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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Pulm Med . 2025 Dec 13;26:167. doi: 10.1186/s12890-025-04055-4 Search in PMC Search in PubMed View in NLM Catalog Add to search Association between menopausal age and chronic obstructive pulmonary disease and the mediating role of smoking: a study based on NHANES 1999–2018 Tianye Li Tianye Li 1 Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 China Find articles by Tianye Li 1, # , Hongjun Zhao Hongjun Zhao 2 Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000 China Find articles by Hongjun Zhao 2, # , Hao Xu Hao Xu 2 Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000 China Find articles by Hao Xu 2 , Mengya Yang Mengya Yang 2 Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000 China Find articles by Mengya Yang 2 , Yanhong Zheng Yanhong Zheng 2 Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000 China Find articles by Yanhong Zheng 2 , Chengshui Chen Chengshui Chen 1 Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 China 2 Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000 China Find articles by Chengshui Chen 1, 2, ✉ , Beibei Wang Beibei Wang 1 Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 China Find articles by Beibei Wang 1, ✉ Author information Article notes Copyright and License information 1 Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 China 2 Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Clinical Research Center, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000 China ✉ Corresponding author. # Contributed equally. Received 2025 May 28; Accepted 2025 Dec 5; Collection date 2026. © The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13067747 PMID: 41390390 Abstract Background Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally, with smoking as its primary risk factor. The relationship between menopausal age and COPD remains unknown. This study examines this potential association. Methods Using NHANES 1999–2018 data, we analyzed associations between menopausal age and COPD via weighted logistic regression, weighted linear regression, restricted cubic spline (RCS) analysis, and explored the potential mediating role of smoking. Results This study included a total of 8,731 participants. Every year increase in menopausal age was associated with a 1.2% reduction in COPD risk after completely adjusting the Model (odds ratio [OR] = 0.988, 95% confidence interval [CI]: 0.979–0.998, P < 0.001). After grouping participants based on tertiles of menopausal age, COPD risks decreased by 19.0% in the T2 (OR = 0.810, 95% CI: 0.662–0.991, P = 0.040) and 21.4% in the T3 (OR = 0.786, 95% CI: 0.644–0.958, P = 0.018) relative to T1, respectively. RCS regression uncovered a linear association between menopausal age and COPD risk (P-overall < 0.001, P-nonlinear = 0.080). Subgroup analysis demonstrated that menopausal age was inversely related to COPD risk in general populations. Mediation analysis suggested that smoking partially mediated (38.0%) the link between menopausal age and COPD. Conclusion Later age at menopause is significantly associated with a reduced risk of COPD, and smoking may partially explain this association. These findings suggest the potential value of targeted screening and highlight the importance of smoking cessation interventions in this population. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-025-04055-4. Keywords: COPD, Menopausal age, Smoking, Mediation analysis, NHANES Background Chronic obstructive pulmonary disease (COPD), classified as a chronic airway disease (CAD), features persistent airflow obstruction resulting from abnormalities in the airways or alveoli. This condition is often accompanied by chronic symptoms, including dyspnea, coughing, and expectoration [ 1 , 2 ]. COPD is the third deadliest disease worldwide, claiming approximately 3.23 million lives in 2019 and putting tremendous pressure on the medical system. In the United States, COPD-related expenditures will reach 800.9 billion USD in 20 years, equaling some 40 billion USD every year [ 3 ]. COPD is primarily caused by smoking, though environmental factors, such as air pollutants, also contribute significantly to its onset and progression [ 4 ]. Smoking is the primary pathogenic factor for COPD [ 5 ]. As the primary pathogenic driver, smoking induces prolonged exposure to cigarette smoke, triggering reactive oxygen species activation, inflammatory oxidative stress, and programmed cell death [ 6 ]. These processes ultimately lead to alveolar enlargement and the pathological manifestations of COPD. Due to the high heterogeneity and complex pathophysiology of COPD, accurate diagnosis and prognostic evaluation remain challenging [ 7 ]. Over the years, the incidence of COPD in women has been increasing, possibly due to the growing number of female smokers and different sex hormone patterns [ 8 , 9 ]. Studies have shown that estrogen reduces inflammation in the lungs and plays a crucial role in maintaining energy balance and metabolic health [ 10 , 11 ]. Therefore, changes in endogenous estrogen levels may be associated with COPD pathogenesis. After menopause, ovarian function declines, resulting in a significant decrease in estrogen levels. This period is accompanied by the occurrence of various complications and may adversely affect lung function [ 12 – 14 ]. It is worth noting that women who experience menopause at an earlier age are at a higher risk for cardiovascular disease, osteoporosis, and other conditions. Recent evidence suggests that an earlier age of menopause may also be associated with decreased lung function and an increased risk of COPD onset [ 14 ]. Therefore, exploring the gender differences in COPD from the perspective of sex hormones, especially the impact of menopausal age on COPD occurrence and development, is significant [ 14 , 15 ]. However, most of these studies have small sample sizes or monocentric data, so the conclusions may be limited. Therefore, it is imperative to investigate the association between menopausal age and COPD. This article uses the National Health and Nutrition Examination Survey (NHANES) dataset to discuss this association and to appraise the role of smoking in this relationship. Method Study population The U.S. Centers for Disease Control and Prevention (CDC) organized and implemented the National Health and Nutrition Examination Survey (NHANES), which collected data on the health and nutritional statuses of Americans every two years. For this study, relevant published data from NHANES 1999–2018 were retrieved, and the ethical requirements of the Declaration of Helsinki were followed [ 16 ]. Written informed consent was obtained before the NHANES survey, and the National Center for Health Statistics (NCHS) de-identified the data before its release to ensure privacy and data security. The initial study population comprised 110,982 individuals across 10 cycles of the NHANES database. The study excluded the following individuals (Fig. 1 ): (i) age < 20 years ( n = 46,235); (ii) male participants ( n = 26,473) (iii) missing COPD diagnosis ( n = 10,446) (iv) missing menopausal age ( n = 16,225); (v) missing relevant covariates (e.g., smoking, alcohol consumption, BMI, hypertension, and diabetes) ( n = 2,782). After applying all exclusion criteria, the final analytical sample consisted of 8,731 participants. Fig. 1. Open in a new tab Study flow diagram. COPD, chronic obstructive pulmonary disease Assessment of COPD The COPD diagnosis was determined based on NHANES-related questionnaires and relevant examinations: (i) Participants were informed of their diagnosis, which could be COPD, chronic bronchitis, or emphysema. (ii) The post-bronchodilator forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) ratio was less than 0.7 [ 17 ]. Participants who met either criterion were diagnosed with COPD. Assessment of menopausal age The menopausal age was determined using the reproductive health questionnaire in the NHANES database and was defined as the age at which the last menstrual period occurred, as reported by the individual. Definition of covariates The following were considered covariates: age, race, education, marital status, household income (poverty-to-income ratio [PIR]), smoking, alcohol consumption, BMI, hypertension, diabetes, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), blood urea nitrogen (BUN), blood uric acid (UA), white blood cells (WBC), glomerular filtration rate (GFR), estrogen use, age at menarche, and number of pregnancies. Races were subdivided into Mexican American, non-Hispanic white, non-Hispanic black, and other. Education was classified as high school diploma, high school diploma, and above high school diploma. Marital status was divided into three categories: married/cohabiting, widowed/divorced/separated, and unmarried. Drinking status was classified as having consumed a glass of any kind of alcohol in one’s lifetime (yes/no). Smoking status was classified based on the cumulative smoking history as current smoking (≥ 100 cigarettes and still smoking), past smoking (≥ 100 cigarettes and quit smoking), or never smoking (< 100 cigarettes). Hypertension was defined as individuals who had been told they had hypertension and were taking hypotensive drugs, or who had a systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 80 mmHg [ 18 ]. Diabetes was defined as individuals who self-reported having diabetes and taking antidiabetic drugs or insulin or who had a fasting blood sugar (FBG) ≥ 126 mg/dL, or glycosylated hemoglobin (HbA1c) ≥ 6.5% [ 19 ]. eGFR = 175 × (creatinine) ∧ 1.154 × (age) ∧ 0.203 × gender coefficient × race coefficient [ 20 ]. PIR came from the demographic questionnaire. Estrogen use, age at menarche, and number of pregnancies were all derived from the reproductive health questionnaire. BUN, UA, WBC and creatinine were obtained from relevant blood tests. Statistical analysis Blood biochemical indicators involved in this study (e.g., FBG, TC, HDL) were derived from standardized tests implemented by a mobile examination center (MEC). Because the MEC used a specific sampling design and testing process, we applied the MEC weight coefficient to the laboratory data per the NHANES Data Analysis Guidelines to ensure nationally representative estimates. R 4.1.3 was employed for statistical analyses. Numerical variables were tested by the Anderson-Darling test for normality. Normally distributed continuous variables were represented as mean ± standard deviation, with intergroup differences tested by a weighted t -test. Abnormally distributed continuous variables were represented as medians (quartiles), with intergroup differences analyzed by a weighted non-parametric test. Categorical variables were presented as frequencies (percentages), with intergroup differences analyzed by a weighted chi-square test. Before the weighted logistic regression analysis, multicollinearity examinations were made on all independent variables. The association between menopausal age and COPD was evaluated through weighted logistic regressions, and participants were grouped by the tertiles of menopausal age to further explore their relationships. To investigate the association between smoking behavior and the timing of menopause, a weighted linear regression model was employed. Then, the relationship between menopausal age and COPD was examined through restricted cubic splines (RCS). A stratified analysis of the association between menopausal age and COPD was further implemented based on race (Mexican American, non-Hispanic white, non-Hispanic black, other), BMI (normal weight, overweight, obese), smoking status (never, former, current), drinking status (yes or no), hypertension (yes or no), and diabetes (yes or no). Next, the mediation analysis discussed the mediating effect of smoking on menopausal age and COPD. All tests were two-sided tests, with P < 0.05 denoting statistical significance. Results Baseline characteristics This study included 8,731 patients, 1,294 of whom developed COPD. COPD patients were significantly older and predominantly consisted of non-Hispanic whites, individuals who consumed alcohol, individuals who used estrogen, individuals who smoked, individuals with hypertension, and those with diabetes ( P < 0.05) (Table 1 ). Meanwhile, COPD patients had significantly higher UA, number of pregnancies, and WBC, but a significantly lower education level, marital status, PIR, BUN, HDL, and menopausal age ( P < 0.05). Table 1. Baseline characteristics by COPD Characteristic Overall (8,731) Non-COPD (7,437) COPD (1,294) P -value Age, years 61(53,70) 60(53,69) 62(55,70) 0.002 Race, n (%) < 0.001 Mexican American 1,310(4.5%) 1,215(5.0%) 95(2.0%) Non-Hispanic White 4,336(77%) 3,519(75%) 817(84%) Non-Hispanic Black 1,757(9.7%) 1,533(10%) 224(7.3%) Other Race 1,328(9.1%) 1,170(10%) 158(6.7%) Education level, n (%) < 0.001 < High school diploma 2,470(18%) 2,109(17%) 361(22%) High school diploma/equivalent 2,227(27%) 1,862(27%) 365(31%) >High school diploma 4,034(55%) 3,466(56%) 568(48%) Marital status, n (%) < 0.001 Married/cohabitation 4,622(61%) 4,016(62%) 606(56%) Widow/divorce/separation 3,706(36%) 3,074(34%) 632(41%) Unmarried 403(3.3%) 347(3.5%) 56(2.3%) PIR 2.93(1.55,5.00) 3.11(1.62,5.00) 2.23(1.24,4.28) < 0.001 Alcohol intake, n (%) 0.012 Yes 5,081(66%) 4,243(65%) 838(70%) No 3,650(34%) 3,194(35%) 456(30%) Smoking, n (%) < 0.001 Never 5,094(55%) 4,635(60%) 459(32%) Former 2,230(27%) 1,789(26%) 441(34%) Current 1,407(18%) 1,013(15%) 394(34%) Diabetes, n (%) 0.002 Yes 2,029(17%) 1,700(17%) 329(21%) No 6,702(83%) 5,737(83%) 965(79%) Hypertension, n (%) 0.034 Yes 6,339(68%) 5,359(67%) 980(71%) No 2,392(32%) 2,078(33%) 314(29%) Estrogen use, n (%) 0.021 Yes 3,370(45%) 2,802(44%) 568(49%) No 5,361(55%) 4,635(56%) 726(51%) BMI, Kg/m 2 28(25,33) 28(25,33) 29(24,34) 0.903 TC, mmol/L 5.38(4.68,6.08) 5.38(4.71,6.08) 5.30(4.58,6.08) 0.235 HDL-C, mmol/L 1.47(1.22,1.78) 1.47(1.22,1.80) 1.42(1.19,1.76) 0.008 BUN, mmol/L 5.00(3.93,6.07) 5.00(3.93,6.07) 4.64(3.60,6.07) 0.013 UA, umol/L 292(244,345) 292(244,345) 303(250,357) 0.044 Creatinine, mg/dL 0.79(0.69,0.90) 0.79(0.69,0.90) 0.80(0.68,0.90) 0.300 WBC,1000 cells/uL 6.90(5.60,8.20) 6.80(5.60,8.10) 7.30(5.90,8.80) < 0.001 eGFR, mL/min/1.73 m² 77(64,92) 78(65,92) 75(63,91) 0.093 Number of pregnancies, times 3.00(2.00,4.00) 3.00(2.00,4.00) 3.00(2.00,5.00) 0.014 Age at menarche, years 13.00(12.00,14.00) 13.00(12.00,14.00) 13.00(12.00,14.00) 0.500 Menopausal age, years 46(39,50) 46(39,51) 44(36,50) < 0.001 Open in a new tab PIR poverty income ratio, BMI body mass index, TC total cholesterol, HDL high-density lipoprotein, BUN blood urea nitrogen, UA uric acid, WBC white blood cell, eGFR estimated glomerular filtration rate Data are presented as median (interquartile range) for continuous variables and n (weighted percentage) for categorical variables P -values were derived from a weighted chi-square test for categorical variables and a weighted nonparametric test for continuous variables Association of menopausal age with COPD Weighted multivariate logistic regression analysis is summarized in Table 2 . After excluding collinearity for all covariates, a weighted multivariate logistic regression was performed. Menopausal age was apparently associated with COPD in Models 1 and 2 ( P < 0.05). Menopausal age was independently associated with COPD in Model 3 (fully adjusted). Specifically, with every year increase in menopausal age, the risk of COPD decreased by 1.2% (OR = 0.988, 95% CI: 0.979–0.998, P < 0.001). Additionally, a tertile-based analysis of menopausal age revealed that compared with T1, COPD risks were 19.0% lower in T2 (OR = 0.810, 95% 0.662–0.991, p = 0.040) and 21.4% lower in T3 (OR = 0.786, 95% CI: 0.644–0.958, P = 0.018), respectively. This analysis confirmed that early menopausal age was associated with COPD and that this link remained consistent across different models. Table 2. The association between menopausal age and COPD Model 1 Model 2 Model 3 OR (95% CI) P OR (95% CI) P OR (95% CI) P Menopausal age as a continuous variable Menopausal age 0.982 (0.974–0.990) < 0.001 0.982 (0.973–0.991) < 0.001 0.988 (0.979–0.998) 0.015 Menopausal age as a categorical variable T1 Ref T2 0.774 (0.638–0.939) 0.010 0.782 (0.643–0.952) 0.015 0.810 (0.662–0.991) 0.040 T3 0.688 (0.573–0.826) < 0.001 0.680 (0.561–0.824) < 0.001 0.786 (0.644–0.958) 0.018 Open in a new tab Model 1: Unadjusted Model 2: Adjusted for age, race, education level, marital status, PIR, BMI Model 3: Adjusted for age, race, education level, marital status, PIR, BMI, alcohol intake, smoking, BUN, creatinine, UA, WBC, hypertension, diabetes, HDL-C, TC, eGFR, estrogen use, age at menarche, number of pregnancies Association between menopausal age and smoking As shown in Table 3 , the association between menopausal age and smoking status was evaluated using multivariate regression analyses across three adjusted models. Compared to never smokers, in the fully adjusted model (Model 3), both former and current smoking were significantly associated with earlier menopause. Former smokers reached menopause 0.845 years earlier ( β = −0.845, 95% CI: −1.337 to −0.353, P < 0.001), while current smokers experienced menopause 1.402 years earlier ( β = −1.402, 95% CI: −2.048 to −0.756, P < 0.001) compared to never smokers. Table 3. The association between menopausal age and smoking Model 1 Model 2 Model 3 β (95% CI) P β (95% CI) P β (95% CI) P Smoking Never Ref Former −0.597 (−1.105 - −0.089) 0.022 −0.817 (−1.328 - −0.307) 0.002 −0.845(−1.337 - −0.353) < 0.001 Current −4.822 (−5.492 - −4.151) < 0.001 −1.725 (−2.325 - −1.124) < 0.001 −1.402(−2.048 - −0.756) < 0.001 Open in a new tab Model 1: Unadjusted Model 2: Adjusted for age, race, education level, marital status, PIR, BMI Model 3: Adjusted for age, race, education level, marital status, PIR, BMI, alcohol intake, BUN, creatinine, UA, WBC, hypertension, diabetes, HDL-C, TC, eGFR, estrogen use, age at menarche, number of pregnancies Nonlinear relationship between menopausal age and COPD Restricted cubic spline (RCS) regression analysis (Fig. 2 and Figure S1) showed that menopausal age was significantly associated with COPD overall (p-overall < 0.001), though the nonlinear association was not statistically significant (p-nonlinear = 0.080) and manifested as a linear trend. Fig. 2. Open in a new tab RCS plot of the non-linear association of menopausal age with COPD after adjustment for the covariates. RCS, restricted cubic spline; OR, odds ratio; COPD, chronic obstructive pulmonary disease Subgroup association between menopausal age and COPD Subgroup analysis is summarized in Table 4 . The association between menopausal age and COPD risk was particularly pronounced among several subgroups when the variable was treated as continuous. This was especially true among Mexican Americans (OR: 0.962, 95% CI: 0.929–0.996, P = 0.028), those with less than a high school diploma (OR: 0.984, 95% CI: 0.971–0.997, P = 0.016), and those who were widowed, divorced, or separated (OR: 0.985, 95% CI: 0.972–0.997, P = 0.016). Significant associations were also observed among individuals with obesity (OR: 0.976, 95% CI: 0.964–0.988, P < 0.001), non-drinkers (OR: 0.979, 95% CI: 0.964–0.994, P = 0.007), or those with a history of estrogen use (OR: 0.982, 95% CI: 0.970–0.995, P = 0.006). Furthermore, menopausal age in the T2 tertile was associated with notably lower COPD risks relative to the lowest quartile (T1) (OR range: 0.622–0.776, P < 0.05), particularly among individuals who were married or cohabiting, obese, alcohol users, or used exogenous estrogen ( P < 0.05). Menopausal age in the T3 tertile was evidently associated with COPD risk compared to the lowest tertile (T1) (OR range: 0.584–0.794, P < 0.05). This association was especially significant among non-Hispanic white individuals with the following characteristics: educational attainment below a high school diploma, widowed, divorced, or separated marital status, obesity, non-alcohol consumer, and a history of estrogen use ( P < 0.05). Notably, this association remained robust and statistically significant among never-smokers (OR: 0.982, 95% CI: 0.966–0.999, P = 0.034). Based on tertile analysis, a statistically significant association was consistently observed among never-smokers. Compared to the lowest tertile (T1), menopausal age in T2 (OR: 0.732, 95% CI: 0.557–0.963, P = 0.026) and T3 (OR: 0.715, 95% CI: 0.521–0.980, P = 0.037) were both significantly associated with reduced COPD risk in this subgroup. Table 4. Multivariate stratified association between menopausal age and COPD Subgroups Menopausal age T2 vs. T1 T3 vs. T1 OR 95% CI P -value OR 95% CI P -value OR 95% CI P -value Race Mexican American 0.962 0.929–0.996 0.028 0.457 0.208–1.004 0.051 0.599 0.356–1.006 0.053 Non-Hispanic White 0.990 0.979–1.001 0.063 0.839 0.661–1.065 0.147 0.794 0.637–0.990 0.041 Non-Hispanic Black 0.992 0.972–1.013 0.470 0.853 0.556–1.307 0.460 1.005 0.656–1.541 0.980 Other Race 0.979 0.944–1.016 0.262 0.682 0.373–1.248 0.211 0.706 0.341–1.462 0.345 Education level < High school diploma 0.984 0.971–0.997 0.016 1.007 0.728–1.394 0.966 0.584 0.407–0.836 0.004 High school diploma/equivalent 0.985 0.967–1.004 0.116 0.748 0.497–1.125 0.161 0.713 0.477–1.064 0.097 >High school diploma 0.993 0.979–1.008 0.374 0.803 0.617–1.045 0.101 0.902 0.675–1.205 0.482 Marital status Married/cohabitation 0.990 0.976–1.003 0.138 0.732 0.567–0.946 0.017 0.832 0.632–1.095 0.188 Widow/divorce/separation 0.985 0.972–0.997 0.016 0.876 1.223-0.628.223.628- 0.434 0.680 0.515–0.898 0.007 Unmarried 1.029 0.978–1.083 0.261 2.263 0.926–5.530 0.072 2.516 0.983–6.442 0.054 BMI category Normal weight 0.994 0.978–1.010 0.448 1.107 0.795–1.542 0.543 0.825 0.586–1.163 0.270 Overweight 1.003 0.983–1.023 0.782 0.895 0.586–1.367 0.604 1.113 0.756–1.639 0.584 Obesity 0.976 0.964–0.988 < 0.001 0.622 0.475–0.813 < 0.001 0.604 0.463–0.787 < 0.001 Alcohol intake Yes 0.991 0.980–1.002 0.113 0.776 0.613–0.983 0.036 0.792 0.624–1.004 0.054 No 0.979 0.964–0.994 0.007 0.852 0.627–1.158 0.303 0.705 0.516–0.964 0.029 Smoking Never 0.982 0.966–0.999 0.034 0.732 0.557–0.963 0.026 0.715 0.521–0.980 0.037 Former 0.993 0.977–1.009 0.374 0.846 0.606–1.181 0.324 0.879 0.626–1.234 0.454 Current 0.986 0.968–1.004 0.134 0.800 0.561–1.141 0.216 0.656 0.410–1.050 0.078 Estrogen use Yes 0.982 0.970–0.995 0.006 0.709 0.538–0.934 0.015 0.759 0.583–0.988 0.040 No 0.994 0.981–1.008 0.397 0.939 0.706–1.250 0.665 0.813 0.597–1.107 0.188 Open in a new tab Mediation analysis of smoking in menopausal age and COPD The mediating effect of smoking on menopausal age and COPD is shown in Table 5 . The results showed that smoking had an apparent mediating effect of 38.0% between menopausal age and COPD (indirect effect: −0.00111). Table 5. Mediating effect of smoking between menopausal age and COPD Mediation effect Estimate 95% CI lower 95% CI upper P -value Total effect −0.00291 −0.00513 < 0.001 < 0.001 Mediation effect −0.00111 −0.00109 < 0.001 < 0.001 Direct effect −0.00181 −0.00415 < 0.001 < 0.01 Proportion mediated 0.380 0.152 0.520 < 0.001 Open in a new tab Discussion This study establishes a significant association between an earlier menopausal age and an increased risk of COPD, independent of multiple confounders. The relationship was predominantly linear. Crucially, mediation analysis revealed that smoking history only partially explains this association. These findings revealed a strong relationship between menopausal age and COPD. While early menopause may be associated with COPD, further prospective studies are needed to verify its efficacy in improving prognosis and cost-effectiveness before it can be incorporated into clinical screening and prevention strategies. Some relevant research has demonstrated a clear link between menopausal age and respiratory diseases. However, only limited research has discussed the connection between menopausal age and COPD. A UK Biobank (UKB)-based observational study first suggested that reduced FVC and vital capacity were associated with natural and surgical menopause, with a stronger association observed in women with earlier menopausal ages. Additionally, the impact of surgery-induced menopause was more significant than that of natural menopause [ 14 ]. Second, a cross-sectional study of 1,274 subjects revealed that menopause was associated with poor lung function and an increased number of respiratory symptoms. Specifically, after six months of menopause, lung function declined, particularly FEV1, which was more prevalent in women with a BMI < 23 [ 21 ]. Furthermore, an Australian cohort study found that lung function was poorer in postmenopausal women than in premenopausal women, regardless of smoking status. The same study also discovered that smoking was associated with reductions in FVC, FEV1, and forced mid-expiratory flow (FEF25-75), which was more significant in postmenopausal women. Postmenopausal smoking further exacerbated the negative impact on lung function [ 22 ]. A systematic review of the non-obstructive model showed that menopause was strongly associated with reduced lung function and that this association was influenced by multiple factors, such as age, menopausal hormone therapy, and metabolic syndrome risk [ 14 ]. However, a Mendelian randomization study discovered that an early menopausal age protected against airflow obstruction and had no apparent effect on FVC or lung capacity limitation. Furthermore, women who experienced menopause before the age of 45 years had a 15% lower risk of airflow obstruction than those who experienced menopause at the normal age [ 23 ]. A large prospective cohort study of 3,545 people suggested that current female smokers were more likely to experience early menopause than non-smokers, while former female smokers were less likely to experience early menopause than current female smokers [ 24 ]. Apart from that, a population-based study revealed that smoking was significantly associated with fertility [ 25 ]. Further research found that the menopausal age of women smokers was one year earlier than that of women non-smokers [ 25 ]. A cross-sectional study using the 2013–2017 Korea National Health and Nutrition Examination Survey illustrated a significant link between smoking and premature menopause [ 26 ]. Meanwhile, a longitudinal cohort study showed that cumulative cigarette consumption was associated with an increased risk of COPD [ 27 ]. Although the aforementioned studies confirmed the significant impact of menopausal age on lung health and its strong correlation with smoking, some research findings are inconsistent, potentially due to sample size and population heterogeneity. Therefore, this research once again confirms the importance of menopausal age in COPD and discusses the role of smoking in relation to it, offering scientific findings that support the prevention and management of COPD. The potential biological mechanisms underlying the association between early menopause and COPD have yet to be fully elucidated. Accumulating evidence suggests that alterations in sex hormone levels and metabolic pathways may play important roles. During the menopausal transition, the decline in estrogen production has been linked to increased insulin resistance, which may promote systemic inflammation and worsen respiratory symptoms [ 28 , 29 ]. Furthermore, estrogen deficiency can impair the metabolism of toxic substances, particularly those derived from cigarette smoke. This can lead to their accumulation and enhanced detrimental effects on lung tissue [ 30 , 31 ]. Notably, smoking may further amplify these effects through multiple pathways. Tobacco smoke constituents can directly damage ovarian function by disrupting gonadotropin balance and sex steroid synthesis, ultimately contributing to earlier menopause [ 32 – 34 ]. Additionally, nicotine and polycyclic aromatic hydrocarbons inhibit aromatase activity, reducing the conversion of androgens to estrogen and further decreasing estrogen levels [ 35 , 36 ]. These hormonal changes may exacerbate female susceptibility to tobacco-induced lung injury. For example, lipidomic studies have revealed a distinct profile of inflammatory mediators in women with COPD [ 37 ]. The relationship between sex hormones and COPD is complex and multifaceted. Depending on the context, estrogen can have both protective and detrimental effects. It influences airway smooth muscle tone through nitric oxide-mediated bronchodilation [ 38 , 39 ] while also potentially contributing to airway remodeling [ 40 ]. Similarly, progesterone may modulate airway contractility; however, evidence remains primarily from animal studies [ 41 ]. This bidirectional regulation may explain the seemingly paradoxical role of sex hormones in COPD pathogenesis and highlights the need for further investigation into the timing, dosage, and tissue-specific effects of hormonal influences. This study offers valuable findings, but there are still certain limitations. Firstly, the cross-sectional design makes it infeasible to investigate causality. More prospective cohort studies are needed to dive into the links between menopausal age and COPD. Secondly, although we adjusted for some confounding factors, it was impossible to eliminate residuals or account for potential confounding factors such as occupational exposure and genetic susceptibility. The inability to incorporate detailed smoking metrics, such as pack-years, due to substantial missing data underscores the necessity of future studies with more extensive smoking assessments to further elucidate this relationship. Finally, our COPD diagnoses were not entirely based on clinical diagnostic and treatment standards due to recall bias. Further rigorous research is required to discuss these relationships. Despite these limitations, this research provides new insights into the role of menopausal age in the clinical management of COPD individuals and suggests directions for incorporating menopausal age into future routine clinical management of COPD. Conclusion A strong correlation has been found between menopausal age and COPD, with smoking playing a significant mediating role. Therefore, menopausal age can be a key marker for assessing COPD risk, and smoking cessation interventions are crucial for preventing and managing COPD in menopausal women. Further research should be conducted to investigate the complex relationship between menopausal age and COPD, especially the specific role of smoking in this relationship. This will allow for more precise clinical management and prevention of COPD. Supplementary Information 12890_2025_4055_MOESM1_ESM.tif (414.3KB, tif) Supplementary Material 1: Figure S1. RCS plot of the non-linear association of menopausal age (40-60) with COPD after adjustment for the covariates. RCS, restricted cubic spline; OR, odds ratio; COPD, chronic obstructive pulmonary disease. Acknowledgements Not applicable. Abbreviations COPD Chronic obstructive pulmonary disease CAD Chronic airway disease NHANES National Health and Nutrition Examination Survey FEV1 Forced expiratory volume in one second FVC Forced vital capacity BUN Blood urea nitrogen PIR Poverty-to-income ratio UA Blood uric acid WBC White blood cells GFR Glomerular filtration rate SPB Systolic blood pressure DBP Diastolic blood pressure FBG Fasting blood sugar HbA1c Glycosylated hemoglobin TC Total cholesterol HDL-C High-density lipoprotein cholesterol MEC Mobile examination center RCS Restrictive cubic splines Authors’ contributions Tianye Li: Conceptualization, methodology, software, investigation, data curation, formal analysis, visualization, and writing-original draft. Hongjun Zhao: Conceptualization, methodology, software, investigation, data curation, formal analysis, visualization, and writing-original draft, Funding acquisition. Hao Xu, Mengya Yang, and Yanhong Zheng: Conceptualization, writing-review & editing. Chengshui Chen: Conceptualization, validation, project administration, supervision, and writing-review & editing, Funding acquisition. Beibei Wang: conceptualization, validation, project administration, supervision, and writing-review & editing. All authors read and approved the final manuscript. Funding This study was supported by the following funding: the National Key Research and Development Program of China grants [grant number 2016YFC1304000], the National Natural Scientific Foundation of China [grant number 82170017], the Zhejiang Provincial Key Research and Development Program [grant number 2020C03067], and the Health Industry Scientific Research Project of Gansu Province grants [grant number GSWSKY2018-18]. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. All the datasets collected and analyzed during this study are available on the NHANES website: https://www.cdc.gov/nchs/nhanes/about/index.html . Declarations Ethics approval and consent to participate The NHANES study protocol has been approved by the Research Ethics Review Board of the National Center for Health Statistics, and it complies with the Declaration of Helsinki. All individuals participating in this study signed a written informed consent form before participating in NHANES. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Tianye Li and Hongjun Zhao contributed equally to this work and should be considered as co-first authors. 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All the datasets collected and analyzed during this study are available on the NHANES website: https://www.cdc.gov/nchs/nhanes/about/index.html . Articles from BMC Pulmonary Medicine are provided here courtesy of BMC ACTIONS View on publisher site PDF (1.3 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top