maternal smoking in pregnancy and lactation. Frontiers in physiology. 2012;3:437 Epub 2012/11/28. 10.3389/fphys.2012.00437 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Schneider S, Huy C, Schutz J, et al. Smoking cessation during pregnancy: a systematic literature review. Drug and alcohol review. 2010;29(1):81–90. Epub 2010/01/19. 10.1111/j.1465-3362.2009.00098.x . [DOI] [PubMed] [Google Scholar]
7.Durmus B, Heppe DH, Taal HR, et al. Parental smoking during pregnancy and total and abdominal fat distribution in school-age children: the Generation R Study. International journal of obesity (2005). 2014;38(7):966–72. Epub 2014/01/23. 10.1038/ijo.2014.9 . [DOI] [PubMed] [Google Scholar]
8.Leary SD, Smith GD, Rogers IS, et al. Smoking during pregnancy and offspring fat and lean mass in childhood. Obesity (Silver Spring, Md). 2006;14(12):2284–93. Epub 2006/12/26. 10.1038/oby.2006.268 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Kwok MK, Schooling CM, Lam TH, et al. Paternal smoking and childhood overweight: evidence from the Hong Kong "Children of 1997". Pediatrics. 2010;126(1):e46–56. Epub 2010/07/01. 10.1542/peds.2009-2642 . [DOI] [PubMed] [Google Scholar]
10.Hui LL, Nelson EA, Yu LM, et al. Risk factors for childhood overweight in 6- to 7-y-old Hong Kong children. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity. 2003;27(11):1411–8. Epub 2003/10/24. 10.1038/sj.ijo.0802423 . [DOI] [PubMed] [Google Scholar]
11.von Kries R, Bolte G, Baghi L, et al. Parental smoking and childhood obesity—is maternal smoking in pregnancy the critical exposure? International journal of epidemiology. 2008;37(1):210–6. Epub 2007/12/07. 10.1093/ije/dym239 . [DOI] [PubMed] [Google Scholar]
12.Harris HR, Willett WC, Michels KB. Parental smoking during pregnancy and risk of overweight and obesity in the daughter. International journal of obesity (2005). 2013;37(10):1356–63. Epub 2013/06/06. 10.1038/ijo.2013.101 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Panzeri I, Pospisilik JA. Epigenetic control of variation and stochasticity in metabolic disease. Molecular metabolism. 2018. Epub 2018/06/18. 10.1016/j.molmet.2018.05.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Soubry A. POHaD: why we should study future fathers. Environmental epigenetics. 2018;4(2):dvy007 Epub 2018/05/08. 10.1093/eep/dvy007 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Sales VM, Ferguson-Smith AC, Patti M-E. Epigenetic Mechanisms of Transmission of Metabolic Disease across Generations. Cell Metabolism. 2017;25(3):559–71. 10.1016/j.cmet.2017.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Pembrey ME, Bygren LO, Kaati G, et al. Sex-specific, male-line transgenerational responses in humans. European journal of human genetics: EJHG. 2006;14(2):159–66. Epub 2006/01/05. 10.1038/sj.ejhg.5201538 . [DOI] [PubMed] [Google Scholar]
17.Bygren LO, Kaati G, Edvinsson S. Longevity determined by paternal ancestors' nutrition during their slow growth period. Acta biotheoretica. 2001;49(1):53–9. Epub 2001/05/23. 10.1023/a:1010241825519 . [DOI] [PubMed] [Google Scholar]
18.Kaati G, Bygren LO, Edvinsson S. Cardiovascular and diabetes mortality determined by nutrition during parents' and grandparents' slow growth period. European journal of human genetics: EJHG. 2002;10(11):682–8. Epub 2002/10/31. 10.1038/sj.ejhg.5200859 . [DOI] [PubMed] [Google Scholar]
19.Northstone K, Golding J, Davey Smith G, et al. Prepubertal start of father's smoking and increased body fat in his sons: further characterisation of paternal transgenerational responses. European journal of human genetics: EJHG. 2014;22(12):1382–6. Epub 2014/04/03. 10.1038/ejhg.2014.31 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Carslake D, Pinger PR, Romundstad P, et al. Early-Onset Paternal Smoking and Offspring Adiposity: Further Investigation of a Potential Intergenerational Effect Using the HUNT Study. PloS one. 2016;11(12):e0166952 Epub 2016/12/03. 10.1371/journal.pone.0166952 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Disney G, Gurrin L, Aitken Z, et al. Hierarchical Models for International Comparisons: Smoking, Disability, and Social Inequality in 21 European Countries. Epidemiology (Cambridge, Mass). 2020;31(2):282–9. Epub 2019/12/24. 10.1097/ede.0000000000001154 . [DOI] [PubMed] [Google Scholar]
22.Johannessen A, Lonnebotn M, Calciano L, et al. Being overweight in childhood, puberty, or early adulthood: Changing asthma risk in the next generation? The Journal of allergy and clinical immunology. 2020;145(3):791-9.e4. Epub 2019/09/11. 10.1016/j.jaci.2019.08.030 . [DOI] [PubMed] [Google Scholar]
23.Svanes C, Koplin J, Skulstad SM, et al. Father's environment before conception and asthma risk in his children: a multi-generation analysis of the Respiratory Health In Northern Europe study. International journal of epidemiology. 2017;46(1):235–45. Epub 2016/08/28. 10.1093/ije/dyw151 . [DOI] [PubMed] [Google Scholar]
24.Accordini S, Calciano L, Johannessen A, et al. A three-generation study on the association of tobacco smoking with asthma. International journal of epidemiology. 2018;47(4):1106–17. Epub 2018/03/14. 10.1093/ije/dyy031 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Yang S, Decker A, Kramer MS. Exposure to parental smoking and child growth and development: a cohort study. BMC pediatrics. 2013;13:104 Epub 2013/07/12. 10.1186/1471-2431-13-104 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Toschke AM, Koletzko B, Slikker W, et al. Childhood obesity is associated with maternal smoking in pregnancy. European journal of pediatrics. 2002;161(8):445–8. Epub 2002/08/13. 10.1007/s00431-002-0983-z . [DOI] [PubMed] [Google Scholar]
27.Toschke AM, Montgomery SM, Pfeiffer U, et al. Early intrauterine exposure to tobacco-inhaled products and obesity. American journal of epidemiology. 2003;158(11):1068–74. Epub 2003/11/25. 10.1093/aje/kwg258 . [DOI] [PubMed] [Google Scholar]
28.Vik T, Jacobsen G, Vatten L, et al. Pre- and post-natal growth in children of women who smoked in pregnancy. Early human development. 1996;45(3):245–55. Epub 1996/07/19. 10.1016/0378-3782(96)01735-5 . [DOI] [PubMed] [Google Scholar]
29.Lane M, McPherson NO, Fullston T, et al. Oxidative stress in mouse sperm impairs embryo development, fetal growth and alters adiposity and glucose regulation in female offspring. PLoS One. 2014;9(7):e100832 Epub 2014/07/10. 10.1371/journal.pone.0100832 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Fullston T, Ohlsson Teague EM, Palmer NO, et al. Paternal obesity initiates metabolic disturbances in two generations of mice with incomplete penetrance to the F2 generation and alters the transcriptional profile of testis and sperm microRNA content. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2013;27(10):4226–43. Epub 2013/07/13. 10.1096/fj.12-224048 . [DOI] [PubMed] [Google Scholar]
31.Ng SF, Lin RC, Laybutt DR, et al. Chronic high-fat diet in fathers programs beta-cell dysfunction in female rat offspring. Nature. 2010;467(7318):963–6. Epub 2010/10/22. 10.1038/nature09491 . [DOI] [PubMed] [Google Scholar]
32.Kwong WY, Wild AE, Roberts P, et al. Maternal undernutrition during the preimplantation period of rat development causes blastocyst abnormalities and programming of postnatal hypertension. Development (Cambridge, England). 2000;127(19):4195–202. Epub 2000/09/08. . [DOI] [PubMed] [Google Scholar]
33.Long JA. jtools: Analysis and Presentation of Social Scientific Data. 2019.
34.MacKinnon DP, Luecken LJ. How and for whom? Mediation and moderation in health psychology. Health Psychol. 2008;27(2s):S99–s100. Epub 2008/05/03. 10.1037/0278-6133.27.2(Suppl.).S99 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Cohen J CP, West SG, Aiken LA. Applied multiple regression/correlation analysis for the behavioral sciences. 3. Hillsdale N, editor 2003. [Google Scholar]
36.Johan Steen TL, Beatrijs Moerkerke, Stijn Vansteelandt medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models. Journal of Statistical Software. 2017;76(11). doi: 10.18637. [Google Scholar]
37.Huypens P, Sass S, Wu M, et al. Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Nature genetics. 2016;48(5):497–9. Epub 2016/03/15. 10.1038/ng.3527 . [DOI] [PubMed] [Google Scholar]
38.Ng SF, Lin RC, Maloney CA, et al. Paternal high-fat diet consumption induces common changes in the transcriptomes of retroperitoneal adipose and pancreatic islet tissues in female rat offspring. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2014;28(4):1830–41. Epub 2014/01/15. 10.1096/fj.13-244046 . [DOI] [PubMed] [Google Scholar]
39.Marczylo EL, Amoako AA, Konje JC, et al. Smoking induces differential miRNA expression in human spermatozoa: a potential transgenerational epigenetic concern? Epigenetics. 2012;7(5):432–9. Epub 2012/03/24. 10.4161/epi.19794 . [DOI] [PubMed] [Google Scholar]
40.Goossens GH. The Metabolic Phenotype in Obesity: Fat Mass, Body Fat Distribution, and Adipose Tissue Function. Obes Facts. 2017;10(3):207–15. Epub 2017/06/01. 10.1159/000471488 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Peltz G, Aguirre MT, Sanderson M, et al. The role of fat mass index in determining obesity. Am J Hum Biol. 2010;22(5):639–47. Epub 2010/08/26. 10.1002/ajhb.21056 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Nianogo RA, Arah OA. Investigating the Role of Childhood Adiposity in the Development of Adult Type 2 Diabetes in a 64-year Follow-up Cohort: An Application of the Parametric G-formula Within an Agent-based Simulation Study. Epidemiology (Cambridge, Mass). 2019;30 Suppl 2:S101–s9. Epub 2019/10/01. 10.1097/ede.0000000000001062 . [DOI] [PubMed] [Google Scholar]
43.Morkve Knudsen GT, Rezwan FI, Johannessen A, et al. Epigenome-wide association of father's smoking with offspring DNA methylation: a hypothesis-generating study. Environmental epigenetics. 2019;5(4):dvz023 Epub 2019/12/13. 10.1093/eep/dvz023 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Ringseis R, Eder K, Mooren FC, et al. Metabolic signals and innate immune activation in obesity and exercise. Exerc Immunol Rev. 2015;21:58–68. Epub 2015/04/01. . [PubMed] [Google Scholar]
45.Ambrosini GL. Childhood dietary patterns and later obesity: a review of the evidence. Proc Nutr Soc. 2014;73(1):137–46. Epub 2013/11/28. 10.1017/S0029665113003765 . [DOI] [PubMed] [Google Scholar]
46.Brug J, van Stralen MM, Te Velde SJ, et al. Differences in weight status and energy-balance related behaviors among schoolchildren across Europe: the ENERGY-project. PloS one. 2012;7(4):e34742 Epub 2012/05/05. 10.1371/journal.pone.0034742 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism: clinical and experimental. 2019;92:6–10. Epub 2018/09/27. 10.1016/j.metabol.2018.09.005 . [DOI] [PubMed] [Google Scholar]
48.Han TS, Hart CL, Haig C, et al. Contributions of maternal and paternal adiposity and smoking to adult offspring adiposity and cardiovascular risk: the Midspan Family Study. BMJ open. 2015;5(11):e007682 10.1136/bmjopen-2015-007682 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Tehard B, van Liere MJ, Com Nougue C, et al. Anthropometric measurements and body silhouette of women: validity and perception. J Am Diet Assoc. 2002;102(12):1779–84. Epub 2002/12/19. 10.1016/s0002-8223(02)90381-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i–xii, 1–253. Epub 2001/03/10. . [PubMed] [Google Scholar]
51.Stein CJ, Colditz GA. The epidemic of obesity. The Journal of clinical endocrinology and metabolism. 2004;89(6):2522–5. Epub 2004/06/08. 10.1210/jc.2004-0288 . [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0235632.r001

Decision Letter 0

Seana Gall

11 May 2020

PONE-D-20-07665

Parents’ smoking onset before conception as related to body mass index and fat mass in adult offspring: Findings from the RHINESSA generation study

PLOS ONE

Dear Mrs Knudsen,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please revise your manuscript according to the reviewers comments.

We would appreciate receiving your revised manuscript by Jun 25 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Seana Gall

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://www.plosone.org/attachments/PLOSOne_formatting_sample_main_body.pdf and http://www.plosone.org/attachments/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

4. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 1 in your text; if accepted, production will need this reference to link the reader to the Table.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Ref: PONE-D-20-07665

Comments to Authors:

Congratulations on this interesting manuscript.

The comments below constitute my review, and a number of points will require addressing.

By way of overall summary: the major revision I suggest is the acknowledgement of the myriad cardiovascular and endocrine risk factors that are all confounders to the outcomes reported here, and as such must be acknowledged as not adjusted for in this work.

Comprehensive review:

In the manuscript titled ‘Parents’ smoking onset before conception as related to body mass index and fat mass in adult offspring: Findings from the RHINESSA generation study’ Knudsen et al report interesting findings related to the largely pre-conception exposure to tobacco smoking in mothers and fathers and subsequent health consequences in offspring through to adulthood. This study comes from analysing three studies, the RHINE and the ECRHS, which provided data on the parents of the cohort, and the RHINESSA project, which provided data on the offspring of this cohort. The RHINESSA study typically publishes data related to respiratory conditions such as asthma and atopic disease. This cohort includes participants from across multiple centres in Europe and also Melbourne, Australia, and aims to follow up multiple generations of participants to investigate genetics and epigenetic influences on disease – as has occurred in this study.

The number of authors listed for such a manuscript is appropriate.

The financial disclosures appear to be thorough and cover the project included in the manuscript.

The ethics statement is complete and appears to cover all research sites.

The authors have responded to the data availability question with ‘no – some restrictions will apply’.

Detailed review:

Background:

The main point of this background is the suggestion that there is evidence that the germline cells of the parents might have critical exposure-sensitive periods for triggering epigenetic responses that will influence the risk of offspring becoming obese later in life. The remainder of the background is not directly of relevance to the manuscript but is interesting. It could be better placed in a broader discussion section as opposed to the background.

The aims of the study are well presented, and ambitious. The overall aim is to investigate how timing of parental smoking onset (divided into 3 groups of before age 15 years, after age 15 years and after birth) may impact offspring BMI and in a small sample, impact fat mass. Secondary aims are to investigate the sex differences in this findings and to investigate the confounders of parental pack years, parental BMI and offspring smoking.

Methods:

Parents: ECRHS survey for baseline data then followed up in RHINE study 10-20 years after. All had postal survey, some had clinics.

Figure 1 is necessary and useful, but can be improved with more details provided about study centres and dates of the original surveys. Currently all we learn from the figure is sample numbers, when it is easy to make this figure more appealing to include the locations of these participants and the dates of the original studies and the form of the studies (postal questionnaires, clinics etc).

Offspring: Appropriately described

Exposure: 4 level exposure created. Parents who started smoking in the 2 year interval around pregnancy were excluded, as the exposure seems only to be defined by years, and not by dates exactly. This is likely due to the original surveys only allowing answers by year.

Outcomes: BMI from self-report. Less than ideal. Body fat mass from bioelectrical impedance analysis and FMI from that measurement/height^2. I am unsure how accurate such techniques are and as such, how valid this approach is.

Confounders: The variables considered were parental socioeconomic status (via parental education), parental pack years, parental BMI (self report), offspring smoking (ever vs never only) and offspring birthweight.

This is a small number of confounders. These are all indeed vital confounders to consider, but there are myriad other critical confounders that could be influencing the outcomes from across the huge list of cardiovascular and endocrine risk factors both prior to conception and post-natally. We also know that the amount of smoking in close confinement to the offspring post birth is of importance, as studies with serum cotinine samples of tobacco exposure prove that although some parents may be smoking a great deal (as per pack years), if they do so outside or otherwise away from the child, they can mitigate possible exposures to their offspring.

In reality a number of other confounders not listed in the methods were also included in results: parental age, offspring education, other parents smoking habits, and other parents BMI (other parents information obtained from the offspring…), however these were not include din final models.

Statistical analysis:

Simple statistical approaches were employed appropriately.

The mediation models were created for all the key confounders listed in the methods. This was performed in an appropriate way utilising well known R packages, which I personally have not utilised.

Results:

Table 1A and 1B and S1A and B: significant missing values for age smoking onset and packyears noted

Tables would be clearer with stepped rows under umbrella terms. For example: ‘educational level, n (%)’ should be far left of the row, then the educational level options should be indented slightly so the reader can easily see the nested options.

Figure 2 shows that following adjustment (for father education and offspring sex) only fathers smoking after age 15 was associated with increased offspring BMI, but starting smoking before the age of 15 or after birth of offspring was not statistically associated. The strength of the association is modest and is demonstrated in Table 2. The use of both a table and figure for this result is perhaps unnecessary.

Table 3 and Fig 3 show the results for Father smoking and offspring FMI. Figure 4 provides interesting sex differences according to fathers smoking onset. There are very wide confidences on most of these data.

Maternal smoking onset and offspring BMI and FMI is presented in the same manner as paternal. Aternal smoking appears to be worse for the offspring in regards to the outcomes presented here.

The mediation analyses of father and mother smoking onset and offspring BMI is of interest and is well presented.

FigS2, along with all mediation plots, should be improved to capitalise the first letters of the Y-axis labels. Again, confidences are large in these data.

The authors should define what exactly the natural direct and indirect effects mean, so that those not used to these analysis can understand better and learn.

Discussion and conclusion:

The descriptive results are interesting, and may well be novel, however we must ask if they of fundamental importance. We know that parents smoking and being of high BMI is a huge risk factor for their children subsequently smoking and being of high BMI, this is well established. Children being of high BMI is a huge risk factor for being of high BMI in adulthood. So demonstrating that a fathers smoking influenced the BMI of their offspring in adulthood does seem somewhat intuitive. Despite this, it is of course important to prove such associations as the authors have done here.

See papers here that may remove some novelty from the findings in this manuscript. There appear to be others also and these should be discussed in the text, even if briefly.

http://dx.doi.org/10.1136/bmjopen-2015-007682

https://dx.doi.org/10.1038%2Fijo.2013.101

The discussion re possible biological mechanisms, focused on epigenetics, of the effects is sound.

The discussion of limitations is good, and highlights many of the issues. The major issue with this paper is the limited adjustment for confounding mediated through variables not considered in the analysis. The mediation of association analysis is interesting and of reportable note, but the primary findings remain largely unadjusted for confounders that are usually considered in the primary analysis. Some measures of risk factors usually included in models as utilised here include variables for physical (in)activity, nutritional status, alcohol consumption, sleep, stress, environmental factors such urban vs regional vs rural etc. A great deal of power is placed on the single measure of socio-economic status (parental education and offspring education) which is not ideal.

Despite this, the manuscript reports interesting findings in relation to the effect of parents pack-years on offspring BMI and FMI, however the headline findings that - fathers beginning to smoke before conception was associated with higher BMI in their adult sons and daughters, and that father’s smoking starting in any time window was associated with higher FMI in adult sons. And that mothers’ pre-as well as postnatal smoking onset was associated with higher offspring adult BMI, but not higher fat mass – are results relying on largely unadjusted models which suggest profound epigenetic influence across the life course. This must be presented and discussed with caution.

The authors appropriately mention the multi-factorial aspects of risk contributing to obesity in the conclusion and I think this is of vital importance.

Reviewer #2: Results

Lines 217-219, check the percentages and they were not adding up to 100%

Lines 250-251. It is not clear if non-significant associations for postnatal smoking or smoking onset <15 were due to reduced sample size in Table 2. They were both positive. Suggest adding N in each line of Table 2.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Henry West

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Jul 6;15(7):e0235632. doi: 10.1371/journal.pone.0235632.r002

Author response to Decision Letter 0

29 May 2020

Response to Reviewers

First, we would like to thank the academic editor and reviewers for their valuable and interesting comments to improve the paper. Please find below our point-by-point response to the comments.

The changes from the original version are referred to by page- and line numbers in the revised version of the manuscript (without track changes).

In-house editor after resubmitting revised manuscript

Comment 1: Your manuscript files have been checked in-house but before we can proceed we need you to address the following issues: Thank you for including your ethics statement on the online submission form. To help ensure that the wording of your manuscript is suitable for publication, would you please also add this statement at the beginning of the Methods section of your manuscript file.

Response 1: Thank you for help ensuring that the wording in the manuscript is suitable for publication. In the revised version of the manuscript, we have added this statement in the first paragraph of the method section (lines 123-125) , and ethic committee name and approval number for each study center have been provided as supportive information in table S1 Resource ethics (line 125).

Comment 2: We also note the following comments in your cover letter: “Due to Norwegian ethical and legal restrictions the data underlying this study are available upon request to qualified researchers. Requests for data access can be directed to Haukeland University Hospital, 5021 Bergen, Norway. Att. RHINESSA PI Cecilie Svanes; email: postmottak@helse-bergen.no; phone: +47 55975000. Org, nr. 983 974 724.”

However, we see Dr. Cecilie Svanes is listed as an author of this study. To ensure your submission adheres to the PLOS ONE policy on acceptable data access restrictions, please provide non-author contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. Note that it is not acceptable for an author to be the sole named individual responsible for ensuring data access.

Response 2: Thank you for drawing our attention to this inconsistency. In the revised cover letter, non-author contact information for data access has been provided.

Academic editor

Comment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response 1: Thank you for drawing our attention to any inconsistency regarding style requirements. We have urged to adhere to PLOS ONE’s requirements and templates in the revised version of the manuscript.

Comment 2: Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

Response 2: Thank you for bringing this to our attention. The questionnaires used in the ECRHS, RHINE and RHINESSA studies are not restricted by copyright more restrictive than CC-BY, and all questionnaire forms can be found and downloaded at the study cohorts web sites. This information has now been added in the method section in the revised manuscript, with URL provided so that all readers can easily access the questionnaires.

Comment 3: We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly.

Response 3: Due to Norwegian ethical and legal restrictions the data underlying this study are available upon request to qualified researchers. Requests for data access can be directed to Haukeland University Hospital, 5021 Bergen, Norway. Att. Head of Department. Dept. of Occupational Medicine, Marit Grønning; email: postmottak@helse-bergen.no; phone: +47 55975000. Org, nr. 983 974 724. The updated Data Availability statement is now promptly addressed in the revised cover letter.

Comment 4: We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 1 in your text; if accepted, production will need this reference to link the reader to the Table.

Response 4: Thank you for drawing our attention to this. In the revised version of the manuscript, we have removed the table that was not referred to in the text (regarding Resource. Funding). The funding information has been entered in the online submission system.

Reviewer #1

Comment 1: The major revision I suggest is the acknowledgement of the myriad cardiovascular and endocrine risk factors that are all confounders to the outcomes reported here, and as such must be acknowledged as not adjusted for in this work.

Response 1: The reviewer is absolutely correct that a myriad of cardiovascular and endocrine risk factors could be influencing offspring BMI. However, our exposure of interest (parental smoking onset) is very far back in time – as far back as in the youth of the preceding generation. Since a confounder must per definition precede both exposure and outcome in time, this rules out many of the likely factors we would traditionally include in models assessing associations with BMI in adults. Any cardiovascular and endocrine risk confounders would have to be either in the parents’ childhood or as far back as in the grandparents to merit status as confounders. As far as we know, no studies have shown associations between children’s cardiovascular or endocrine characteristics and BMI in the next generation of adults – or between grandparental cardiovascular and endocrine characteristics and BMI in adult grandchildren. Some factors could perhaps have been included as potential mediators. However, since we already focus on four mediators (parental pack years, parental BMI, offspring smoking status and offspring birthweight), we feel it is difficult to include even more mediators without losing focus of the main aim, i.e. investigating relations between parental smoking onset in adolescence and BMI in the next generation. However, to properly acknowledge the myriad of cardiovascular and endocrine risk factors for adult BMI, we have added a paragraph in the revised Discussion where we discuss these and explain better why they are not confounders in the analysis of parental smoking onset in adolescence and adult offspring BMI (lines 446-449 and 451-452).

Comment 2: The main point of this background is the suggestion that there is evidence that the germline cells of the parents might have critical exposure-sensitive periods for triggering epigenetic responses that will influence the risk of offspring becoming obese later in life. The remainder of the background is not directly of relevance to the manuscript but is interesting. It could be better placed in a broader discussion section as opposed to the background.

Response 2: We understand the reviewer’s comment. However, as one of the aims of the study was to investigate if effects were modified by offspring sex, we consider a paragraph addressing this in the background, to be of relevance.

Comment 3: Figure 1 is necessary and useful, but can be improved with more details provided about study centres and dates of the original surveys. Currently all we learn from the figure is sample numbers, when it is easy to make this figure more appealing to include the locations of these participants and the dates of the original studies and the form of the studies (postal questionnaires, clinics etc).

Response 3: Thank you for pointing this out. We agree with the reviewer that additional details on study centres and survey dates could be beneficial. Information about time points, locations and form of the studies has been included in a new flow chart in the revised manuscript.

Comment 4: Parents who started smoking in the 2 year interval around pregnancy were excluded, as the exposure seems only to be defined by years, and not by dates exactly. This is likely due to the original surveys only allowing answers by year.

Response 4: The reviewer is correct, and this is why we have not been able to define the exposure in more detail.

Comment 5: BMI from self-report. Less than ideal. Body fat mass from bioelectrical impedance analysis and FMI from that measurement/height^2. I am unsure how accurate such techniques are and as such, how valid this approach is.

Response 5: We do agree with the reviewer that BMI based on self-reported height and weight is not ideal, and that is why we pointed this out as a limitation of the present study (lines 425-426). However, bioelectrical impedance analysis (BIA) has since it was introduced in the 1980’s been widely applied and particularly useful in large epidemiological studies, where more advanced methods are not feasible, and simpler methods are needed. In addition to the cohorts used in the present study (ECRHS, RHINE, and RHINESSA), BIA has also been used in other large cohort studies (NHANES (USA), NUGENOB (EU), MONICA (DK) to predict body composition (as fat mass and body fat %). Several studies have been conducted on the validation of BIA, and there is broad consensus that is a valid and precise tool for estimating body composition in healthy subjects. We therefore consider this method useful and a valid measure of obesity and to compare body composition across populations (https://www.nature.com/articles/ejcn2012168; https://academic.oup.com/ajcn/article/64/3/459S/4651645; https://academic.oup.com/ajcn/article/64/3/436S/4651636)

Comment 6: The variables considered were parental socioeconomic status (via parental education), parental pack years, parental BMI (self report), offspring smoking (ever vs never only) and offspring birthweight. This is a small number of confounders. These are all indeed vital confounders to consider, but there are myriad other critical confounders that could be influencing the outcomes from across the huge list of cardiovascular and endocrine risk factors both prior to conception and post-natally.

Response 6: We acknowledge this concern. However, as outlined in our response to comment 1 above, a confounder must precede both exposure and outcome in time. For a more elaborate response, please see our response to comment 1.

Comment 7: We also know that the amount of smoking in close confinement to the offspring post birth is of importance, as studies with serum cotinine samples of tobacco exposure prove that although some parents may be smoking a great deal (as per pack years), if they do so outside or otherwise away from the child, they can mitigate possible exposures to their offspring.

Response 7: We thank the reviewer for pointing out this important issue. Unfortunately, we do not have any information about where the parents smoked so we cannot shed proper light on this in our analyses. Nevertheless, since this is of high relevance for our study, we have added a paragraph in the revised discussion with some reflections regarding this limitation (lines 438-440).

Comment 8: In reality a number of other confounders not listed in the methods were also included in results: parental age, offspring education, other parents smoking habits, and other parents BMI (other parents information obtained from the offspring…), however these were not include din final models.

Response 8: The reviewer correctly states that we did consider these factors as potential confounders, and we referred to these in the DAG (figure S1). However, we did not include them in the final models since they could not be defined as true confounders, i.e. preceding both exposure and outcome in time. For more information regarding this, please see our response to comment 1 above.

Comment 9: Table 1A and 1B and S1A and B: significant missing values for age smoking onset and packyears noted.

Response 9: The reviewer correctly states that there is a significant amount of missing values for packyears in the tables. However, the number of missing values for age of smoking is in fact not correctly updated, and we thank the reviewer for drawing our attention to this. In the final study population, where subjects with missing information on parental smoking have been excluded, these numbers equal 0. This has been corrected in the revised manuscript.

Comment 10: Tables would be clearer with stepped rows under umbrella terms. For example: ‘educational level, n (%)’ should be far left of the row, then the educational level options should be indented slightly so the reader can easily see the nested options.

Response 10: We agree with the reviewer, and in the revised manuscript, we have changed table 1A and 1B according to the comment.

Comment 11: Figure 2 shows that following adjustment (for father education and offspring sex) only fathers smoking after age 15 was associated with increased offspring BMI, but starting smoking before the age of 15 or after birth of offspring was not statistically associated. The strength of the association is modest and is demonstrated in Table 2. The use of both a table and figure for this result is perhaps unnecessary.

Response 11: We acknowledge the reviewer’s comment that it may be unnecessary to present both a table and a figure of the adjusted association between parental smoking onset and offspring bmi. However, as the figure additionally visualise both the crude and adjusted regression analyses (the table presents adjusted analysis), we do think including both of them adds information on the strength and the direction of the effect estimates, which may be of interest to a reader.

Comment 12: FigS2, along with all mediation plots, should be improved to capitalise the first letters of the Y-axis labels.

Response 12: Thank you for pointing this out. In the revised manuscript, all the mediation plots have capital first letter of the Y-axis labels.

Comment 13: The authors should define what exactly the natural direct and indirect effects mean, so that those not used to these analysis can understand better and learn.

Response 13: We agree with the reviewer that the methodology could be explained in a clearer manner. In the revised paragraph, we have attempted to improve the definition of natural direct and indirect effects so that readers may more easily understand (lines 211-213).

Comment 14: The descriptive results are interesting, and may well be novel, however we must ask if they of fundamental importance. We know that parents smoking and being of high BMI is a huge risk factor for their children subsequently smoking and being of high BMI, this is well established. Children being of high BMI is a huge risk factor for being of high BMI in adulthood. So demonstrating that a fathers smoking influenced the BMI of their offspring in adulthood does seem somewhat intuitive. Despite this, it is of course important to prove such associations as the authors have done here. See papers here that may remove some novelty from the findings in this manuscript. There appear to be others also and these should be discussed in the text, even if briefly.

Response 14: We appreciate that the results may seem somewhat intuitive, as the reviewer points out. Nevertheless, we believe that the results are highly novel because we look at smoking in one generation and BMI in the next generation, suggesting epigenetic mechanisms instead of traditional exposure-outcome associations within the individual. We agree that such associations are known for BMI (that parental BMI is associated with offspring BMI), and that smoke exposure in an individual (active smoking, but also passive) is associated with BMI in the same individual. However, evidence is very scarce with regard to inter-generational effects of smoking on BMI. We thank the reviewer for the two relevant papers suggested. One of them is included in the background section (line 89), and the other is added in the discussion section (line 410).

Comment 15: The major issue with this paper is the limited adjustment for confounding mediated through variables not considered in the analysis. The mediation of association analysis is interesting and of reportable note, but the primary findings remain largely unadjusted for confounders that are usually considered in the primary analysis. Some measures of risk factors usually included in models as utilised here include variables for physical (in)activity, nutritional status, alcohol consumption, sleep, stress, environmental factors such urban vs regional vs rural etc. A great deal of power is placed on the single measure of socio-economic status (parental education and offspring education) which is not ideal.

Response 15: We acknowledge this concern. For a more elaborate response, please see our response to comment 1.

Comment 16: Despite this, the manuscript reports interesting findings in relation to the effect of parents pack-years on offspring BMI and FMI, however the headline findings that - fathers beginning to smoke before conception was associated with higher BMI in their adult sons and daughters, and that father’s smoking starting in any time window was associated with higher FMI in adult sons. And that mothers’ pre-as well as postnatal smoking onset was associated with higher offspring adult BMI, but not higher fat mass – are results relying on largely unadjusted models which suggest profound epigenetic influence across the life course. This must be presented and discussed with caution.

Response 16: We acknowledge this concern, and therefore point out in the discussion that “A multitude of exposures and difference in genetic background exists in population studies, and as the offspring in the present study have reached adulthood, they have been exposed to a variety of environmental factors”(lines 443-446)”Thus, unmeasured factors may have impacted on our findings” (line 454). With regard to the mediation analysis, we also acknowledge that we have not been able to investigate “whether there are violations to the identification assumptions, especially with regard to all potential variables being independent and accounted for” (lines 457-458). This therefore needs further investigation. With regard to the reviewer’s comment on largely unadjusted models, for a more elaborate response, please see our response to comment 1.

Reviewer #2

Comment 1: Lines 217-219, check the percentages and they were not adding up to 100%.

Response 1: Thank you for bringing to our attention that this should be better described. The percentages are calculated based on number of unique parents (fathers=2111, mothers =2569) and not on number of participating offspring. In the revised version of the manuscript this has been pointed out in the text (line 223) and in table 1A (line 238) and table 1B (line 242) to make it easier for the readers to understand what the numbers are based on.

Comment 2: Lines 250-251. It is not clear if non-significant associations for postnatal smoking or smoking onset <15 were due to reduced sample size in Table 2. They were both positive. Suggest adding N in each line of Table 2.

Response 2: We agree with the reviewer, and this has been included in the table in the revised manuscript

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(45.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0235632.r003

Decision Letter 1

Seana Gall

19 Jun 2020

Parents’ smoking onset before conception as related to body mass index and fat mass in adult offspring: Findings from the RHINESSA generation study

PONE-D-20-07665R1

Dear Dr. Knudsen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Seana Gall

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for your detailed responses to the editor and reviewer comments. They were well considered.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0235632.r004

Acceptance letter

Seana Gall

24 Jun 2020

PONE-D-20-07665R1

Parents’ smoking onset before conception as related to body mass index and fat mass in adult offspring: Findings from the RHINESSA generation study

Dear Dr. Knudsen:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Seana Gall

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Fig. Directed Acyclic Graph (DAG).

The figure presents covariates considered to be included in the statistical model.

(TIF)

Click here for additional data file.^{(602.2KB, tif)}

S2 Fig. Visualising mediations of the association with fathers’ ≥15 smoking onset on offspring BMI.

(TIF)

Click here for additional data file.^{(1.2MB, tif)}

S3 Fig. Visualising mothers’ pack years as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(1.4MB, tif)}

S4 Fig. Visualising mothers’ BMI as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(800.1KB, tif)}

S5 Fig. Visualising offspring’s smoking habits as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(809.7KB, tif)}

S6 Fig. Visualising offspring’s birthweight as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(TIF)

Click here for additional data file.^{(809KB, tif)}

S1 Table

(PDF)

Click here for additional data file.^{(195.4KB, pdf)}

S2 Table. Associations between mothers’ smoking onset and offspring (n = 111) FMI.

The figure shows regression model adjusted for mothers’ education and offspring sex and reveals no association with mothers’ preconception and postnatal smoking onset and FMI in her offspring.

(PDF)

Click here for additional data file.^{(83.2KB, pdf)}

S3 Table. Mothers’ pack years as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(PDF)

Click here for additional data file.^{(103.6KB, pdf)}

S4 Table. Mothers’ BMI as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

(PDF)

Click here for additional data file.^{(102.4KB, pdf)}

S5 Table. Offspring’s smoking habits as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

The association between mothers’ preconception smoking onset before 15 years of age as well as smoking onset after birth and offspring BMI is partially mediated by offspring’s own smoking status.

(PDF)

Click here for additional data file.^{(102.5KB, pdf)}

S6 Table. Offspring’s birthweight as mediator of the observed associations between mothers’ smoking onset and offspring BMI.

In a subsample with birth weight data, there is no evidence of mediation by offspring birthweight.

(PDF)

Click here for additional data file.^{(102.4KB, pdf)}

S1 File. Table of ethic committee name and approval number for each study center.

(PDF)

Click here for additional data file.^{(66.6KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(45.2KB, docx)}

Data Availability Statement

[pone.0235632.ref001] 1.Riedel C, Schonberger K, Yang S, et al. Parental smoking and childhood obesity: higher effect estimates for maternal smoking in pregnancy compared with paternal smoking—a meta-analysis. International journal of epidemiology. 2014;43(5):1593–606. Epub 2014/08/01. 10.1093/ije/dyu150 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref002] 2.Ino T. Maternal smoking during pregnancy and offspring obesity: meta-analysis. Pediatrics international: official journal of the Japan Pediatric Society. 2010;52(1):94–9. Epub 2009/04/30. 10.1111/j.1442-200X.2009.02883.x . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref003] 3.Ino T, Shibuya T, Saito K, et al. Relationship between body mass index of offspring and maternal smoking during pregnancy. International journal of obesity (2005). 2012;36(4):554–8. Epub 2011/12/21. 10.1038/ijo.2011.255 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref004] 4.Oken E, Levitan EB, Gillman MW. Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis. International journal of obesity (2005). 2008;32(2):201–10. Epub 2008/02/19. 10.1038/sj.ijo.0803760 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref005] 5.Lisboa PC, de Oliveira E, de Moura EG. Obesity and endocrine dysfunction programmed by maternal smoking in pregnancy and lactation. Frontiers in physiology. 2012;3:437 Epub 2012/11/28. 10.3389/fphys.2012.00437 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref006] 6.Schneider S, Huy C, Schutz J, et al. Smoking cessation during pregnancy: a systematic literature review. Drug and alcohol review. 2010;29(1):81–90. Epub 2010/01/19. 10.1111/j.1465-3362.2009.00098.x . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref007] 7.Durmus B, Heppe DH, Taal HR, et al. Parental smoking during pregnancy and total and abdominal fat distribution in school-age children: the Generation R Study. International journal of obesity (2005). 2014;38(7):966–72. Epub 2014/01/23. 10.1038/ijo.2014.9 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref008] 8.Leary SD, Smith GD, Rogers IS, et al. Smoking during pregnancy and offspring fat and lean mass in childhood. Obesity (Silver Spring, Md). 2006;14(12):2284–93. Epub 2006/12/26. 10.1038/oby.2006.268 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref009] 9.Kwok MK, Schooling CM, Lam TH, et al. Paternal smoking and childhood overweight: evidence from the Hong Kong "Children of 1997". Pediatrics. 2010;126(1):e46–56. Epub 2010/07/01. 10.1542/peds.2009-2642 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref010] 10.Hui LL, Nelson EA, Yu LM, et al. Risk factors for childhood overweight in 6- to 7-y-old Hong Kong children. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity. 2003;27(11):1411–8. Epub 2003/10/24. 10.1038/sj.ijo.0802423 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref011] 11.von Kries R, Bolte G, Baghi L, et al. Parental smoking and childhood obesity—is maternal smoking in pregnancy the critical exposure? International journal of epidemiology. 2008;37(1):210–6. Epub 2007/12/07. 10.1093/ije/dym239 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref012] 12.Harris HR, Willett WC, Michels KB. Parental smoking during pregnancy and risk of overweight and obesity in the daughter. International journal of obesity (2005). 2013;37(10):1356–63. Epub 2013/06/06. 10.1038/ijo.2013.101 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref013] 13.Panzeri I, Pospisilik JA. Epigenetic control of variation and stochasticity in metabolic disease. Molecular metabolism. 2018. Epub 2018/06/18. 10.1016/j.molmet.2018.05.010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref014] 14.Soubry A. POHaD: why we should study future fathers. Environmental epigenetics. 2018;4(2):dvy007 Epub 2018/05/08. 10.1093/eep/dvy007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref015] 15.Sales VM, Ferguson-Smith AC, Patti M-E. Epigenetic Mechanisms of Transmission of Metabolic Disease across Generations. Cell Metabolism. 2017;25(3):559–71. 10.1016/j.cmet.2017.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref016] 16.Pembrey ME, Bygren LO, Kaati G, et al. Sex-specific, male-line transgenerational responses in humans. European journal of human genetics: EJHG. 2006;14(2):159–66. Epub 2006/01/05. 10.1038/sj.ejhg.5201538 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref017] 17.Bygren LO, Kaati G, Edvinsson S. Longevity determined by paternal ancestors' nutrition during their slow growth period. Acta biotheoretica. 2001;49(1):53–9. Epub 2001/05/23. 10.1023/a:1010241825519 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref018] 18.Kaati G, Bygren LO, Edvinsson S. Cardiovascular and diabetes mortality determined by nutrition during parents' and grandparents' slow growth period. European journal of human genetics: EJHG. 2002;10(11):682–8. Epub 2002/10/31. 10.1038/sj.ejhg.5200859 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref019] 19.Northstone K, Golding J, Davey Smith G, et al. Prepubertal start of father's smoking and increased body fat in his sons: further characterisation of paternal transgenerational responses. European journal of human genetics: EJHG. 2014;22(12):1382–6. Epub 2014/04/03. 10.1038/ejhg.2014.31 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref020] 20.Carslake D, Pinger PR, Romundstad P, et al. Early-Onset Paternal Smoking and Offspring Adiposity: Further Investigation of a Potential Intergenerational Effect Using the HUNT Study. PloS one. 2016;11(12):e0166952 Epub 2016/12/03. 10.1371/journal.pone.0166952 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref021] 21.Disney G, Gurrin L, Aitken Z, et al. Hierarchical Models for International Comparisons: Smoking, Disability, and Social Inequality in 21 European Countries. Epidemiology (Cambridge, Mass). 2020;31(2):282–9. Epub 2019/12/24. 10.1097/ede.0000000000001154 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref022] 22.Johannessen A, Lonnebotn M, Calciano L, et al. Being overweight in childhood, puberty, or early adulthood: Changing asthma risk in the next generation? The Journal of allergy and clinical immunology. 2020;145(3):791-9.e4. Epub 2019/09/11. 10.1016/j.jaci.2019.08.030 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref023] 23.Svanes C, Koplin J, Skulstad SM, et al. Father's environment before conception and asthma risk in his children: a multi-generation analysis of the Respiratory Health In Northern Europe study. International journal of epidemiology. 2017;46(1):235–45. Epub 2016/08/28. 10.1093/ije/dyw151 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref024] 24.Accordini S, Calciano L, Johannessen A, et al. A three-generation study on the association of tobacco smoking with asthma. International journal of epidemiology. 2018;47(4):1106–17. Epub 2018/03/14. 10.1093/ije/dyy031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref025] 25.Yang S, Decker A, Kramer MS. Exposure to parental smoking and child growth and development: a cohort study. BMC pediatrics. 2013;13:104 Epub 2013/07/12. 10.1186/1471-2431-13-104 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref026] 26.Toschke AM, Koletzko B, Slikker W, et al. Childhood obesity is associated with maternal smoking in pregnancy. European journal of pediatrics. 2002;161(8):445–8. Epub 2002/08/13. 10.1007/s00431-002-0983-z . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref027] 27.Toschke AM, Montgomery SM, Pfeiffer U, et al. Early intrauterine exposure to tobacco-inhaled products and obesity. American journal of epidemiology. 2003;158(11):1068–74. Epub 2003/11/25. 10.1093/aje/kwg258 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref028] 28.Vik T, Jacobsen G, Vatten L, et al. Pre- and post-natal growth in children of women who smoked in pregnancy. Early human development. 1996;45(3):245–55. Epub 1996/07/19. 10.1016/0378-3782(96)01735-5 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref029] 29.Lane M, McPherson NO, Fullston T, et al. Oxidative stress in mouse sperm impairs embryo development, fetal growth and alters adiposity and glucose regulation in female offspring. PLoS One. 2014;9(7):e100832 Epub 2014/07/10. 10.1371/journal.pone.0100832 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref030] 30.Fullston T, Ohlsson Teague EM, Palmer NO, et al. Paternal obesity initiates metabolic disturbances in two generations of mice with incomplete penetrance to the F2 generation and alters the transcriptional profile of testis and sperm microRNA content. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2013;27(10):4226–43. Epub 2013/07/13. 10.1096/fj.12-224048 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref031] 31.Ng SF, Lin RC, Laybutt DR, et al. Chronic high-fat diet in fathers programs beta-cell dysfunction in female rat offspring. Nature. 2010;467(7318):963–6. Epub 2010/10/22. 10.1038/nature09491 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref032] 32.Kwong WY, Wild AE, Roberts P, et al. Maternal undernutrition during the preimplantation period of rat development causes blastocyst abnormalities and programming of postnatal hypertension. Development (Cambridge, England). 2000;127(19):4195–202. Epub 2000/09/08. . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref033] 33.Long JA. jtools: Analysis and Presentation of Social Scientific Data. 2019.

[pone.0235632.ref034] 34.MacKinnon DP, Luecken LJ. How and for whom? Mediation and moderation in health psychology. Health Psychol. 2008;27(2s):S99–s100. Epub 2008/05/03. 10.1037/0278-6133.27.2(Suppl.).S99 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref035] 35.Cohen J CP, West SG, Aiken LA. Applied multiple regression/correlation analysis for the behavioral sciences. 3. Hillsdale N, editor 2003. [Google Scholar]

[pone.0235632.ref036] 36.Johan Steen TL, Beatrijs Moerkerke, Stijn Vansteelandt medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models. Journal of Statistical Software. 2017;76(11). doi: 10.18637. [Google Scholar]

[pone.0235632.ref037] 37.Huypens P, Sass S, Wu M, et al. Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Nature genetics. 2016;48(5):497–9. Epub 2016/03/15. 10.1038/ng.3527 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref038] 38.Ng SF, Lin RC, Maloney CA, et al. Paternal high-fat diet consumption induces common changes in the transcriptomes of retroperitoneal adipose and pancreatic islet tissues in female rat offspring. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2014;28(4):1830–41. Epub 2014/01/15. 10.1096/fj.13-244046 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref039] 39.Marczylo EL, Amoako AA, Konje JC, et al. Smoking induces differential miRNA expression in human spermatozoa: a potential transgenerational epigenetic concern? Epigenetics. 2012;7(5):432–9. Epub 2012/03/24. 10.4161/epi.19794 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref040] 40.Goossens GH. The Metabolic Phenotype in Obesity: Fat Mass, Body Fat Distribution, and Adipose Tissue Function. Obes Facts. 2017;10(3):207–15. Epub 2017/06/01. 10.1159/000471488 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref041] 41.Peltz G, Aguirre MT, Sanderson M, et al. The role of fat mass index in determining obesity. Am J Hum Biol. 2010;22(5):639–47. Epub 2010/08/26. 10.1002/ajhb.21056 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref042] 42.Nianogo RA, Arah OA. Investigating the Role of Childhood Adiposity in the Development of Adult Type 2 Diabetes in a 64-year Follow-up Cohort: An Application of the Parametric G-formula Within an Agent-based Simulation Study. Epidemiology (Cambridge, Mass). 2019;30 Suppl 2:S101–s9. Epub 2019/10/01. 10.1097/ede.0000000000001062 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref043] 43.Morkve Knudsen GT, Rezwan FI, Johannessen A, et al. Epigenome-wide association of father's smoking with offspring DNA methylation: a hypothesis-generating study. Environmental epigenetics. 2019;5(4):dvz023 Epub 2019/12/13. 10.1093/eep/dvz023 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref044] 44.Ringseis R, Eder K, Mooren FC, et al. Metabolic signals and innate immune activation in obesity and exercise. Exerc Immunol Rev. 2015;21:58–68. Epub 2015/04/01. . [PubMed] [Google Scholar]

[pone.0235632.ref045] 45.Ambrosini GL. Childhood dietary patterns and later obesity: a review of the evidence. Proc Nutr Soc. 2014;73(1):137–46. Epub 2013/11/28. 10.1017/S0029665113003765 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref046] 46.Brug J, van Stralen MM, Te Velde SJ, et al. Differences in weight status and energy-balance related behaviors among schoolchildren across Europe: the ENERGY-project. PloS one. 2012;7(4):e34742 Epub 2012/05/05. 10.1371/journal.pone.0034742 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref047] 47.Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism: clinical and experimental. 2019;92:6–10. Epub 2018/09/27. 10.1016/j.metabol.2018.09.005 . [DOI] [PubMed] [Google Scholar]

[pone.0235632.ref048] 48.Han TS, Hart CL, Haig C, et al. Contributions of maternal and paternal adiposity and smoking to adult offspring adiposity and cardiovascular risk: the Midspan Family Study. BMJ open. 2015;5(11):e007682 10.1136/bmjopen-2015-007682 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref049] 49.Tehard B, van Liere MJ, Com Nougue C, et al. Anthropometric measurements and body silhouette of women: validity and perception. J Am Diet Assoc. 2002;102(12):1779–84. Epub 2002/12/19. 10.1016/s0002-8223(02)90381-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0235632.ref050] 50.Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i–xii, 1–253. Epub 2001/03/10. . [PubMed] [Google Scholar]

[pone.0235632.ref051] 51.Stein CJ, Colditz GA. The epidemic of obesity. The Journal of clinical endocrinology and metabolism. 2004;89(6):2522–5. Epub 2004/06/08. 10.1210/jc.2004-0288 . [DOI] [PubMed] [Google Scholar]

PERMALINK

Parents’ smoking onset before conception as related to body mass index and fat mass in adult offspring: Findings from the RHINESSA generation study

Gerd Toril Mørkve Knudsen

Shyamali Dharmage

Christer Janson

Michael J Abramson

Bryndís Benediktsdóttir

Andrei Malinovschi

Svein Magne Skulstad

Randi Jacobsen Bertelsen

Francisco Gomez Real

Vivi Schlünssen

Nils Oskar Jõgi

José Luis Sánchez-Ramos

Mathias Holm

Judith Garcia-Aymerich

Bertil Forsberg

Cecilie Svanes

Ane Johannessen

Roles

Abstract

Background

Methods

Study design and population

Parent population

Fig 1. Flow chart of study population.

Offspring population

Exposure: Parental smoking

Outcomes: Offspring body mass index and fat mass index

Potential confounders and mediators

Statistical analysis

Results

Table 1.

Fathers’ smoking onset and offspring BMI and FMI

Fig 2. Visualising associations between fathers’ smoking onset and offspring (n = 2916) BMI.

Table 2. Associations between fathers’ smoking onset and offspring (n = 2916) BMI.

Table 3. Associations between fathers’ smoking onset and offspring (n = 129) FMI.

Fig 3. Visualising associations between fathers’ smoking onset and offspring (n = 129) FMI.

Fig 4. Visualising mean FMI differences in sons and daughters according to fathers’ smoking onset.

Mothers’ smoking onset and offspring BMI and FMI

Table 4. Associations between mothers’ smoking onset and offspring (n = 3531) BMI.

Fig 5. Visualising associations between mothers’ smoking onset and offspring (n = 3531) BMI.

Mediation analyses of fathers’ smoking onset and offspring BMI

Table 5. Mediation of the observed association between fathers’ ≥15 smoking onset and offspring BMI.

Mediation analyses of mothers’ smoking onset and offspring BMI

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Seana Gall

Roles

Author response to Decision Letter 0

Decision Letter 1

Seana Gall

Roles

Acceptance letter

Seana Gall

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases