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. Author manuscript; available in PMC: 2022 Jan 15.
Published in final edited form as: Environ Pollut. 2020 Dec 3;269:116216. doi: 10.1016/j.envpol.2020.116216

Long-term exposure to particulate matter and roadway proximity with age at natural menopause in the Nurses’ Health Study II Cohort

Huichu Li a, Jaime E Hart a,b, Shruthi Mahalingaiah a, Rachel C Nethery c, Elizabeth Bertone-Johnson d,e, Francine Laden a,b,f
PMCID: PMC7785633  NIHMSID: NIHMS1653577  PMID: 33316492

Abstract

Evidence has shown associations between air pollution and traffic-related exposure with accelerated aging, but no study to date has linked the exposure with age at natural menopause, an important indicator of reproductive aging. In this study, we sought to examine the associations of residential exposure to ambient particulate matter (PM) and distance to major roadways with age at natural menopause in the Nurses’ Health Study II (NHS II), a large, prospective female cohort in US. A total of 105,996 premenopausal participants in NHS II were included at age 40 and followed through 2015. Time-varying residential exposures to PM10, PM2.5-10, and PM2.5 and distance to roads was estimated. We calculated hazard ratios (HR) and 95% confidence intervals (CIs) for natural menopause using Cox proportional hazard models adjusting for potential confounders and predictors of age at menopause. We also examined effect modification by region, smoking, body mass, physical activity, menstrual cycle length, and population density. There were 64,340 reports of natural menopause throughout 1,059,229 person-years of follow-up. In fully adjusted models, a 10 μg/m3 increase in the cumulative average exposure to PM10 (HR: 1.02, 95% CI: 1.00, 1.04), PM2.5-10 (HR: 1.03, 95% CI: 1.00, 1.05), and PM2.5 (HR: 1.03, 95% CI:1.00, 1.06) and living within 50 m to a major road at age 40 (HR: 1.03, 95%CI: 1.00, 1.06) were associated with slightly earlier menopause. No statistically significant effect modification was found, although the associations of PM were slightly stronger for women who lived in the West and for never smokers. To conclude, we found exposure to ambient PM and traffic in midlife was associated with slightly earlier onset of natural menopause. Our results support previous evidence that exposure to air pollution and traffic may accelerate reproductive aging.

Keywords: air pollution, particulate matter, roadway proximity, age at menopause, reproductive aging

Graphical Abstract

graphic file with name nihms-1653577-f0001.jpg

Introduction

Natural menopause is clinically defined as the cessation of menstruation for at least 12 consecutive months (World Health Organization, 1996). It is a natural consequence of ovarian aging and age related oocyte depletion. The current median age at natural menopause in the US has been reported to be approximately 51-52 years and it varies by geographic region and by race/ethnicity (Gold et al., 2013; McKnight et al., 2011; Morabia and Costanza, 1998; Thomas et al., 2001). So far, it has been suggested that genetics and a number of modifiable factors, including smoking, body weight, diet, physical activity, reproductive health behaviors, and socioeconomic status, may explain part of this variation (Gold, 2011; Gold et al., 2013; He et al., 2009; He and Murabito, 2014; Lujan-Barroso et al., 2018; Nagel et al., 2005; Pyun et al., 2014; Schoenaker et al., 2014; Zhu et al., 2018). However, evidence on the possible influence of ambient environment exposure with age at natural menopause remains scarce and only one recent study in Europe investigated the association between an environmental exposure, surrounding greenness, and age at menopause (Triebner et al., 2019).

Previous studies have shown that air pollution and traffic-related exposures were associated with shorter telomere length, a biomarker of aging (Martens and Nawrot, 2018, 2016; Miri et al., 2019). Although the mechanisms are still not clear, two studies have linked shorter telomere length with earlier age at menopause in women (Gray et al., 2014; Shenassa and Rossen, 2015). However, to the best of our knowledge, no study has examined the association of air pollution and traffic exposure with age at natural menopause. Our primary objective was to investigate these associations in a large US prospective cohort study – the Nurses’ Health Study II (NHS II). We compared the associations in different exposure time windows and explored whether the associations were modified by lifestyle factors that have been previously associated with age at menopause.

Material and Methods

Study Population

The NHS II cohort is an ongoing prospective cohort including 116,429 female registered nurses who completed the baseline questionnaire in 1989. At baseline, the participants were between 25 to 42 years of age and from 14 states in the US (California, Connecticut, Indiana, Iowa, Kentucky, Massachusetts, Michigan, Missouri, New York, North Carolina, Ohio, Pennsylvania, South Carolina, and Texas). However, they have resided in all 50 states and the District of Columbia since the mid-1990’s. A follow-up questionnaire has been mailed to the participant’s residential address every 2 years collecting information on major health risk factors and health outcomes. For this analysis, we included women who were premenopausal, still responding to the questionnaires, and had at least 1 geocoded residential address in the continental US after age 40. Women who reported a hysterectomy, oophorectomy, or cancer (except basal and squamous cell carcinoma), or had died before age 40 were excluded. This study was approved by the Institutional Review Board of Brigham and Women’s Hospital and the Human Subjects Committee of the Harvard T.H. Chan School of Public Health, and informed consent was implied by return of the questionnaires.

Outcome Assessment

Menopausal status, reasons for menopause (natural, surgical, chemotherapy or radiation), and age at menopause were self-reported by the NHS II participants in every questionnaire. For this analysis, we considered menopause due to natural causes (natural menopause) as the outcome of interest. To avoid outcome misclassification, all self-reported menopausal status and reasons were verified by their consistency on two consecutive questionnaires. If a participant did not indicate age at menopause in the questionnaire, we used her age at the return of that questionnaire as a surrogate. Participants who reported menopause due to surgery, chemotherapy or radiation, or did not report the reason of menopause were censored at the time of report. We also censored participants who failed to report menopausal status in two subsequent questionnaires after the last valid report of being premenopausal, who reported hysterectomy or oophorectomy (both bilateral and unilateral), women who died, and women who were diagnosed with cancer (except basal and squamous cell carcinoma). Death was confirmed by next-of-kin and postal authorities or by searching the National Death Index. Cancer diagnosis was identified from self-report and confirmed via medical records or from cancer registries.

Exposure Assessment

The residential address of each participant were geocoded to obtain latitude and longitude. We assumed that each woman who moved did so at the beginning of the questionnaire cycle. Monthly levels of particulate matter (PM) with an aerodynamic diameter less than or equal to 10 microns (PM10), between 2.5 to 10 microns (PM2.5-10), and less than or equal to 2.5 microns (PM2.5) at residential addresses in the continental US were obtained from spatio-temporal prediction models (Yanosky et al., 2014). These models used monthly monitoring data from several monitoring networks across the US and geospatial predictors, including road proximity, elevation, urban land use, population density, point sources of PM, and meteorological variables such as monthly average wind speed, temperature, and total precipitation, to predict monthly PM10 and PM2.5 between January 1989 to December 2007. Monthly PM2.5-10 was calculated by subtracting the predicted PM2.5 from PM10 at each location. Cross-validation results showed good predictive performance with strong agreement between the predictions and observed values. We used these monthly predictions to calculate the annual means for PM10, PM2.5-10, and PM2.5, respectively, at each address. Predictions for 2007 were carried forward for all subsequent years. We created 3 time-varying long-term exposure metrics for each PM fraction: the cumulative average from age 40 with a 1 year lag to represent exposures during mid-adulthood; the average from age 40 to 45 to represent exposures in early mid-adulthood (cumulative average from age 40 with a 1 year lag when participants were still under 45); and the annual average in the previous year to represent recent exposures.

Roadway proximity was used to represent traffic-related exposures and was measured by distance to major roadways for each residential address using the ESRI StreetMap Pro 2007 road segment data. We defined major roadways using the US Census Feature Class Codes: A1 (primary roads, typically interstate highways with limited access, division between the opposing directions of traffic, and defined exits), A2 (non-interstate highways and major roads without limited access), and A3 (secondary roads usually with more than 2 lanes). Distance to the largest (A1), the two largest (A1-A2), and all (A1-A3) road segments were calculated at age 40 and in the previous year to represent early mid-adulthood and recent exposure, and was categorized by 0-49 m, 50-199 m, 200-499 m, and 500 m or above, based on the distribution of exposure in the cohort and exposure studies (Karner et al., 2010).

Covariates

Time-varying and invariant information on potential confounders were collected from the follow-up questionnaires every 2 years (every 4 years for diet and physical activity). For this analysis, we considered covariates that have been suggested to be associated with age at natural menopause and/or with exposure to PM or roadway proximity: body mass index (BMI, kg/m2), smoking status (never, past, current) and intensity (< 25 or ≥ 25 cigarettes/day), physical activity (metabolic equivalent task (MET) hours/week), marital status, alcohol consumption (0 g/day, 0.1-4.9 g/day, 5.0-14.9 g/day, and ≥ 15.0 g/day), US Census Bureau regions of residence (Northeast, Midwest, West, and South), history of rotating shift work (never, past, current), oral contraceptive use (never, past, current), pre- or peri-menopausal use of hormone therapy (never, past, current), parity (nulliparous, 1-2, 3 or more full-term pregnancies), history of breastfeeding (less than 1 month, 1-12 months, 13-24 months, and more than 24 months), age at first birth (under 20, 20-25, 26-30, and ≥ 31 years old). Overall diet quality was measured via the 2010 Alternate Healthy Eating Index (AHEI) (Chiuve et al., 2012). We also controlled for diagnosis of uterine fibroids and endometriosis, which are major indicators for receiving hysterectomy and oophorectomy, to reduce bias by censoring on the report of both procedures (Whiteman et al., 2008; Wright et al., 2013). Neighborhood socioeconomic status was measured by Census tract-level population density, median income, and median home values. Race/ethnicity and age at menarche were also included as time invariant covariates. Information on menstrual cycle length at age 18-22 was provided at baseline. All missing values were modeled using missing indicators. We further assessed exposures to residential greenness measured by the Normalized Difference Vegetation Index (NDVI) in July of each year from the Advanced Very High Resolution Radiometer satellite at 1 km resolution.

Statistical analysis

We calculated person-years for all participants from age 40 until self-reported natural menopause, censoring, or the return of the 2015 questionnaire, whichever came first. We further excluded participants without addresses geocoded to the street level for the analysis of roadway proximity. Then we computed the hazard ratios (HRs) and 95% confidence intervals (CIs) of natural menopause for a 10 μg/m3 increase in each PM fraction and in each exposure time window, separately, using a time-varying Cox proportional hazard model with age as the time scale. All models were stratified by participants’ age in 1989 to control for possible time trends. After assessing the correlations between size fractions, we fitted a two-pollutant model including PM2.5-10 and PM2.5 for each exposure time window. Associations of roadway proximity with age at natural menopause were also computed as HRs and 95% CIs for both continuous and categorical variables. For each exposure measure, we fitted a basic model with age and calendar years only, a parsimonious model additionally adjusting for BMI, smoking, race/ethnicity, region, and socioeconomic status, and a full model adjusting for all covariates. Non-linear exposure-response relationships were examined using cubic splines for all continuous exposure measures. Hazard ratios greater than 1 indicate that the exposure is associated with earlier menopause, and HRs less than 1 indicate an association with later menopause. We also predicted the median age at menopause from the models and compared the predicted menopausal age between PM levels at the 25th to 75th percentile, and between the nearest category (0-49 m) of distance to major roads to the furthest category (≥ 500 m).

To examine the robustness of our findings, we conducted sensitivity analyses by ending the follow-up period in 2009 to avoid carryforward of exposure predictions; restricting to women who never changed their residential addresses; and restricting to women who never reported using hormone therapy and who never used oral contraceptives after age 40. As air pollution and greenspace exposure were negatively correlated and one previous study has linked residential greenness with later menopause, we further adjusted analyses for residential greenness (Triebner et al., 2019). To control for possible selection bias from exclusion before age 40, we calculated stabilized inverse probability weights of being included at age 40 using exposures and covariates measured at the baseline year (1989), and incorporated these personal weights in the Cox model, respectively.

We also explored effect modification by region, smoking status, BMI (< 25, 25-29.9, and ≥ 30 kg/m2), physical activity (< 3, 3-27, and > 27 MET hours/week), menstrual cycle length at age 18-22 (≤ 25 days, 26-31 days, 32-39 days, and ≥ 40 days or irregular), and Census tract level population density (< 1,000 and ≥ 1,000 people per km2) in separate models, and statistical significance was examined by adding multiplicative interaction terms to the model. All statistical tests were 2-sided with an α level of 0.05. All analyses were conducted in SAS 9.4.

Results

Characteristics of all participants throughout follow-up are shown in Table 1. Among the 105,996 NHS II participants who were still premenopausal at age 40, 66% were never smokers, 83% had at least 1 full-term pregnancy, and 74% never used hormone therapy during follow-up. The mean [± standard deviation (SD)] levels of cumulative average PM10, PM2.5-10, and PM2.5 were 22.6 (± 6.5), 9.0 (± 4.7), and 13.6 (± 3.1) μg/m3, respectively. Within each time window, PM10 was highly correlated with PM2.5-10 (Spearman r=0.85-0.86) and moderately with PM2.5 (Spearman r=0.69-0.71), while the correlation between PM2.5-10 and PM2.5 was low (Spearman r=0.26-0.29). We also observed very high correlations for PM10, PM2.5-10, and PM2.5, respectively, between exposure windows (Spearman r=0.90-0.99).

Table 1.

Characteristics of 105,996 participants aged 40 or above in the Nurses’ Health Study II (1989-2015) overall and by quartiles of cumulative PM10 exposure

Characteristics Overall PM10
1st quartile		 PM10
2nd quartile		 PM10
3rd quartile		 PM10
4th quartile
US Census region of residence
Northeast 35 57 39 26 14
Midwest 32 25 28 38 38
West 16 9 9 9 36
South 18 9 24 26 12
Race/ethnicity
Nonwhite 5 3 4 6 8
White 93 96 94 93 89
Missing 2 1 2 2 2
Ever changed address after age 40 70 73 69 68 68
Age at menarche (years)
Under 10 7 7 7 8 8
11-13 74 74 74 74 74
14-16 18 18 18 18 18
17 and above 1 1 1 1 1
Menstrual cycle length at age 18-22
≤ 25 days 11 11 12 11 11
26-31 days 62 63 62 62 62
32-39 days 14 14 14 13 13
≥ 40 days or irregular 7 7 7 7 7
Missing 6 5 5 6 7
Hormone therapy use
Never user 74 75 74 74 73
Past user 14 13 14 14 14
Current user 12 11 11 12 12
Missing 1 1 1 1 1
Cigarette smoking
Never smoker 66 64 65 66 67
Past smoker, < 25 cigarettes/day 22 23 22 21 20
Past smoker, ≥ 25 cigarettes/day 3 4 3 3 3
Past smoker, unknown intensity 1 1 1 1 1
Current smoker, < 25 cigarettes/day 7 7 7 8 7
Current smoker, ≥ 25 cigarettes/day 1 1 1 1 1
Current smoker, unknown intensity 0.2 0.2 0.2 0.3 0.2
Body mass index (kg/m2)
< 21 12 12 12 12 12
21-24.9 31 32 31 30 30
25-29.9 24 25 24 24 24
≥ 30 21 21 21 22 22
Missing 11 11 11 12 12
Physical Activity (MET-hours/week)
< 3 18 17 18 18 18
3-9 22 22 22 23 22
9-18 21 21 21 21 21
18-27 13 13 13 13 13
27-42 12 13 12 12 12
≥ 42 14 15 14 13 14
Age at first birth (years)
Under 20 7 7 6 6 7
20-25 28 30 29 28 24
26-30 31 33 31 32 29
> 30 16 16 16 16 17
Missing/Nulliparous 18 14 17 18 23
Parity
Nulliparous 17 14 16 17 22
1-2 full term pregnancies 53 53 53 54 53
3 or more full term pregnancies 30 33 30 29 26
History of breastfeeding (months)
< 1 13 13 13 13 11
1-12 24 24 25 25 24
13-24 18 20 19 18 17
> 24 17 20 17 16 16
Missing/Nulliparous 27 23 27 28 32
Oral contraceptive use
Never user 14 14 14 14 14
Past user 79 79 78 79 78
Current user 7 6 7 7 8
Married 80 85 82 80 75
History of rotating shift work
Never 30 31 29 29 31
Past 63 62 64 63 61
Current 7 7 7 7 7
Uterine fibroids 16 16 16 16 16
Endometriosis 6 6 6 6 6
Alternate heathy eating index (AHEI) 53.5 (12.1) 53.8 ± 12.1 53.3 ± 12.1 52.9 ± 12.0 54.1 ±12.1
Missing AHEI 8 8 8 9 9
Alcohol Consumption (g/day)
0 35 34 35 36 37
0.1-4.9 32 33 32 32 31
5.0-14.9 17 19 18 17 17
≥15.0 7 7 7 6 7
Missing 8 8 8 9 9
Census tract population density
1st quartile 24 43 25 18 11
2nd quartile 25 30 27 26 17
3rd quartile 25 17 25 31 28
4th quartile 25 9 22 25 43
Census tract median income
1st quartile 23 23 22 22 24
2nd quartile 24 24 24 25 23
3rd quartile 25 26 25 26 25
4th quartile 27 26 29 27 27
Census tract median home values
1st quartile 23 24 23 23 22
2nd quartile 24 25 25 26 21
3rd quartile 25 27 25 27 24
4th quartile 27 24 27 24 33
Cumulative average PM10 (μg/m3) 22.6 ± 6.5 16.6 ± 2.8 20.5 ± 2.6 23.4 ± 2.9 29.9 ± 7.0
Cumulative average PM2.5-10 (μg/m3) 9.0 ± 4.7 5.5 ± 2.0 7.4 ± 2.4 9.1 ± 2.7 14.1 ± 5.4
Cumulative average PM2.5 (μg/m3) 13.6 ± 3.1 11.2 ± 2.2 13.2 ± 2.2 14.4 ± 2.5 15.8 ± 3.3
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Abbreviations: PM10, particulate matter with an aerodynamic diameters less than or equal to 10 microns; PM2.5-10, particulate matter with an aerodynamic diameters between 2.5 to 10 microns; PM2.5, particulate matter with an aerodynamic diameters less than or equal to 2.5 microns; MET, metabolic equivalent

Note: Values are means ± SD for continuous variables, percentages for categorical variables, and are standardized to calendar year distribution of the study population.

A total of 64,340 reports of natural menopause were identified from 105,996 eligible NHS II participants and 1,059,229 person-years of follow-up. Exclusions before age 40 and censoring during the follow-up are showed in Table S1. The median age at natural menopause was 51 years old. No statistically significant non-linear exposure-response relationships were observed (p for deviations from linearity=0.33-0.97). As shown in Table 2, after adjusting for all covariates, we found residential PM10, PM2.5-10, and PM2.5 were associated with a slightly earlier onset of menopause. For example, the HRs and 95% CIs of natural menopause for a 10 μg/m3 increase of the cumulative averages of PM10, PM2.5-10, and PM2.5 was 1.02 (95% CI=1.00, 1.04), 1.03 (95% CI=1.00, 1.05), and 1.03 (95% CI=1.00, 1.06), respectively. Similar associations were also found for exposures to all three PM fractions in age 40-45. Hazard ratios from models with both PM2.5-10 and PM2.5 were similar to the single pollutant models. The predicted median age at natural menopause among women with cumulative average PM10, PM2.5-10, and PM2.5 at the 75th percentile (PM10: 25.4 μg/m3, PM2.5-10: 11.0 μg/m3, and PM2.5: 15.6 μg/m3) was 0.6, 0.6, and 0.4 months earlier compared to those with exposure levels at the 25th percentile (PM10: 18.4 μg/m3, PM2.5-10: 5.8 μg/m3, and PM2.5: 11.5 μg/m3), respectively.

Table 2.

Hazard ratios (HRs) and 95% confidence intervals (CIs) for residential PM10, PM10-2.5, and PM2.5 with natural menopause in Nurses’ Health Study II participants aged 40 or above in 1989-2015 (n=105,996)

Events, n Person-years HR (95% CI) a
Basic b Parsimonious c Full d
Single pollutant models
Cumulative average
PM10 64,340 1,059,229 1.01 (1.00, 1.03) 1.02 (1.01, 1.04) 1.02 (1.00, 1.04)
PM2.5-10 64,340 1,059,229 1.01 (0.99, 1.03) 1.04 (1.01, 1.06) 1.03 (1.00, 1.05)
PM2.5 64,340 1,059,229 1.02 (0.99, 1.05) 1.03 (0.99, 1.06) 1.03 (1.00, 1.06)
Age 40-45
PM10 64,340 1,059,229 1.01 (1.00, 1.03) 1.03 (1.01, 1.04) 1.02 (1.01, 1.04)
PM2.5-10 64,340 1,059,229 1.01 (1.00, 1.03) 1.04 (1.01, 1.06) 1.03 (1.01, 1.05)
PM2.5 64,340 1,059,229 1.02 (0.99, 1.05) 1.03 (1.00, 1.06) 1.03 (1.00, 1.06)
Previous year
PM10 64,340 1,059,229 1.01 (0.99, 1.02) 1.02 (1.00, 1.03) 1.02 (1.00, 1.03)
PM2.5-10 64,340 1,059,229 1.00 (0.99, 1.02) 1.02 (1.00, 1.04) 1.02 (0.99, 1.04)
PM2.5 64,340 1,059,229 1.02 (0.99, 1.05) 1.02 (0.99, 1.05) 1.02 (0.99, 1.06)
Two-pollutant models
Cumulative average
PM2.5-10 64,340 1,059,229 1.01 (0.99, 1.03) 1.03 (1.01, 1.06) 1.02 (1.00, 1.05)
PM2.5 64,340 1,059,229 1.02 (0.99, 1.05) 1.01 (0.98, 1.05) 1.02 (0.99, 1.05)
Age 40-45
PM2.5-10 64,340 1,059,229 1.01 (0.99, 1.03) 1.04 (1.01, 1.06) 1.02 (1.00, 1.05)
PM2.5 64,340 1,059,229 1.01 (0.99, 1.04) 1.01 (0.98, 1.04) 1.02 (0.99, 1.05)
Previous year
PM2.5-10 64,340 1,059,229 1.00 (0.98, 1.02) 1.02 (0.99, 1.04) 1.01 (0.99, 1.04)
PM2.5 64,340 1,059,229 1.02 (0.99, 1.05) 1.01 (0.98, 1.05) 1.02 (0.98, 1.05)
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Abbreviations: HR, hazard ratio; CI, confidence interval; PM10, particulate matter with an aerodynamic diameters less than or equal to 10 microns; PM2.5-10, particulate matter with an aerodynamic diameters between 2.5 to 10 microns; PM2.5, particulate matter with an aerodynamic diameters less than or equal to 2.5 microns.

a

Per 10 μg/m3 increase. Hazard ratios greater than 1 indicate that the exposure is associated with earlier menopause, and HRs less than 1 indicate an association with later menopause.

b

Adjusted for age and calendar years.

c

Additionally adjusted for body mass index, smoking status, race/ethnicity, region, and Census tract median income, median home values, and population density

d

Additionally adjusted for physical activity, parity, age at first birth, history of breastfeeding, female hormone use, oral contraceptives use, history of rotating shift work, alternate healthy eating index, alcohol intake, marital status, diagnosis of endometriosis or uterine fibroids, and age at menarche

There were 53,526 reports of natural menopause over 892,262 person-years of follow-up in 91,343 NHS II participants for the analysis of roadway proximity. Our fully adjusted model suggested women who lived within 50 m from A1-A3 roads at age 40 and in the previous year had earlier onset of menopause compared to those who lived 500 m or more from the roads (age 40: HR=1.03, 95%CI=1.00, 1.06; previous year: HR=1.02, 95%CI=0.99, 1.05). Similar HRs were also observed for distance to A1-A2 roads, although they were not statistically significant (Table 3). The predicted median age at menopause was 1 month earlier among women who lived within 50 m to all major roads at age 40 compared to those who lived more than 500 m away from such roads.

Table 3.

Hazard ratios (HR) and 95% confidence intervals (CIs) for residential distance to major roadways with natural menopause in the Nurses’ Health Study II participants aged 40 and above in 1989-2015 (n=91,343)

Distance to roads Events, n Person-years HR (95% CI) a
Basic b Parsimonious c Full d
Age 40
A1
0-49 m 88 1,516 0.97 (0.79, 1.19) 0.95 (0.77, 1.17) 0.95 (0.77, 1.17)
50-199 m 1,396 21,871 1.06 (1.00, 1.11) 1.05 (0.99, 1.11) 1.03 (0.98, 1.09)
200-499 m 3,740 60,419 0.99 (0.96, 1.02) 0.99 (0.96, 1.02) 0.98 (0.95, 1.01)
≥ 500 m 48,302 808,456 Reference Reference Reference
Continuous (per 100 m) 53,526 892,262 1.01 (0.99, 1.02) 1.01 (0.99, 1.02) 1.00 (0.99, 1.01)
A1-A2
0-49 m 616 10,223 1.03 (0.95, 1.12) 1.03 (0.95, 1.11) 1.03 (0.95, 1.11)
50-199 m 3,000 47,839 1.04 (1.00, 1.07) 1.02 (0.99, 1.06) 1.01 (0.98, 1.05)
200-499 m 7,165 116,790 1.00 (0.97, 1.02) 0.99 (0.97, 1.02) 0.99 (0.96, 1.01)
≥ 500 m 42,745 717,410 Reference Reference Reference
Continuous (per 100 m) 53,526 892,262 1.01 (1.00, 1.01) 1.00 (1.00, 1.01) 1.00 (0.99, 1.01)
A1-A3
0-49 m 6,487 105,018 1.05 (1.02, 1.09) 1.04 (1.01, 1.07) 1.03 (1.00, 1.06)
50-199 m 14,743 239,263 1.02 (1.00, 1.05) 1.01 (0.99, 1.04) 1.00 (0.98, 1.03)
200-499 m 17,770 295,746 1.00 (0.98, 1.03) 1.00 (0.98, 1.02) 0.99 (0.97, 1.02)
≥ 500 m 14,526 252,235 Reference Reference Reference
Continuous (per 100 m) 53,526 892,262 1.01 (1.00, 1.01) 1.01 (1.00, 1.01) 1.00 (1.00, 1.01)
Previous year
A1
0-49 m 80 1,422 1.02 (0.82, 1.28) 1.01 (0.81, 1.26) 0.99 (0.80, 1.24)
50-199 m 1,255 20,954 1.02 (0.96, 1.08) 1.01 (0.96, 1.07) 1.02 (0.96, 1.08)
200-499 m 3,441 57,457 1.01 (0.97, 1.04) 1.00 (0.97, 1.04) 0.99 (0.96, 1.03)
≥ 500 m 48,750 812,429 Reference Reference Reference
Continuous (per 100 m) 53,526 892,262 1.00 (0.99, 1.02) 1.00 (0.99, 1.02) 1.00 (0.99, 1.01)
A1-A2
0-49 m 590 10,037 1.03 (0.95, 1.12) 1.04 (0.95, 1.12) 1.03 (0.95, 1.12)
50-199 m 2,748 45,540 1.03 (0.99, 1.07) 1.02 (0.98, 1.06) 1.02 (0.98, 1.06)
200-499 m 6,623 111,701 1.01 (0.98, 1.03) 1.00 (0.97, 1.03) 1.00 (0.97, 1.02)
≥ 500 m 43,565 724,984 Reference Reference Reference
Continuous (per 100 m) 53,526 892,262 1.01 (1.00, 1.01) 1.00 (1.00, 1.01) 1.00 (1.00, 1.01)
A1-A3
0-49 m 6,028 100,564 1.04 (1.01, 1.07) 1.03 (1.00, 1.06) 1.02 (0.99, 1.05)
50-199 m 13,811 230,494 1.02 (1.00, 1.04) 1.01 (0.98, 1.03) 1.00 (0.98, 1.03)
200-499 m 17,152 290,598 1.00 (0.98, 1.02) 0.99 (0.97, 1.01) 0.99 (0.97, 1.01)
≥ 500 m 16,535 270,606 Reference Reference Reference
Continuous (per 100 m) 53,526 892,262 1.01 (1.00, 1.01) 1.00 (1.00, 1.01) 1.00 (1.00, 1.01)
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Abbreviations: HR, hazard ratio; CI, confidence interval.

a

Hazard ratios greater than 1 indicate that the exposure is associated with earlier menopause, and HRs less than 1 indicate an association with later menopause.

b

Adjusted for age and calendar years.

c

Additionally adjusted for body mass index, smoking status, race/ethnicity, region, and Census tract median income, median home values, and population density.

d

Additionally adjusted for physical activity, parity, age at first birth, history of breastfeeding, female hormone use, oral contraceptives use, history of rotating shift work, alternate healthy eating index, alcohol intake, marital status, diagnosis of endometriosis or uterine fibroids, and age at menarche.

Both PM and road proximity showed similar associations with natural menopause in sensitivity analyses when the follow-up was ended in 2009, and among women who never used oral contraceptives after age 40. Besides, the HRs were comparable to the main results when restricting the analysis to women who never used hormone therapy and who never changed their addresses after age 40, although the 95% CIs included the null except for PM among those who never moved. Additional adjustment for residential NDVI and for exclusion before age 40 showed similar HRs and 95%CIs with natural menopause. (Table S2 and S3).

We did not observe evidence of effect modification by BMI, physical activity, menstrual cycle length at age 18-22, and Census tract population density for any PM fraction in any time windows (p values for interaction=0.07-0.96). We observed associations of PM2.5 and PM10 with earlier menopause only among never smokers (except for the exposure in the previous year) (Figure 1). There was suggestive evidence of effect modification by region as women who lived in the West had greater and statistically significant HRs than women in other regions (Figure 1). No effect modification was observed for analyses of roadway proximity (p values for interaction=0.08-0.85).

Figure 1. Hazard ratios and 95% confidence intervals for the associations of residential exposure to PM10, PM2.5-10, and PM2.5 in different time windows with natural menopause by smoking status (A) and by region (B).

Figure 1

Open in a new tab

Abbreviations: HR, hazard ratio; CI, confidence interval; PM10, particulate matter with an aerodynamic diameters less than or equal to 10 microns; PM2.5-10, particulate matter with an aerodynamic diameters between 2.5 to 10 microns; PM2.5, particulate matter with an aerodynamic diameters less than or equal to 2.5 microns.

Note: Hazard ratios and 95% confidence intervals were calculated as per 10 μg/m3 increase of PM exposure and adjusted for age, calendar years, smoking, body mass index, physical activity, parity, age at first birth, histories of breastfeeding, female hormone use, oral contraceptives use, histories of rotating shift work, alternate healthy eating index, alcohol intake, marital status, diagnosis of endometriosis and uterine fibroids, race/ethnicity, age at menarche, regions, and Census tract median income, median home values, and population density.

Discussion

In this large, prospective, nationwide female cohort, we observed an modest association of long-term exposure to ambient particulate air pollution (PM10, PM2.5-10, and PM2.5) after age 40 with earlier onset of menopause. Similarly, we also found women who lived close to all major roadways (< 50 m) at age 40 and in the previous year had slightly earlier menopause compared to women living further away (≥ 500 m). Our results were robust in sensitivity analyses. In addition, slightly stronger associations of PM and earlier menopause were found among never smokers and among women who lived in the West; however, there was no evidence of effect modification by body weight, physical activity, and population density. No evidence of effect modification was observed for roadway proximity.

To our knowledge, this is the first study examining the associations between ambient air pollution and roadway proximity with age at natural menopause. An earlier age at menopause has been suggested to be associated with higher risk of cardiovascular diseases, osteoporosis, depression, and shorter life expectancy (Atsma et al., 2006; Hu et al., 1999; Jung et al., 2015; Kuh et al., 2016; Muka et al., 2016; Savonitto et al., 2018; Shuster et al., 2010; Sullivan et al., 2017; Xu et al., 2020). It is believed that natural menopause occurs when the number of follicles reduces to a critical threshold of around 1,000, while other evidence suggests that the timing of natural menopause is also determined by the rate of follicle atresia (Thomford et al., 1987). One study has examined the associations of residential PM2.5 exposures with antral follicle counts among women (mean age of 35) from a fertility clinic. This study found associations of a 2 μg/m3 increase of 3-month average PM2.5 with a 7.2% reduction of antral follicle counts, and this effect was comparable with the effect of a 2-year increase of age (Gaskins et al., 2019). Several studies in animals also observed PM or diesel exhaust from traffic were associated with significant reductions in ovarian reserve (Gai et al., 2017; Liao et al., 2020; Ogliari et al., 2013; Veras et al., 2009). A recent cross-sectional study showed inverse associations of PM exposure and road proximity with ovarian reserve hormone levels among 67 women (Abareshi et al., 2020). Ovarian follicle atresia is mainly driven by the cyclic change of hormone milieu in the ovary, and can also be triggered by inflammation and oxidative stress (Agarwal et al., 2012; Boots and Jungheim, 2015; Kaipia et al., 1996; Lu et al., 2018). One study among 820 NHS II participants showed associations of blood inflammatory markers and early menopause (menopause before age 45) (Bertone-Johnson et al., 2019). Abundant evidence suggests that PM and traffic related exhaust can promote systemic inflammation and oxidative stress (Brook et al., 2010; Mazzoli-Rocha et al., 2010; Rao et al., 2018). Other studies also demonstrated chemical components in particulate air pollution and diesel exhaust (e.g. polycyclic aromatic hydrocarbon) may induce oocyte toxicity and thus promote follicular atresia and arrest (Borman et al., 2000; Meek, 1998; Misaki et al., 2008; Mori et al., 2002; Oh et al., 2008; Takeda et al., 2004). However, more mechanistic evidence is needed, especially evidence focusing on potential mechanistic pathways (e.g. DNA damage, inflammation, and oxidative stress) linking PM exposure and oocyte survival, and also on exposure in midlife. Besides, as evidence has shown that endocrine disrupting chemicals can adversely affect follicle development and ovulation, it is important to understand whether PM and its endocrine disrupting constituents can induce similar effects and which stages are the most susceptible (Gore et al., 2015).

We observed consistent associations of slightly earlier menopause with exposure to PM10, PM2.5-10, and PM2.5 exposures at age 40-45, and with roadway proximity in age 40, a time period that is considered to be the late reproductive and early perimenopausal stage in women’s reproductive lifespan (Harlow et al., 2012). It has also been suggested that the rate of follicle atresia accelerates after age 37 in most women possibly as a consequence of accumulation of cellular damage and lower damage repair capacity with age (Djahanbakhch et al., 2007; Faddy, 2000; Faddy and Gosden, 1996; Wallace and Kelsey, 2010). Therefore, our results may indicate PM and traffic-related pollution could accelerate the already expedited follicle loss in early midlife. Meanwhile, when considering exposure in the previous year, only PM10 and road proximity were found to be associated with menopause. This may further indicate PM and traffic-related exposure could lead to excess atresia in addition to the accumulated aging-related damage in oocytes.

Although we observed statistically significant associations of PM and roadway proximity with menopause, these associations were quite modest when compared to the HRs of other potential predictors of age at natural menopause in the model (e.g. smoking) (Table S3). The predicted median age at menopause in our analysis was less than 1 month younger for women with PM exposure at the 75th percentile to the 25th percentile, while previous literature has suggested women who smoke had 1-2 years younger menopausal age (Gold, 2011) . On the other hand, we observed stronger and statistically significant associations of PM with earlier menopause in never smokers but not in past or current smokers. It is possible that the stronger association of smoking with earlier menopause masked the modest impact of PM. Besides, PM2.5 was shown to have slightly stronger associations with earlier menopause among women who lived in the West. This effect heterogeneity of PM2.5 was consistent with a cohort study in the US, which showed PM2.5 was associated with higher breast cancer risk only among women in the west and suggested this heterogeneity may be due to the differences in chemical compositions of PM2.5 by region (White et al., 2019).

Our study had a few notable limitations. First, we had to rely on self-reported menopause queried as whether the participants’ periods had ceased permanently in the questionnaire. This defining criterion of menopause was less accurate than the definition commonly used in other epidemiological studies as the cessation of menstruation for at least 12 consecutive months (World Health Organization, 1996). Besides, as this information was collected every two years, we had very limited ability to identify the exact timing of menopause, which would lead to increased measurement error, weakening our ability to detect effects. However, one previous validation study in the Nurses’ Health Study suggested the prospective assessment of menopausal age is quite robust, with a 98.8% consistency in self-reported menopause status and types of menopause and a mean difference of 0.06 years in reported age at natural menopause over a two-year period (Colditz et al., 1987). Second, exposure measurement error was possible. Although the predicted PM from our geospatial models revealed satisfactory agreement with real time monitoring data, there may be misclassification due to lack of information on the amount of time spent at home and the use of household air filtration by participants (Kioumourtzoglou et al., 2014). Additionally, because all exposure measurements were only available until 2007, it is likely that there is more measurement error in the subsequent years after as we carried the predictions forward. However, our sensitivity analysis ending the follow-up in 2009 showed very similar HRs. Third, the very high correlations between different exposure time windows for all PM fractions, and the high correlations of PM10 with both PM2.5-10 and PM2.5 may limit our ability to fully distinguish the actual critical effect time window for air pollution and age at menopause, especially between the cumulative average and exposure in age 40-45, and which PM fraction contributed most to the observed associations. In addition, as the exposure was calculated after age 40, associations of air pollution exposure in earlier life periods such as childhood and adolescent, were not examined. Early exposure may also contribute to left censoring in our data. Although our sensitivity analysis correcting for baseline exclusion supported robustness of the main findings, our earliest exposure measure may not fully capture early exposure, especially in potentially important developmental windows. We were also unable to examine the association of exposure to other major gaseous air pollutants with the outcome due to data availability, although road proximity may partly reflect exposures to nitric oxides and traffic noise. Finally, compared to the general female population in the US, our participants were mostly white, worked as health professionals, and had relatively high socioeconomic status. Therefore, our ability to examine potential disparities by race was limited and our results may only apply to white women with similar characteristics rather than the general female population in the US or in other countries with higher levels of air pollution.

Despite the limitations, this large nation-wide cohort provided us with sufficient power to detect a modest association of PM and road proximity with age at natural menopause. With more than 90% of the NHS II participants were pre-menopausal in 1989 and around 1% remained premenopausal in 2015, we were able to capture the entire menopausal transition for the majority of the participants over 20 years of follow-up. We had detailed information on the participants’ lifestyles, reproduction history, and health status updated biennially, which allowed adjustment of time-varying confounding in the analysis. Finally, with the high spatio-temporal resolution PM models and residential history updated continuously, we were able to examine time-varying exposures in different life stages in the analysis.

Conclusions

Exposure to higher levels of ambient particulate air pollution and living near to major roadways after age 40 were modestly associated with earlier onset of natural menopause in this large, prospective, and nationwide cohort of female nurses in US. Exposure from age 40-45, which corresponds to the late reproductive and early premenopausal stage, showed more consistent associations with earlier menopause compared to later exposure. We also observed some effect heterogeneity by smoking and by region. A previous study suggested a 3% increase of cardiovascular disease risk for a 1-year younger menopausal age, therefore, the observed associations of PM with age at menopause are unlikely be of important clinical significance (Hu et al., 1999). Nevertheless, our results were consistent with previous observations of air pollution and traffic with accelerated aging. As this is the first study of air pollution and traffic with age at natural menopause, more evidence is needed for confirmation.

Supplementary Material

1

Highlights.

  • Limited evidence on air pollution and traffic with reproductive aging.

  • We examined associations of these exposure and menopausal age in a large cohort.

  • Women with higher residential PM in midlife had slightly earlier menopause.

  • Women who lived closer to roads in early midlife had slightly earlier menopause.

  • Our finding supported air pollution and traffic may accelerate reproductive aging.

Acknowledgements

This work was supported by the National Institute of Health [grant numbers U01 CA176726, U01 HL145386, R01 ES028033, and P30 ES000002]; and by the Health Effects Institute [grant number 4953-RFA14-3/16-4].

Abbreviations

NHS II

Nurses’ Health Study II

PM10

Particulate matter with an aerodynamic diameter less than or equal to 10 microns

PM2.5-10

Particulate matter with an aerodynamic diameter between 2.5 to 10 microns

PM2.5

Particulate matter with an aerodynamic diameter less than or equal to 2.5 microns

HR

Hazard ratio

CI

Confidence interval

Footnotes

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Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Reference

  1. Abareshi F, Sharifi Z, Hekmatshoar R, Fallahi M, Lari Najafi M, Ahmadi Asour A, Mortazavi F, Akrami R, Miri M, Dadvand P, 2020. Association of exposure to air pollution and green space with ovarian reserve hormones levels. Environ. Res. 184, 109342 10.1016/j.envres.2020.109342 [DOI] [PubMed] [Google Scholar]
  2. Agarwal A, Aponte-Mellado A, Premkumar BJ, Shaman A, Gupta S, 2012. The effects of oxidative stress on female reproduction: a review. Reprod. Biol. Endocrinol. RBE 10, 49–49. 10.1186/1477-7827-10-49 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Atsma F, Bartelink M-LEL, Grobbee DE, van der Schouw YT, 2006. Postmenopausal status and early menopause as independent risk factors for cardiovascular disease: a meta-analysis. Menopause 13. [DOI] [PubMed] [Google Scholar]
  4. Bertone-Johnson ER, Manson JE, Purdue-Smithe AC, Hankinson SE, Rosner BA, Whitcomb BW, 2019. A prospective study of inflammatory biomarker levels and risk of early menopause. Menopause N. Y. N 26, 32–38. 10.1097/GME.0000000000001162 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Boots CE, Jungheim ES, 2015. Inflammation and Human Ovarian Follicular Dynamics. Semin. Reprod. Med 33, 270–275. 10.1055/s-0035-1554928 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Borman SM, Christian PJ, Sipes IG, Hoyer PB, 2000. Ovotoxicity in female Fischer rats and B6 mice induced by low-dose exposure to three polycyclic aromatic hydrocarbons: comparison through calculation of an ovotoxic index. Toxicol. Appl. Pharmacol 167, 191–198. 10.1006/taap.2000.9006 [DOI] [PubMed] [Google Scholar]
  7. Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC, Whitsel L, Kaufman JD , American Heart Association Council on Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism, 2010. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 121, 2331–2378. 10.1161/CIR.0b013e3181dbece1 [DOI] [PubMed] [Google Scholar]
  8. Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, Stampfer MJ, Willett WC, 2012. Alternative Dietary Indices Both Strongly Predict Risk of Chronic Disease. J. Nutr 142, 1009–1018. 10.3945/jn.111.157222 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Colditz GA, Stampfer MJ, Willett WC, Stason WB, Rosner B, Hennekens CH, Speizer FE, 1987. Reproducibility and validity of self-reported menopausal status in a prospective cohort study. Am. J. Epidemiol 126, 319–325. 10.1093/aje/126.2.319 [DOI] [PubMed] [Google Scholar]
  10. Djahanbakhch O, Ezzati M, Zosmer A, 2007. Reproductive ageing in women. J. Pathol 211, 219–231. 10.1002/path.2108 [DOI] [PubMed] [Google Scholar]
  11. Faddy MJ, 2000. Follicle dynamics during ovarian ageing. Mol. Cell. Endocrinol 163, 43–48. 10.1016/S0303-7207(99)00238-5 [DOI] [PubMed] [Google Scholar]
  12. Faddy MJ, Gosden RG, 1996. Ovary and ovulation: A model conforming the decline in follicle numbers to the age of menopause in women. Hum. Reprod 11, 1484–1486. 10.1093/oxfordjournals.humrep.a019422 [DOI] [PubMed] [Google Scholar]
  13. Gai H-F, An J-X, Qian X-Y, Wei Y-J, Williams JP, Gao G-L, 2017. Ovarian Damages Produced by Aerosolized Fine Particulate Matter (PM(2.5)) Pollution in Mice: Possible Protective Medications and Mechanisms. Chin. Med. J. (Engl.) 130, 1400–1410. 10.4103/0366-6999.207472 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gaskins AJ, Mínguez-Alarcon L, Fong KC, Abdelmessih S, Coull BA, Chavarro JE, Schwartz J, Kloog I, Souter I, Hauser R, Laden F, 2019. Exposure to Fine Particulate Matter and Ovarian Reserve Among Women from a Fertility Clinic. Epidemiol. Camb. Mass 30, 486–491. 10.1097/EDE.0000000000001029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gold EB, 2011. The Timing of the Age at Which Natural Menopause Occurs. Perimenopause 38, 425–440. 10.1016/j.ogc.2011.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gold EB, Crawford SL, Avis NE, Crandall CJ, Matthews KA, Waetjen LE, Lee JS, Thurston R, Vuga M, Harlow SD, 2013. Factors Related to Age at Natural Menopause: Longitudinal Analyses From SWAN. Am. J. Epidemiol 178, 70–83. 10.1093/aje/kws421 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gore AC, Chappell VA, Fenton SE, Flaws JA, Nadal A, Prins GS, Toppari J, Zoeller RT, 2015. EDC-2: The Endocrine Society’s Second Scientific Statement on Endocrine-Disrupting Chemicals. Endocr. Rev 36, E1–E150. 10.1210/er.2015-1010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gray KE, Schiff MA, Fitzpatrick AL, Kimura M, Aviv A, Starr JR, 2014. Leukocyte telomere length and age at menopause. Epidemiol. Camb. Mass 25, 139–146. 10.1097/EDE.0000000000000017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Harlow SD, Gass M, Hall JE, Lobo R, Maki P, Rebar RW, Sherman S, Sluss PM, de Villiers TJ, for the STRAW 10 Collaborative Group, 2012. Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging. Menopause 19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. He C, Kraft P, Chen C, Buring JE, Paré G, Hankinson SE, Chanock SJ, Ridker PM, Hunter DJ, Chasman DI, 2009. Genome-wide association studies identify loci associated with age at menarche and age at natural menopause. Nat. Genet 41, 724–728. 10.1038/ng.385 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. He C, Murabito JM, 2014. Genome-wide association studies of age at menarche and age at natural menopause. Mol. Cell. Endocrinol 382, 767–779. 10.1016/j.mce.2012.05.003 [DOI] [PubMed] [Google Scholar]
  22. Hu FB, Grodstein F, Hennekens CH, Colditz GA, Johnson M, Manson JE, Rosner B, Stampfer MJ, 1999. Age at Natural Menopause and Risk of Cardiovascular Disease. Arch. Intern. Med 159, 1061–1066. 10.1001/archinte.159.10.1061 [DOI] [PubMed] [Google Scholar]
  23. Jung SJ, Shin A, Kang D, 2015. Menarche age, menopause age and other reproductive factors in association with post-menopausal onset depression: Results from Health Examinees Study (HEXA). J. Affect. Disord 187, 127–135. 10.1016/j.jad.2015.08.047 [DOI] [PubMed] [Google Scholar]
  24. Kaipia A, Chun SY, Eisenhauer K, Hsueh AJ, 1996. Tumor necrosis factor-alpha and its second messenger, ceramide, stimulate apoptosis in cultured ovarian follicles. Endocrinology 137, 4864–4870. 10.1210/endo.137.11.8895358 [DOI] [PubMed] [Google Scholar]
  25. Karner AA, Eisinger DS, Niemeier DA, 2010. Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data. Environ. Sci. Technol 44, 5334–5344. 10.1021/es100008x [DOI] [PubMed] [Google Scholar]
  26. Kioumourtzoglou M-A, Spiegelman D, Szpiro AA, Sheppard L, Kaufman JD, Yanosky JD, Williams R, Laden F, Hong B, Suh H, 2014. Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies. Environ. Health Glob. Access Sci. Source 13, 2 10.1186/1476-069X-13-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kuh D, Muthuri S, Cooper R, Moore A, Mackinnon K, Cooper C, Adams JE, Hardy R, Ward KA, 2016. Menopause, Reproductive Life, Hormone Replacement Therapy, and Bone Phenotype at Age 60-64 Years: A British Birth Cohort. J. Clin. Endocrinol. Metab 101, 3827–3837. 10.1210/jc.2016-1828 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Liao B-Q, Liu C-B, Xie S-J, Liu Y, Deng Y-B, He S-W, Fu X-P, Fu B-B, Wang Y-L, Chen M-H, Lin Y-H, Li F-P, Xie X, Hong X-R, Wang H-L, 2020. Effects of fine particulate matter (PM2.5) on ovarian function and embryo quality in mice. Environ. Int 135, 105338 10.1016/j.envint.2019.105338 [DOI] [PubMed] [Google Scholar]
  29. Lu J, Wang Z, Cao J, Chen Y, Dong Y, 2018. A novel and compact review on the role of oxidative stress in female reproduction. Reprod. Biol. Endocrinol. RBE 16, 80–80. 10.1186/s12958-018-0391-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lujan-Barroso L, Gibert K, Obón-Santacana M, Chirlaque MD, Sánchez M-J, Larrañaga N, Barricarte A, Quirós JR, Salamanca-Fernández E, Colorado-Yohar S, Gómez-Pozo B, Agudo A, Duell EJ, 2018. The influence of lifestyle, diet, and reproductive history on age at natural menopause in Spain: Analysis from the EPIC-Spain sub-cohort. Am. J. Hum. Biol 30, e23181 10.1002/ajhb.23181 [DOI] [PubMed] [Google Scholar]
  31. Martens DS, Nawrot TS, 2018. Ageing at the level of telomeres in association to residential landscape and air pollution at home and work: a review of the current evidence. Toxicol. Lett 298, 42–52. 10.1016/j.toxlet.2018.06.1213 [DOI] [PubMed] [Google Scholar]
  32. Martens DS, Nawrot TS, 2016. Air Pollution Stress and the Aging Phenotype: The Telomere Connection. Curr. Environ. Health Rep 3, 258–269. 10.1007/s40572-016-0098-8 [DOI] [PubMed] [Google Scholar]
  33. Mazzoli-Rocha F, Fernandes S, Einicker-Lamas M, Zin WA, 2010. Roles of oxidative stress in signaling and inflammation induced by particulate matter. Cell Biol. Toxicol 26, 481–498. 10.1007/s10565-010-9158-2 [DOI] [PubMed] [Google Scholar]
  34. McKnight KK, Wellons MF, Sites CK, Roth DL, Szychowski JM, Halanych JH, Cushman M, Safford MM, 2011. Racial and regional differences in age at menopause in the United States: findings from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. Am. J. Obstet. Gynecol 205, 353.e1–8. 10.1016/j.ajog.2011.05.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Meek MD, 1998. Ah Receptor and Estrogen Receptor-Dependent Modulation of Gene Expression by Extracts of Diesel Exhaust Particles. Environ. Res 79, 114–121. 10.1006/enrs.1998.3870 [DOI] [PubMed] [Google Scholar]
  36. Miri M, Nazarzadeh M, Alahabadi A, Ehrampoush MH, Rad A, Lotfi MH, Sheikhha MH, Sakhvidi MJZ, Nawrot TS, Dadvand P, 2019. Air pollution and telomere length in adults: A systematic review and meta-analysis of observational studies. Environ. Pollut. Barking Essex 1987 244, 636–647. 10.1016/j.envpol.2018.09.130 [DOI] [PubMed] [Google Scholar]
  37. Misaki K, Suzuki M, Nakamura M, Handa H, Iida M, Kato T, Matsui S, Matsuda T, 2008. Aryl Hydrocarbon Receptor and Estrogen Receptor Ligand Activity of Organic Extracts from Road Dust and Diesel Exhaust Particulates. Arch. Environ. Contam. Toxicol 55, 199–209. 10.1007/s00244-007-9110-5 [DOI] [PubMed] [Google Scholar]
  38. Morabia A, Costanza MC, 1998. International variability in ages at menarche, first livebirth, and menopause. World Health Organization Collaborative Study of Neoplasia and Steroid Contraceptives. Am. J. Epidemiol 148, 1195–1205. 10.1093/oxfordjournals.aje.a009609 [DOI] [PubMed] [Google Scholar]
  39. Mori Y, Taneda S, Hayashi H, Sakushima A, Kamata K, Suzuki AK, Yoshino S, Sakata M, Sagai M, Seki K, 2002. Estrogenic Activities of Chemicals in Diesel Exhaust Particles. Biol. Pharm. Bull 25, 145–146. 10.1248/bpb.25.145 [DOI] [PubMed] [Google Scholar]
  40. Muka T, Oliver-Williams C, Kunutsor S, Laven JSE, Fauser BCJM, Chowdhury R, Kavousi M, Franco OH, 2016. 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 Cardiol. 1, 767–776. 10.1001/jamacardio.2016.2415 [DOI] [PubMed] [Google Scholar]
  41. Nagel G, Altenburg H-P, Nieters A, Boffetta P, Linseisen J, 2005. Reproductive and dietary determinants of the age at menopause in EPIC-Heidelberg. Maturitas 52, 337–347. 10.1016/j.maturitas.2005.05.013 [DOI] [PubMed] [Google Scholar]
  42. Ogliari KS, de Lichtenfels AJFC, de Marchi MRR, Ferreira AT, Dolhnikoff M, Saldiva PHN, 2013. Intrauterine exposure to diesel exhaust diminishes adult ovarian reserve. Fertil. Steril 99, 1681–1688.e2. 10.1016/j.fertnstert.2013.01.103 [DOI] [PubMed] [Google Scholar]
  43. Oh SM, Ryu BT, Chung KH, 2008. Identification of estrogenic and antiestrogenic activities of respirable diesel exhaust particles by bioassay-directed fractionation. Arch. Pharm. Res 31, 75–82. 10.1007/s12272-008-1123-8 [DOI] [PubMed] [Google Scholar]
  44. Pyun J-A, Kim S, Cho NH, Koh I, Lee J-Y, Shin C, Kwack K, 2014. Genome-wide association studies and epistasis analyses of candidate genes related to age at menarche and age at natural menopause in a Korean population. Menopause. [DOI] [PubMed] [Google Scholar]
  45. Rao X, Zhong J, Brook RD, Rajagopalan S, 2018. Effect of Particulate Matter Air Pollution on Cardiovascular Oxidative Stress Pathways. Antioxid. Redox Signal 28, 797–818. 10.1089/ars.2017.7394 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Savonitto S, Morici N, Franco N, Misuraca L, Lenatti L, Ferri LA, Lo Jacono E, Leuzzi C, Corrada E, Aranzulla TC, Cagnacci A, Colombo D, La Vecchia C, Prati F, 2018. Age at menopause, extent of coronary artery disease and outcome among postmenopausal women with acute coronary syndromes. Int. J. Cardiol 259, 8–13. 10.1016/j.ijcard.2018.02.065 [DOI] [PubMed] [Google Scholar]
  47. Schoenaker DA, Jackson CA, Rowlands JV, Mishra GD, 2014. Socioeconomic position, lifestyle factors and age at natural menopause: a systematic review and meta-analyses of studies across six continents. Int. J. Epidemiol 43, 1542–1562. 10.1093/ije/dyu094 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Shenassa ED, Rossen LM, 2015. Telomere length and age-at-menopause in the US. Maturitas 82, 215–221. 10.1016/j.maturitas.2015.07.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Shuster LT, Rhodes DJ, Gostout BS, Grossardt BR, Rocca WA, 2010. Premature menopause or early menopause: long-term health consequences. Maturitas 65, 161–166. 10.1016/j.maturitas.2009.08.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sullivan SD, Lehman A, Nathan NK, Thomson CA, Howard BV, 2017. Age of menopause and fracture risk in postmenopausal women randomized to calcium + vitamin hormone therapy, or the combination: results from the Women’s Health Initiative Clinical Trials. Menopause N. Y. N 24, 371–378. 10.1097/GME.0000000000000775 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Takeda K, Tsukue N, Yoshida S, 2004. Endocrine-disrupting activity of chemicals in diesel exhaust and diesel exhaust particles. Environ. Sci. Int. J. Environ. Physiol. Toxicol 11, 33–45. [PubMed] [Google Scholar]
  52. Thomas F, Renaud F, Benefice E, de Meeüs T, Guegan JF, 2001. International variability of ages at menarche and menopause: patterns and main determinants. Hum. Biol 73, 271–290. 10.1353/hub.2001.0029 [DOI] [PubMed] [Google Scholar]
  53. Thomford PJ, Jelovsek FR, Mattison DR, 1987. Effect of oocyte number and rate of atresia on the age of menopause. Reprod. Toxicol. Elmsford N 1, 41–51. 10.1016/0890-6238(87)90070-0 [DOI] [PubMed] [Google Scholar]
  54. Triebner K, Markevych I, Hustad S, Benediktsdóttir B, Forsberg B, Franklin KA, Gullón Blanco JA, Holm M, Jaquemin B, Jarvis D, Jõgi R, Leynaert B, Lindberg E, Martinez-Moratalla J, Muniozguren Agirre N, Pin I, Sánchez-Ramos JL, Heinrich J, Gómez Real F, Dadvand P, 2019. Residential surrounding greenspace and age at menopause: A 20-year European study (ECRHS). Environ. Int 132, 105088 10.1016/j.envint.2019.105088 [DOI] [PubMed] [Google Scholar]
  55. Veras MM, Damaceno-Rodrigues NR, Guimarães Silva RM, Scoriza JN, Saldiva PHN, Caldini EG, Dolhnikoff M, 2009. Chronic exposure to fine particulate matter emitted by traffic affects reproductive and fetal outcomes in mice. Environ. Res 109, 536–543. 10.1016/j.envres.2009.03.006 [DOI] [PubMed] [Google Scholar]
  56. Wallace WHB, Kelsey TW, 2010. Human ovarian reserve from conception to the menopause. PloS One 5, e87762 10.1371/journal.pone.0008772 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. White AJ, Keller JP, Zhao S, Carroll R, Kaufman JD, Sandler DP, 2019. Air Pollution, Clustering of Particulate Matter Components, and Breast Cancer in the Sister Study: A U.S.-Wide Cohort. Environ. Health Perspect 127, 107002 10.1289/EHP5131 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Whiteman MK, Hillis SD, Jamieson DJ, Morrow B, Podgornik MN, Brett KM, Marchbanks PA, 2008. Inpatient hysterectomy surveillance in the United States, 2000-2004. Am. J. Obstet. Gynecol 198, 34.e1–7. 10.1016/j.ajog.2007.05.039 [DOI] [PubMed] [Google Scholar]
  59. World Health Organization, 1996. Research on the menopause in the 1990s. World Health Organ. Tech. Rep. Ser 866, 1–107. [PubMed] [Google Scholar]
  60. Wright JD, Herzog TJ, Tsui J, Ananth CV, Lewin SN, Lu Y-S, Neugut AI, Hershman DL, 2013. Nationwide trends in the performance of inpatient hysterectomy in the United States. Obstet. Gynecol 122, 233–241. 10.1097/AOG.0b013e318299a6cf [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Xu X, Jones M, Mishra GD, 2020. Age at natural menopause and development of chronic conditions and multimorbidity: results from an Australian prospective cohort. Hum. Reprod 35, 203–211. 10.1093/humrep/dez259 [DOI] [PubMed] [Google Scholar]
  62. Yanosky JD, Paciorek CJ, Laden F, Hart JE, Puett RC, Liao D, Suh HH, 2014. Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors. Environ. Health 13, 63 10.1186/1476-069X-13-63 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Zhu D, Chung H-F, Pandeya N, Dobson AJ, Cade JE, Greenwood DC, Crawford SL, Avis NE, Gold EB, Mitchell ES, Woods NF, Anderson D, Brown DE, Sievert LL, Brunner EJ, Kuh D, Hardy R, Hayashi K, Lee JS, Mizunuma H, Giles GG, Bruinsma F, Tillin T, Simonsen MK, Adami H-O, Weiderpass E, Canonico M, Ancelin M-L, Demakakos P, Mishra GD, 2018. Relationships between intensity, duration, cumulative dose, and timing of smoking with age at menopause: A pooled analysis of individual data from 17 observational studies. PLOS Med. 15, e1002704 10.1371/journal.pmed.1002704 [DOI] [PMC free article] [PubMed] [Google Scholar]

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