Advertisement
  • Loading metrics

Relationships between intensity, duration, cumulative dose, and timing of smoking with age at menopause: A pooled analysis of individual data from 17 observational studies

  • Dongshan Zhu,

    Roles Formal analysis, Methodology, Visualization, Writing – original draft

    Affiliation The University of Queensland, School of Public Health, Brisbane, Queensland, Australia

  • Hsin-Fang Chung,

    Roles Data curation, Supervision, Writing – review & editing

    Affiliation The University of Queensland, School of Public Health, Brisbane, Queensland, Australia

  • Nirmala Pandeya,

    Roles Data curation, Supervision, Writing – review & editing

    Affiliations The University of Queensland, School of Public Health, Brisbane, Queensland, Australia, Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

  • Annette J. Dobson,

    Roles Methodology, Supervision, Writing – review & editing

    Affiliation The University of Queensland, School of Public Health, Brisbane, Queensland, Australia

  • Janet E. Cade,

    Roles Investigation, Writing – review & editing

    Affiliation Nutritional Epidemiology Group, School of Food Science and Nutrition, University of Leeds, Leeds, United Kingdom

  • Darren C. Greenwood,

    Roles Investigation, Writing – review & editing

    Affiliation Nutritional Epidemiology Group, School of Food Science and Nutrition, University of Leeds, Leeds, United Kingdom

  • Sybil L. Crawford,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America

  • Nancy E. Avis,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina

  • Ellen B. Gold,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Public Health Sciences, University of California, Davis School of Medicine, California, United States of America

  • Ellen S. Mitchell,

    Roles Investigation, Writing – review & editing

    Affiliation Family and Child Nursing, School of Nursing, University of Washington, Seattle, Washington, United States of America

  • Nancy F. Woods,

    Roles Investigation, Writing – review & editing

    Affiliation Biobehavioral Nursing and Health Systems, School of Nursing, University of Washington, Seattle, Washington, United States of America

  • Debra Anderson,

    Roles Investigation, Writing – review & editing

    Affiliation Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia

  • Daniel E. Brown,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Anthropology, University of Hawaii, Hilo, Hawaii, United States of America

  • Lynnette L. Sievert,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Anthropology, UMass Amherst, Amherst, Massachusetts, United States of America

  • Eric J. Brunner,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Epidemiology and Public Health, University College London, London, United Kingdom

  • Diana Kuh,

    Roles Investigation, Writing – review & editing

    Affiliation Medical Research Council Unit for Lifelong Health and Ageing at UCL, London, United Kingdom

  • Rebecca Hardy,

    Roles Investigation, Writing – review & editing

    Affiliation Medical Research Council Unit for Lifelong Health and Ageing at UCL, London, United Kingdom

  • Kunihiko Hayashi,

    Roles Investigation, Writing – review & editing

    Affiliation School of Health Sciences, Gunma University, Maebashi City, Gunma, Japan

  • Jung Su Lee,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

  • Hideki Mizunuma,

    Roles Investigation, Writing – review & editing

    Affiliation Fukushima Medical Center for Children and Women, Fukushima Medical University, Fukushima, Japan

  • Graham G. Giles,

    Roles Investigation, Writing – review & editing

    Affiliations Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

  • Fiona Bruinsma,

    Roles Investigation, Writing – review & editing

    Affiliation Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia

  • Therese Tillin,

    Roles Investigation, Writing – review & editing

    Affiliation Institute of Cardiovascular Science, University College London, London, United Kingdom

  • Mette Kildevæld Simonsen,

    Roles Investigation, Writing – review & editing

    Affiliation UcDiakonissen and Parker Institute, Frederiksberg, Denmark

  • Hans-Olov Adami,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway

  • Elisabete Weiderpass,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland, Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway, Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway

  • Marianne Canonico,

    Roles Investigation, Writing – review & editing

    Affiliation Paris-Saclay University, Paris-South University, UVSQ, Center for Research in Epidemiology and Population Health, INSERM, France

  • Marie-Laure Ancelin,

    Roles Investigation, Writing – review & editing

    Affiliation INSERM, University Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France

  • Panayotes Demakakos,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Epidemiology and Public Health, University College London, London, United Kingdom

  •  [ ... ],
  • Gita D. Mishra

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    g.mishra@uq.edu.au

    Affiliation The University of Queensland, School of Public Health, Brisbane, Queensland, Australia

  • [ view all ]
  • [ view less ]

Relationships between intensity, duration, cumulative dose, and timing of smoking with age at menopause: A pooled analysis of individual data from 17 observational studies

  • Dongshan Zhu, 
  • Hsin-Fang Chung, 
  • Nirmala Pandeya, 
  • Annette J. Dobson, 
  • Janet E. Cade, 
  • Darren C. Greenwood, 
  • Sybil L. Crawford, 
  • Nancy E. Avis, 
  • Ellen B. Gold, 
  • Ellen S. Mitchell
PLOS
x

Abstract

Background

Cigarette smoking is associated with earlier menopause, but the impact of being a former smoker and any dose-response relationships on the degree of smoking and age at menopause have been less clear. If the toxic impact of cigarette smoking on ovarian function is irreversible, we hypothesized that even former smokers might experience earlier menopause, and variations in intensity, duration, cumulative dose, and age at start/quit of smoking might have varying impacts on the risk of experiencing earlier menopause.

Methods and findings

A total of 207,231 and 27,580 postmenopausal women were included in the cross-sectional and prospective analyses, respectively. They were from 17 studies in 7 countries (Australia, Denmark, France, Japan, Sweden, United Kingdom, United States) that contributed data to the International collaboration for a Life course Approach to reproductive health and Chronic disease Events (InterLACE). Information on smoking status, cigarettes smoked per day (intensity), smoking duration, pack-years (cumulative dose), age started, and years since quitting smoking was collected at baseline. We used multinomial logistic regression models to estimate multivariable relative risk ratios (RRRs) and 95% confidence intervals (CIs) for the associations between each smoking measure and categorised age at menopause (<40 (premature), 40–44 (early), 45–49, 50–51 (reference), and ≥52 years). The association with current and former smokers was analysed separately. Sensitivity analyses and two-step meta-analyses were also conducted to test the results. The Bayesian information criterion (BIC) was used to compare the fit of the models of smoking measures.

Overall, 1.9% and 7.3% of women experienced premature and early menopause, respectively. Compared with never smokers, current smokers had around twice the risk of experiencing premature (RRR 2.05; 95% CI 1.73–2.44) (p < 0.001) and early menopause (1.80; 1.66–1.95) (p < 0.001). The corresponding RRRs in former smokers were attenuated to 1.13 (1.04–1.23; p = 0.006) and 1.15 (1.05–1.27; p = 0.005). In both current and former smokers, dose-response relationships were observed, i.e., higher intensity, longer duration, higher cumulative dose, earlier age at start smoking, and shorter time since quitting smoking were significantly associated with higher risk of premature and early menopause, as well as earlier menopause at 45–49 years. Duration of smoking was a strong predictor of age at natural menopause. Among current smokers with duration of 15–20 years, the risk was markedly higher for premature (15.58; 11.29–19.86; p < 0.001) and early (6.55; 5.04–8.52; p < 0.001) menopause. Also, current smokers with 11–15 pack-years had over 4-fold (4.35; 2.78–5.92; p < 0.001) and 3-fold (3.01; 2.15–4.21; p < 0.001) risk of premature and early menopause, respectively. Smokers who had quit smoking for more than 10 years had similar risk as never smokers (1.04; 0.98–1.10; p = 0.176). A limitation of the study is the measurement errors that may have arisen due to recall bias.

Conclusions

The probability of earlier menopause is positively associated with intensity, duration, cumulative dose, and earlier initiation of smoking. Smoking duration is a much stronger predictor of premature and early menopause than others. Our findings highlight the clear benefits for women of early smoking cessation to lower their excess risk of earlier menopause.

Author summary

Why was this study done?

  • Current smoking is associated with earlier age at natural menopause.
  • The impact of former smoking on age at natural menopause is inconclusive, and the dose-response relationships between the degree of smoking (including intensity and duration) and age at natural menopause have been less clear.

What did the researchers do and find?

  • We pooled individual-level data from 17 observational studies that contributed to the International collaboration for a Life course Approach to reproductive health and Chronic disease Events (InterLACE) consortium. A total of 207,231 and 27,580 postmenopausal women were included in the cross-sectional and prospective analyses, respectively.
  • We examined the associations between smoking measures (smoking status, intensity, duration, cumulative dose, and timing of cigarette smoking) and age at natural menopause.
  • Compared with never smokers, former smokers had around 15% higher risk of premature and early menopause, but this was much lower than that of current smokers, who had double the risk when compared to never smokers.
  • Early menopause is positively associated with intensity, duration, cumulative dose, and earlier initiation of smoking in both current and former smokers. Duration of smoking was a strong predictor of early menopause.

What do these findings mean?

  • Our findings highlight the clear benefits for women of early smoking cessation to lower their excess risk of earlier menopause, thereby reducing the increased risks of chronic diseases (e.g., cardiovascular disease and osteoporosis) associated with both smoking and earlier menopause.

Introduction

Natural menopause is defined as when a woman has had no menstrual periods for 12 consecutive months that did not result from interventions (such as bilateral oophorectomy, hysterectomy, chemotherapy, or radiotherapy). It typically occurs between 49 and 52 years of age, with a median age of 51.4 years in high-income countries [13]. Approximately 5% of women report early menopause [4,5], occurring between ages 40 and 45 years, whereas premature menopause—also termed premature ovarian failure (POF)—occurs before age 40 years [5] in about 1% of women [6,7]. Women with early or premature menopause are at increased risk of morbidity and mortality in later life, including cardiovascular disease [8,9], osteoporosis [9], and type 2 diabetes [10,11].

In addition to genetic factors [5], socioeconomic status, body weight, cigarette smoking, race/ethnicity, age at menarche, and nulliparity influence age at menopause [1215]. Cigarette smoking is the most established factor and leads to having menopause by almost one year earlier [3,15,16], but important knowledge gaps remain. A recent systematic review of 109 studies concluded that although current smoking is associated with earlier age at menopause, evidence for the impact of being a former smoker was inconclusive. In addition, dose-response relationships on the degree of smoking and age at menopause have been less clear [16], and few studies have examined the relationship between age at start [17,18] or quit of smoking [19,20] and age at menopause. If the hypothesized toxic impact of cigarette smoking on ovarian function is irreversible [21], even former smokers may experience earlier menopause, and variations in intensity, duration, cumulative dose, as well as age at start/quit of smoking might have varying impacts on the risk of experiencing earlier menopause.

To investigate these issues, we analysed individual-level data from more than 200,000 postmenopausal women in 17 studies that contributed data to the International collaboration for a Life course Approach to reproductive health and Chronic disease Events (InterLACE) [22,23]. Dose-response relationships were investigated between the multiple aspects of smoking and age at menopause in both cross-sectional and prospective analyses.

Methods

Participants

InterLACE has pooled individual-level data on reproductive health and chronic diseases from over 500,000 women from 25 observational studies across 10 countries. Most studies are prospective and collected survey data on key reproductive, sociodemographic, and lifestyle factors, as well as disease outcomes. Participants in each of the included studies were recruited and provided consent according to the approved protocols of the Institutional Review Board or Human Research Ethics Committee at each relevant institution. More detailed descriptions of the InterLACE consortium have been published previously [22,23]. For the present analyses, 17 studies of InterLACE (Table 1) provided information on women’s smoking status at the baseline survey and age at natural menopause. Of these, 14 collected longitudinal data, while the remaining 3 provided only cross-sectional information. As the use of hormone therapy (HT) and oral contraceptive pills (OCPs) precludes accurate definition of menopausal status, hormone users were excluded from the analyses unless natural menopause was specifically reported. The cross-sectional analysis was based on 207,231 postmenopausal women with information on age at natural menopause (i.e., women who experienced surgical menopause were excluded), smoking status, and key covariates at baseline, BMI, years of education, and race/ethnicity/region. The prospective analysis was based on 27,580 women who experienced menopause after the baseline survey. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 Text).

thumbnail
Table 1. Characteristics of individual studies in the InterLACE consortium for cross-sectional analysis*.

https://doi.org/10.1371/journal.pmed.1002704.t001

Outcome and exposure variables

Age at natural menopause was the outcome variable and was self-reported. In the cross-sectional analyses, age at menopause was categorised as <40 years (premature menopause), 40–44 (early menopause), 45–49, 50–51 (reference category), and 52 years and above, while in the prospective analyses—due to the insufficient number of women with premature menopause—age at menopause was classified into 4 categories by combining the premature and early menopause groups as <45 years.

Information on smoking status, intensity, duration, and timing of cigarette smoking was collected through self-reported questionnaires. Smoking status was categorised as current, former, or never. Current smokers were defined as women who started smoking before menopause and were regular smokers at baseline or who experienced menopause before they quit smoking. Former smokers were defined as women who experienced menopause after they quit smoking at least 1 year. Smoking intensity was defined as the average number of cigarettes smoked per day categorised as 1–9, 10–19, and 20 cigarettes or more. Among former smokers, duration was defined as the difference between the reported age at starting and quitting and, for current smokers, the difference between the age at starting and age at menopause. Therefore, by definition of duration in current smokers, women with later menopause would tend to have a longer duration. In order to observe the association between smoking duration and earlier menopause, we focused on short duration of smoking (similar reason for categories of pack-years and years since quitting smoking). To determine appropriate categories for duration of smoking, we used a fractional-polynomial model to plot the shape of the relationship between smoking duration (in all current smokers) and age at menopause. As expected, we found a U-shape, and duration of 20 years was determined as an appropriate cut-point to define a maximum duration that had an impact on age at menopause. Duration of smoking was then categorised as <10, 10–14, and 15–20 years. The cumulative dose of smoking was calculated as pack-years (the number of cigarettes smoked per day divided by 20 [assuming 20 cigarettes a pack] and multiplied by the duration of smoking) [17]. We categorized pack-years as ≤5, 6–10, and 11–15. Other smoking characteristics included age when a woman started smoking (categorised as <15, 15–19, 20 or more years of age), and years since quitting smoking (calculated by taking the difference between age at menopause and age at quitting). Inherent in the definition of years since quitting smoking, age at menopause places an upper time limit possible. Thus, in the analysis we focused on the more recent years since quitting (categorised as 1–5, 6–10, and 11–15 years).

Covariates

Baseline BMI, years of education, race/ethnicity/region, parity, and age at menarche were used as covariates because these have been shown to be associated with age at menopause [14,15]. BMI was categorised according to World Health Organization (WHO) criteria as <18.5 kg/m2, 18.5 to 24.9 kg/m2, 25 to 29.9 kg/m2, and ≥30 kg/m2. Years of education was categorised into ≤10, 11–12, and >12 years. Race/ethnicity/region was combined into one, with 7 categories: White European, White Australian/New Zealand, White American/Canadian, Japanese, Other Asian, African American/Black, and Other. Parity was categorised as none, one child, two children, and three or more children. Age at menarche was divided into 5 categories as ≤11, 12, 13, 14, and 15 years or more.

Statistical analyses

The data analyses for the present study were performed following a prospective research proposal (S2 Text). We performed pooled analyses of all the individual-level data. Multinomial (polytomous) logistic regression models with 5 categories of outcome for age at menopause (<40, 40–44, 45–49, 50–51, and 52 years and older) were used, with age 50–51 years at menopause as a reference group. The intensity, duration, cumulative dose, and age started smoking were analysed in combination with smoking status for current and former smokers. Never smokers were used as reference for all smoking measures. All statistical models were adjusted for BMI, education level, a combined variable for race/ethnicity/region, based on the established relations of these in the literature to the outcome. Age at menarche and parity were also potential confounders that could affect the association between smoking exposures and age at menopause. Thus, the models were additionally adjusted for both covariates but with only 14 studies included because the WHITEHALL and Southall And Brent Revisited (SABRE) studies did not collect age at menarche and Healthy Ageing of Women (HOW) study did not collect parity. Adjusted RRRs (i.e., the ratio of two relative risks, RRRs) [24] and 95% confidence intervals (CIs) for smoking measures and each category of age at menopause were estimated. The Bayesian information criterion (BIC) was used to compare the fit of the models that used each of the measures of smoking characteristic. A lower BIC implies a better fit.

Sensitivity analyses and two-step meta-analyses.

Because the UK Biobank data contributed more than 60% of the total sample used in the cross-sectional analyses, we conducted a sensitivity analysis by excluding this study. Also, we analysed the associations of intensity, duration, age of starting smoking, and years since quitting with age at menopause by adjusting for all covariates, including the other smoking characteristics. Finally, in order to evaluate the heterogeneity among studies, in both cross-sectional and prospective analyses, we also performed study-specific regressions and random-effects meta-analysis for the associations between each level of the cigarette smoking measures and each category of menopausal age to estimate the magnitude of association.

Statistical analyses were performed by using SAS (version 9.4; SAS Institute Inc., Cary, NC) and Stata (version 14.0; Stata Corp., College Station, TX). In SAS, the SURVEYLOGISTIC procedure was used with the generalized logit link to adjust for the clustering of data within studies to obtain robust standard errors. The SGPANEL procedure was used to plot the associations. In STATA, the METAN command was used to perform meta-analysis. All statistical tests were based on the two-sided 5% level of significance.

Results

Baseline characteristics

The mean age at baseline was 58.0 years (range 42.0 to 74.5 years), with more than half of the women born between 1940 and 1949 (Table 1). Most women were white European (85.8%). The mean age at natural menopause was 50.2 years (SD: 4.4), and the median was 50.0 years (interquartile range: 48.0–53.0), with 57.5% of participants being never smokers, 30.7% former, and 11.8% current smokers at baseline. Current smokers had a longer duration of smoking (mean: 29.6 versus 19.4 years) and higher number of pack-years (mean: 22.9 versus 16.7 pack-years) than former smokers (Table 2).

thumbnail
Table 2. Overall baseline characteristics of women used for cross-sectional analysis (n = 207,231).

https://doi.org/10.1371/journal.pmed.1002704.t002

Cross-sectional associations

Compared with never smokers, current smokers experienced an increased risk of premature (RRR 2.05, 95% CI 1.73–2.44; p < 0.001) and early (1.80; 1.66–1.95; p < 0.001) menopause as well as earlier menopause at 45–49 years (1.47; 1.42–1.52; p < 0.001). In former smokers, the corresponding RRRs were substantially attenuated to 1.13 (1.04–1.23; p = 0.006), 1.15 (1.05–1.27; p = 0.005), and 1.09 (1.05–1.13; p < 0.001), respectively (Table 3). A consistent trend was found between all measures of smoking characteristics and age at menopause. Current smokers with long duration of smoking had much higher risk of premature menopause and early menopause, with RRR of 15.58 (95% CI 11.29–19.86; p < 0.001) and 6.55 (95% CI 5.04–8.52; p < 0.001), respectively, in duration 15–20 years. In both current and former smokers, dose-response relationships were observed in all measures of smoking characteristics, i.e., higher intensity, longer duration, higher cumulative dose, earlier age at start smoking, and shorter time since quitting smoking were significantly associated with higher risk of premature and early menopause, as well as earlier menopause at 45–49 years (Table 3, Fig 1). Compared with never smokers, women who had recently quit smoking (within 5 years) had an increased risk of premature (1.70; 1.56–1.86; p < 0.001) and early (1.70; 1.49–1.93; p < 0.001) menopause, which were close to the levels of risk seen in current smokers (Table 3). Duration of smoking had the lowest BIC value (366,427), followed by cumulative dose (381,329). When further adjusted for age at menarche and parity, the estimates for smoking status were attenuated slightly (S1 Table).

thumbnail
Fig 1. Relationship between smoking exposure and age at natural menopause.

The cross-sectional associations of (A) intensity of smoking (cigarettes/day), (B) cumulative dose of smoking (pack-years), (C) age started smoking (years), and (D) years since quitting smoking (years) with the risk for premature menopause (<40 years), early menopause (40–44 years), and menopause at 45–49 years in current and/or former smokers. All RRRs were adjusted for race/ethnicity/region, education level, and BMI. Never smokers were used as reference group. RRR, relative risk ratio.

https://doi.org/10.1371/journal.pmed.1002704.g001

thumbnail
Table 3. Cross-sectional associations between cigarette smoking and age at menopause (n = 207,231).

https://doi.org/10.1371/journal.pmed.1002704.t003

Sensitivity analyses and meta-analyses

After excluding the UK Biobank data, the associations in former smokers with premature (1.04, 0.86–1.26; p = 0.641) and early (1.01; 0.87–1.18; p = 0.730) menopause were no longer significant (S2 Table). In addition, when intensity, duration, and age at start of smoking were analysed by further adjusting for other smoking characteristics (S3 Table), a similar set of relationships were found as when the UK Biobank data were included, except that for current smokers the risk of early menopause was no longer significant (1.28; 0.91–1.81; p = 0.061) compared with the reference group (women who had quit smoking for at least 11 years). The study-specific regressions and random-effects meta-analysis for the associations between each level of cigarettes smoking exposures and each category of menopause showed consistent results with the main analysis (S4 Table).

Prospective associations

Overall, a similar pattern of results were obtained from prospective analyses that only included women who experienced menopause after baseline. Due to the limited number of women experiencing early menopause (<45 years) at a subsequent survey, some estimates of association for early menopause were attenuated (e.g., cumulative dose of smoking in current smokers) or with wide CIs (e.g., intensity of smoking in former smokers) (Table 4). The study-specific regressions and random-effects meta-analysis for prospective analyses were provided in S5 Table.

thumbnail
Table 4. Prospective associations between cigarette smoking and age at menopause (n = 27,580).

https://doi.org/10.1371/journal.pmed.1002704.t004

Discussion

Summary of results

Our results indicate that compared with never smokers, both current smokers and former smokers are at higher risk of premature and early menopause and menopause at 45–49 years, although the association is weaker among former smokers. Intensity, duration, cumulative dose, younger age at start smoking, and shorter time since quitting smoking are all associated with earlier age at menopause in both current and former smokers, but the excess risks were substantially lower in former smokers. Consistent dose-response relationships were observed in both current and former smokers. Smoking duration is a much stronger predictor of earlier menopause than others. Current smokers with long duration (15–20 years) showed particularly higher risk of premature and early menopause. These findings highlight the clear benefits of early smoking cessation to lower the excess risk of earlier menopause.

Former smokers and age at menopause

Current smoking is an established risk factor for earlier menopause [3,15,16], whereas the relationship between past smoking and age at menopause is less conclusive. Compared with never smokers, some previous studies showed that former smokers had earlier menopause [20,25], others found no association [2628], and one study a reduced risk [29]. The number of former smokers in studies that found no association was small (around 1,000 women) [26,28], and the study that found a negative association had only 83 former smokers [29]. We found that being a former smoker was significantly associated with premature and early menopause, as well as menopause at 45–49 years, indicating that the effect of smoking on ovarian senescence is not limited to current smoking.

Potential mechanisms linking smoking to earlier menopause

Several hypotheses might explain why smoking leads to earlier menopause. First, cigarette smoke yields polycyclic aromatic hydrocarbons (PAHs), which activate the aromatic hydrocarbon receptor. This receptor induces expression of the apoptosis-promoting gene Bcl2-associated X protein (BAX) in oocytes, which increases the rate of oocyte apoptosis leading to earlier ovarian failure [30]. This mechanism indicates a permanent, irreversible influence that occurs in both former and current smokers and may explain why former smokers have an earlier menopause than never smokers. PAHs also induce other hepatic enzymes, such as monooxygenases and transferases, which enhance steroid metabolism and clearance [31]. Furthermore, PAHs induce or up-regulate the expression of cytochrome P450 enzymes to solubilize and decrease the serum estrogen levels [32]. Second, smoking has been suggested to have an anti-estrogenic effect [33]; awaking the 2-hydroxylation pathway in the liver would convert bioactive estrogens into 2-hydroxyestrogens with minimal activity in peripheral tissues [34]. Finally, smoking increases adrenal production of androgens, which resists or blunts the functions of estrogens [35]. The latter two mechanisms reflect a temporary and reversible effect and may explain why being a current smoker has a greater impact on earlier menopause than being a former smoker.

Intensity, duration, quantity, and timing of smoking and age at menopause

Some studies have reported a potential dose-response relationship between intensity of smoking and earlier menopause [17,18,28], while other studies have shown no such association [27,29]. A systematic review including virtually every informative study indicated that the dose-response gradient was slight and nonlinear [16]. However, limited by the significant heterogeneity between studies, this review neither gave overall estimates nor did it separate the estimates for current and former smokers. In contrast, we observed a clear dose-response relationship in both current and former smokers, compatible with the toxic effect of smoking on oocyte quantity. Experimental studies using animal models have shown that exposure to tobacco smoke causes follicle loss at all stages of folliculogenesis [36], and a dose-response trend exists for nicotine on impairment in follicle growth [37].

Evidence of the association between duration of smoking and age at menopause has been inconsistent [17,18,26]. A recent large-scale study reported a possible dose-response relationship between smoking duration and earlier menopause [18]. However, the analyses were performed in all smokers, and thus the specific associations of smoking duration among former or current smokers could not be examined. By definition, smoking duration is somewhat confounded with former/current smoking status, warranting separate analysis. Also, in that study [18], because age at menopause was categorized only into two categories of <50 and ≥50, the association with premature or early menopause could not be examined. We found that prolonged smoking was linked to earlier menopause, both in former and current smokers. The associations of smoking duration with premature and early menopause were much higher in current smokers than that in former smokers. The lowest BIC value indicates that duration of smoking is the strongest predictor of earlier age at menopause in this study.

Number of pack-years reflects a person’s cumulative cigarette exposure over time. Of the four cross-sectional studies that examined the association between pack-years and age at menopause [17,18,30,38], three suggested a slight dose-response relationship [17,18,38], with a higher number of pack-years associated with earlier menopause. However, all three studies used data from all smokers without stratifying by current or former smoking status. Our results showed that higher cumulative dose of smoking was strongly associated with earlier menopause in both current and former smokers.

Some studies found a dose-response relationship with younger age at starting to smoke and earlier age at menopause [17,18], while others did not detect a significant association [20,39]. We found that the earlier age at starting to smoke, the higher the risk of experiencing earlier menopause both among current and former smokers, especially for women who started smoking before age 15. Similar to one previous study [20], we found that the more recently women had quit smoking, the higher the risk of earlier menopause. This finding indicated that the residual influence of smoking on earlier menopause declined as years since quitting smoking increased.

Limitations

Some limitations need to be considered when interpreting these results. Current smoking status was determined at the baseline survey. For women who experienced menopause after baseline, their smoking status was measured at varying years before menopause. Thus, the possibility of measurement errors in smoking status may have arisen. Smoking, however, is a habit that is resistant to change. In studies including women who reported smoking status both before and after menopause (e.g., UK Biobank, NSHD, NCDS, SWAN, and SABRE), the concordance was over 83%, supporting the use of smoking exposure at baseline as a proxy for smoking status close to or after menopause. For example, when van Asselt and colleagues [26] repeated their analyses by defining current and former smokers at 5 years before menopause instead of 1 year, the estimated effects were similar. This suggests that among women with menopause after baseline in this study (the mean baseline age and menopausal age for these women were 46.0 and 51.5 years, respectively), our estimates reflect the effect of smoking exposure just before or close to menopausal age. Another limitation was that the influence of passive smoking, which may lead to earlier menopause [18], was not considered in this study; thus, when the never-smoking group was used as the reference without excluding passive smokers, the association might have been underestimated. However, SWAN study found that both baseline and time-varying passive smoking did not predict age at menopause [3], so we assume that the bias from not including passive smoking is small. Third, age at menopause was self-reported. However, because premature and early menopause are landmark health events, we consider major misclassification unlikely. Fourth, the original questions used to collect smoking data may not be exactly the same across studies, which may lead to some heterogeneity in the responses. Based on validation studies, smoking questions that have a reasonably standard format (e.g., questions about smoking status or duration of smoking) are not expected to impact the findings in any substantive way [40,41]. Finally, over 80% women are white in our study, which may limit the generalizability of the findings to other races.

Strengths of this study

The main strength of this study was the use of pooled individual-level data from 17 studies across different geographic regions and populations, which resulted in sufficient statistical power to perform both cross-sectional and prospective analyses in current and former smokers separately. The percentages of women with missing data on smoking status, BMI, education level, and race/ethnicity were relatively small (ranging from 1.3% to 5.4%). To our knowledge, it is the first study of this size to examine and quantify the dose-response relationships between intensity, duration, cumulative dose, timing of smoking, and age at menopause separately for former and current smokers. Also, the participant-level data in InterLACE enabled harmonizing variables using common definitions, coding and cut points, which is not usually possible with meta-analyses of published results.

Conclusion

Our pooled analysis provides robust evidence that both former and current smokers are at an increased risk of experiencing earlier menopause, but the risk is considerably lower for former smokers. We found significant relationships between the intensity, duration, cumulative dose, the timing of starting, and years since quitting smoking with the risk of earlier menopause in both current and former smokers. Duration and cumulative dose of smoking are good predictors of age at menopause. Current smokers with 15 or more years of smoking duration experience a 15-fold increased risk of premature and a 6-fold increased risk of early menopause. Given that 175 million women ages 15 or older are current smokers globally [42], our findings highlight the clear benefits for women of early smoking cessation to lower their excess risk of earlier menopause, thereby reducing the increased risks of chronic diseases (e.g., cardiovascular disease and osteoporosis) associated with both smoking and earlier menopause. Furthermore, smoking cessation itself reduces the risk of many other smoking-related diseases, such as lung cancer and chronic obstructive pulmonary disease. In our future research, we intend to quantify the direct and indirect effects (via age at menopause) of smoking on the risk of chronic diseases.

Supporting information

S1 Text. STROBE Statement—checklist of items that should be included in reports of observational studies.

https://doi.org/10.1371/journal.pmed.1002704.s001

(DOC)

S3 Text. Funding details of studies contributed to the InterLACE consortium.

https://doi.org/10.1371/journal.pmed.1002704.s003

(DOCX)

S1 Table. Cross-sectional associations between cigarette smoking and age at menopause, further adjusted age at menarche and parity in 14 studies (n = 194,368).

https://doi.org/10.1371/journal.pmed.1002704.s004

(DOCX)

S2 Table. Cross-sectional associations between cigarette smoking and age at menopause after excluding UK Biobank study (n = 69,217).

https://doi.org/10.1371/journal.pmed.1002704.s005

(DOCX)

S3 Table. Cross-sectional association between intensity, duration, age at start of smoking, and age at menopause-adjusted other smoking factors.

https://doi.org/10.1371/journal.pmed.1002704.s006

(DOCX)

S4 Table. Meta-analysis results from study-specific cross-sectional associations between cigarette smoking and age at menopause.

https://doi.org/10.1371/journal.pmed.1002704.s007

(DOCX)

S5 Table. Meta-analysis results from study-specific prospective associations between cigarette smoking and age at menopause.

https://doi.org/10.1371/journal.pmed.1002704.s008

(DOCX)

S6 Table. List of contacts for data access.

https://doi.org/10.1371/journal.pmed.1002704.s009

(DOCX)

Acknowledgments

The data on which this research is based were drawn from 17 observational studies that contributed to the InterLACE consortium. The funding details of each individual study are listed in S3 Text. All studies would like to thank the participants for volunteering their time to be involved in the respective studies. The findings and views in this paper are not necessarily those of the original studies or their respective funding agencies.

References

  1. 1. Henderson KD, Bernstein L, Henderson B, Kolonel L, Pike MC. Predictors of the timing of natural menopause in the Multiethnic Cohort Study. Am J Epidemiol. 2008;167(11):1287–94. pmid:18359953
  2. 2. Morabia A, Costanza MC. International variability in ages at menarche, first livebirth, and menopause. World Health Organization Collaborative Study of Neoplasia and Steroid Contraceptives. Am J Epidemiol. 1998;148(12):1195–205. pmid:9867266
  3. 3. Gold EB, Crawford SL, Avis NE, Crandall CJ, Matthews KA, Waetjen LE, et al. Factors related to age at natural menopause: longitudinal analyses from SWAN. Am J Epidemiol. 2013;178(1):70–83. pmid:23788671
  4. 4. Santoro N. Mechanisms of premature ovarian failure. Ann Endocrinol (Paris). 2003;64(2):87–92. pmid:12773939
  5. 5. Shifren JL, Gass ML, NAMS Recommendations for Clinical Care of Midlife Women Working Group. The North American Menopause Society recommendations for clinical care of midlife women. Menopause (New York, NY). 2014;21(10):1038–62. pmid:25225714
  6. 6. Luborsky JL, Meyer P, Sowers MF, Gold EB, Santoro N. Premature menopause in a multi-ethnic population study of the menopause transition. Hum Reprod. 2003;18(1):199–206. pmid:12525467
  7. 7. Coulam CB, Adamson SC, Annegers JF. Incidence of premature ovarian failure. Obstet Gynecol. 1986;67(4):604–6. pmid:3960433
  8. 8. Muka T, Oliver-Williams C, Kunutsor S, Laven JS, Fauser BC, Chowdhury R, et al. Association of Age at Onset of Menopause and Time Since Onset of Menopause With Cardiovascular Outcomes, Intermediate Vascular Traits, and All-Cause Mortality: A Systematic Review and Meta-analysis. JAMA cardiology. 2016;1(7):767–76. pmid:27627190
  9. 9. Shuster LT, Rhodes DJ, Gostout BS, Grossardt BR, Rocca WA. Premature menopause or early menopause: long-term health consequences. Maturitas. 2010;65(2):161–6. pmid:19733988
  10. 10. Brand JS, Onland-Moret NC, Eijkemans MJ, Tjonneland A, Roswall N, Overvad K, et al. Diabetes and onset of natural menopause: results from the European Prospective Investigation into Cancer and Nutrition. Hum Reprod. 2015;30(6):1491–8. pmid:25779698
  11. 11. LeBlanc ES, Kapphahn K, Hedlin H, Desai M, Parikh NI, Liu S, et al. Reproductive history and risk of type 2 diabetes mellitus in postmenopausal women: findings from the Women's Health Initiative. Menopause (New York, NY). 2017;24(1):64–72. pmid:27465714
  12. 12. Jungari SB, Chauhan BG. Prevalence and Determinants of Premature Menopause among Indian Women: Issues and Challenges Ahead. Health Soc Work. 2017;42(2):79–86. pmid:28340231
  13. 13. Zhu D, Chung HF, Pandeya N, Dobson AJ, Kuh D, Crawford SL, et al. Body mass index and age at natural menopause: an international pooled analysis of 11 prospective studies. Eur J Epidemiol. 2018;33(8):699–710. pmid:29460096
  14. 14. Mishra GD, Pandeya N, Dobson AJ, Chung HF, Anderson D, Kuh D, et al. Early menarche, nulliparity and the risk for premature and early natural menopause. Hum Reprod. 2017;32(3):679–86. pmid:28119483
  15. 15. Schoenaker DA, Jackson CA, Rowlands JV, Mishra GD. Socioeconomic position, lifestyle factors and age at natural menopause: a systematic review and meta-analyses of studies across six continents. Int J Epidemiol. 2014;43(5):1542–62. pmid:24771324
  16. 16. Parente RC, Faerstein E, Celeste RK, Werneck GL. The relationship between smoking and age at the menopause: A systematic review. Maturitas. 2008;61(4):287–98. pmid:19019585
  17. 17. Cramer DW, Harlow BL, Xu H, Fraer C, Barbieri R. Cross-sectional and case-controlled analyses of the association between smoking and early menopause. Maturitas. 1995;22(2):79–87. pmid:8538488
  18. 18. Hyland A, Piazza K, Hovey KM, Tindle HA, Manson JE, Messina C, et al. Associations between lifetime tobacco exposure with infertility and age at natural menopause: the Women's Health Initiative Observational Study. Tob Control. 2016;25(6):706–14. pmid:26666428
  19. 19. Mikkelsen TF, Graff-Iversen S, Sundby J, Bjertness E. Early menopause, association with tobacco smoking, coffee consumption and other lifestyle factors: a cross-sectional study. BMC Public Health. 2007;7:149. pmid:17617919
  20. 20. Willett W, Stampfer MJ, Bain C, Lipnick R, Speizer FE, Rosner B, et al. Cigarette smoking, relative weight, and menopause. Am J Epidemiol. 1983;117(6):651–8. pmid:6859020
  21. 21. Tziomalos K, Charsoulis F. Endocrine effects of tobacco smoking. Clin Endocrinol (Oxf). 2004;61(6):664–74. pmid:15579179
  22. 22. Mishra GD, Anderson D, Schoenaker DA, Adami HO, Avis NE, Brown D, et al. InterLACE: A New International Collaboration for a Life Course Approach to Women's Reproductive Health and Chronic Disease Events. Maturitas. 2013;74(3):235–40. pmid:23313437
  23. 23. Mishra GD, Chung HF, Pandeya N, Dobson AJ, Jones L, Avis NE, et al. The InterLACE study: Design, data harmonization and characteristics across 20 studies on women's health. Maturitas. 2016;92:176–85. pmid:27621257
  24. 24. Borooah VK. Logit and probit: Ordered and multinomial models: Sage; 2002.
  25. 25. Palmer JR, Rosenberg L, Wise LA, Horton NJ, Adams-Campbell LL. Onset of natural menopause in African American women. American journal of public health. 2003;93(2):299–306. pmid:12554590
  26. 26. van Asselt KM, Kok HS, van Der Schouw YT, Grobbee DE, te Velde ER, Pearson PL, et al. Current smoking at menopause rather than duration determines the onset of natural menopause. Epidemiology. 2004;15(5):634–9. pmid:15308964
  27. 27. Gold EB, Bromberger J, Crawford S, Samuels S, Greendale GA, Harlow SD, et al. Factors associated with age at natural menopause in a multiethnic sample of midlife women. Am J Epidemiol. 2001;153(9):865–74. pmid:11323317
  28. 28. Hayatbakhsh MR, Clavarino A, Williams GM, Sina M, Najman JM. Cigarette smoking and age of menopause: a large prospective study. Maturitas. 2012;72(4):346–52. pmid:22695707
  29. 29. Cooper GS, Sandler DP, Bohlig M. Active and passive smoking and the occurrence of natural menopause. Epidemiology. 1999;10(6):771–3. pmid:10535795
  30. 30. Blanck HM, Marcus M, Tolbert PE, Schuch C, Rubin C, Henderson AK, et al. Time to menopause in relation to PBBs, PCBs, and smoking. Maturitas. 2004;49(2):97–106. pmid:15474753
  31. 31. Welch RM, Levin W, Kuntzman R, Jacobson M, Conney AH. Effect of halogenated hydrocarbon insecticides on the metabolism and uterotropic action of estrogens in rats and mice. Toxicol Appl Pharmacol. 1971;19(2):234–46. pmid:4105824
  32. 32. Elsherbiny ME, Brocks DR. The ability of polycyclic aromatic hydrocarbons to alter physiological factors underlying drug disposition. Drug Metab Rev. 2011;43(4):457–75. pmid:21823901
  33. 33. Baron JA, La Vecchia C, Levi F. The antiestrogenic effect of cigarette smoking in women. Am J Obstet Gynecol. 1990;162(2):502–14. pmid:2178432
  34. 34. Michnovicz JJ, Hershcopf RJ, Naganuma H, Bradlow HL, Fishman J. Increased 2-hydroxylation of estradiol as a possible mechanism for the anti-estrogenic effect of cigarette smoking. The New England journal of medicine. 1986;315(21):1305–9. pmid:3773953
  35. 35. Khaw KT, Tazuke S, Barrett-Connor E. Cigarette smoking and levels of adrenal androgens in postmenopausal women. The New England journal of medicine. 1988;318(26):1705–9. pmid:2967432
  36. 36. Dechanet C, Anahory T, Mathieu Daude JC, Quantin X, Reyftmann L, Hamamah S, et al. Effects of cigarette smoking on reproduction. Hum Reprod Update. 2011;17(1):76–95. pmid:20685716
  37. 37. Liu Y, Li GP, Rickords LF, White KL, Sessions BR, Aston KI, et al. Effect of nicotine on in vitro maturation of bovine oocytes. Anim Reprod Sci. 2008;103(1–2):13–24. pmid:17178443
  38. 38. Torgerson DJ, Avenell A, Russell IT, Reid DM. Factors associated with onset of menopause in women aged 45–49. Maturitas. 1994;19(2):83–92. pmid:7968648
  39. 39. Yang HJ, Suh PS, Kim SJ, Lee SY. Effects of Smoking on Menopausal Age: Results From the Korea National Health and Nutrition Examination Survey, 2007 to 2012. J Prev Med Public Health. 2015;48(4):216–24. pmid:26265667
  40. 40. Patrick DL, Cheadle A, Thompson DC, Diehr P, Koepsell T, Kinne S. The validity of self-reported smoking: a review and meta-analysis. American journal of public health. 1994;84(7):1086–93. pmid:8017530
  41. 41. Wong SL, Shields M, Leatherdale S, Malaison E, Hammond D. Assessment of validity of self-reported smoking status. Health Rep. 2012;23(1):47–53. pmid:22590805
  42. 42. Drope J, Schluger NW, Cahn Z, Drope J, Hamill S, Islami F, et al. The tobacco atlas, 6th edn. 2018.