Menopause Is Associated with Accelerated Lung Function Decline.

RATIONALE: Menopause is associated with changes in sex hormones, which affect immunity, inflammation, and osteoporosis and may impair lung function. Lung function decline has not previously been investigated in relation to menopause. OBJECTIVES: To study whether lung function decline, assessed by FVC and FEV1, is accelerated in women who undergo menopause. METHODS: The population-based longitudinal European Community Respiratory Health Survey provided serum samples, spirometry, and questionnaire data about respiratory and reproductive health from three study waves (n = 1,438). We measured follicle-stimulating hormone and luteinizing hormone and added information on menstrual patterns to determine menopausal status using latent class analysis. Associations with lung function decline were investigated using linear mixed effects models, adjusting for age, height, weight, pack-years, current smoking, age at completed full-time education, spirometer, and including study center as random effect. MEASUREMENTS AND MAIN RESULTS: Menopausal status was associated with accelerated lung function decline. The adjusted mean FVC decline was increased by -10.2 ml/yr (95% confidence interval [CI], -13.1 to -7.2) in transitional women and -12.5 ml/yr (95% CI, -16.2 to -8.9) in post-menopausal women, compared with women menstruating regularly. The adjusted mean FEV1 decline increased by -3.8 ml/yr (95% CI, -6.3 to -2.9) in transitional women and -5.2 ml/yr (95% CI, -8.3 to -2.0) in post-menopausal women. CONCLUSIONS: Lung function declined more rapidly among transitional and post-menopausal women, in particular for FVC, beyond the expected age change. Clinicians should be aware that respiratory health often deteriorates during reproductive aging.

Menopause as a predictor of new-onset asthma: A longitudinal Northern European population study.

BACKGROUND: There is limited and conflicting evidence on the effect of menopause on asthma. OBJECTIVES: We sought to study whether the incidence of asthma and respiratory symptoms differ by menopausal status in a longitudinal population-based study with an average follow-up of 12 years. METHODS: The Respiratory Health in Northern Europe study provided questionnaire data pertaining to respiratory and reproductive health at baseline (1999-2001) and follow-up (2010-2012). The study cohort included women aged 45 to 65 years at follow-up, without asthma at baseline, and not using exogenous hormones (n = 2322). Menopausal status was defined as nonmenopausal, transitional, early postmenopausal, and late postmenopausal. Associations with asthma (defined by the use of asthma medication, having asthma attacks, or both) and respiratory symptoms scores were analyzed by using logistic (asthma) and negative binomial (respiratory symptoms) regressions, adjusting for age, body mass index, physical activity, smoking, education, and study center. RESULTS: The odds of new-onset asthma were increased in women who were transitional (odds ratio, 2.40; 95% CI, 1.09-5.30), early postmenopausal (odds ratio, 2.11; 95% CI, 1.06-4.20), and late postmenopausal (odds ratio, 3.44; 95% CI, 1.31-9.05) at follow-up compared with nonmenopausal women. The risk of respiratory symptoms increased in early postmenopausal (coefficient, 0.40; 95% CI, 0.06-0.75) and late postmenopausal (coefficient, 0.69; 95% CI, 0.15-1.23) women. These findings were consistent irrespective of smoking status and across study centers. CONCLUSIONS: New-onset asthma and respiratory symptoms increased in women becoming postmenopausal in a longitudinal population-based study. Clinicians should be aware that respiratory health might deteriorate in women during reproductive aging.

Forward Selection Component Analysis: Algorithms and Applications.

Principal Component Analysis (PCA) is a powerful and widely used tool for dimensionality reduction. However, the principal components generated are linear combinations of all the original variables and this often makes interpreting results and root-cause analysis difficult. Forward Selection Component Analysis (FSCA) is a recent technique that overcomes this difficulty by performing variable selection and dimensionality reduction at the same time. This paper provides, for the first time, a detailed presentation of the FSCA algorithm, and introduces a number of new variants of FSCA that incorporate a refinement step to improve performance. We then show different applications of FSCA and compare the performance of the different variants with PCA and Sparse PCA. The results demonstrate the efficacy of FSCA as a low information loss dimensionality reduction and variable selection technique and the improved performance achievable through the inclusion of a refinement step.