Describing the status of reproductive ageing simply and precisely: A reproductive ageing score based on three questions and validated with hormone levels
Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported...
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| Published in | PloS one Vol. 15; no. 6; p. e0235478 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Public Library of Science
30.06.2020
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| Abstract | Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17[beta]-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90-0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83-0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. |
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| AbstractList | Objective
Most women live to experience menopause and will spend 4–8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers.
Methods
Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17β-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC).
Results
The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90–0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83–0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women.
Conclusions
The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. ObjectiveMost women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers.MethodsQuestionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17β-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC).ResultsThe RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90-0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83-0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women.ConclusionsThe RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. Equation 6. Quadratic logistic function approximating the function mu(B)(with age in years). Equation 1. Proportion of women who have regular menstruation for each number of reported menstruations in the last year(with period = number of periods per year, x = number of women answering "Yes" to the question: "Do you have regular periods?", y = number of women answering "No, they have been irregular for a few months" and z = number of women answering "No, my periods have stopped", e.g. x(11) = number of women reporting regular menstruation among those who report 11 menstruations in the last 12 months). Equation 5. Biquadratic exponential function mu(A)depending of the number of periods. Equation 3. Age modification by smoking and oophorectomy. Equation 2. Proportion of women whose menstruations have already stopped, for each reported year of age(with age = age in years, x = number of women answering "Yes" to the question: "Do you have regular periods?", y = number of women answering "No, they have been irregular for a few months", z = number of women answering "No, my periods have stopped", e.g. x(40) = number of women reporting regular menstruations among those who are 40 years old). Equation 7. Final formula to calculate the reproductive ageing score (RAS)(with period being the number of periods per year and age as the age in years, modified according to smoking status and oophorectomy). Objective Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Methods Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17 beta-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). Results The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90-0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83-0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. Conclusions The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. Equation 4. The reproductive ageing score as an aggregation function of mu(A)and mu(B). Objective Most women live to experience menopause and will spend 4–8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Methods Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17β-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). Results The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90–0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83–0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. Conclusions The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. Objective Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Methods Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17[beta]-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). Results The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90-0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83-0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. Conclusions The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17[beta]-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90-0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83-0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. Objective: Most women live to experience menopause and will spend 4–8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause; categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Methods: Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17β-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). Results: The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90–0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83–0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. Conclusions: The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. |
| Audience | Academic |
| Author | Schlünssen, Vivi Triebner, Kai Tsaneva-Atanasova, Krasimira Leynaert, Bénédicte Gómez Real, Francisco Jarvis, Deborah Rovira, Jesús Martínez Moratalla Rodriguez, Francisco J Benediktsdóttir, Bryndís Dharmage, Shyamali C Skulstad, Svein Magne Heinrich, Joachim Hustad, Steinar Holm, Mathias Svanes, Cecilie Demoly, Pascal Franklin, Karl A Lindberg, Eva Sánchez-Ramos, José Luis Muniozguren Agirre, Nerea Johannessen, Ane |
| AuthorAffiliation | University of Exeter, UNITED KINGDOM 5 Team of Epidemiology, Inserm UMR1152, Paris, France 10 Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Ludwig Maximilians University Munich, Munich, Germany 12 Department of Respiratory Epidemiology, Occupational Medicine and Public Health, Imperial College, London, England, United Kingdom 7 Department of Pulmonology—Division of Allergy, University Hospital of Montpellier, Montpellier, France 13 Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden 4 Department of Occupational Medicine, Haukeland University Hospital, Bergen, Norway 9 Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden 3 Department of Global Public Health and Primary Care, Centre for International Health, University of Bergen, Bergen, Norway 19 Department of Applied Mathematics, University of Malaga, Malaga, Spain 17 Department of Public Health, Aarhus University, Aarhus, |
| AuthorAffiliation_xml | – name: 9 Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden – name: 20 Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway – name: 6 Faculty of Medicine, University of Iceland, Reykjavik, Iceland – name: 19 Department of Applied Mathematics, University of Malaga, Malaga, Spain – name: 10 Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Ludwig Maximilians University Munich, Munich, Germany – name: University of Exeter, UNITED KINGDOM – name: 14 Servicio de Neumología, Servicio de Salud de Castilla–La Mancha, Albacete, Spain – name: 2 Core Facility for Metabolomics, University of Bergen, Bergen, Norway – name: 15 Epidemiologia, Salud Pública, Departamento de Salud, Gobierno Vasco, Galdakao, Spain – name: 11 Department of Occupational and Environmental Medicine, Göteborgs Universitet, Göteborg, Sweden – name: 7 Department of Pulmonology—Division of Allergy, University Hospital of Montpellier, Montpellier, France – name: 18 National Research Center for the Working Environment, Copenhagen, Denmark – name: 3 Department of Global Public Health and Primary Care, Centre for International Health, University of Bergen, Bergen, Norway – name: 5 Team of Epidemiology, Inserm UMR1152, Paris, France – name: 1 Department of Clinical Science, University of Bergen, Bergen, Norway – name: 12 Department of Respiratory Epidemiology, Occupational Medicine and Public Health, Imperial College, London, England, United Kingdom – name: 4 Department of Occupational Medicine, Haukeland University Hospital, Bergen, Norway – name: 13 Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden – name: 17 Department of Public Health, Aarhus University, Aarhus, Denmark – name: 8 Allergy and Lung Health Unit, Melbourne School of Population Health, University of Melbourne, Melbourne, Australia – name: 16 Department of Nursing, University of Huelva, Huelva, Spain |
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| CitedBy_id | crossref_primary_10_1186_s13104_022_05970_z crossref_primary_10_1371_journal_pone_0269569 |
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| DOI | 10.1371/journal.pone.0235478 |
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| Notes | PMCID: PMC7326235 Competing Interests: The authors have declared that no competing interests exist. |
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| References | YF Zollner (pone.0235478.ref028) 2005; 14 R Belohlavek (pone.0235478.ref016) 2016; 70 P Ney de Souza (pone.0235478.ref023) 2004 SD Harlow (pone.0235478.ref026) 2012; 15 GE Hale (pone.0235478.ref025) 2007; 92 S West (pone.0235478.ref013) 2014; 29 BC Sydora (pone.0235478.ref027) 2016 J Guthrie (pone.0235478.ref007) 2002; 5 LA Zadeh (pone.0235478.ref015) 1969 L Speroff (pone.0235478.ref021) 1999; 900 D KJ Jarvis (pone.0235478.ref011) 2002; 20 A Allshouse (pone.0235478.ref002) 2018; 45 LA Zadeh (pone.0235478.ref014) 1965; 8 EK Bjelland (pone.0235478.ref020) 2014; 29 PG Burney (pone.0235478.ref010) 1994; 7 MA Lumsden (pone.0235478.ref001) 2019; 22 JM Rodríguez-Jiménez (pone.0235478.ref017) 2016 HG Burger (pone.0235478.ref008) 2005; 12 L Speroff (pone.0235478.ref022) 2011 CR Gracia (pone.0235478.ref003) 2018; 45 JF Randolph (pone.0235478.ref004) 2011; 96 J Dratva (pone.0235478.ref019) 2009; 16 SD Harlow (pone.0235478.ref006) 2012; 19 MR Sowers (pone.0235478.ref005) 2008; 93 SD Harlow (pone.0235478.ref018) 2011; 38 R Development Core Team (pone.0235478.ref024) 2018 A Johannessen (pone.0235478.ref009) 2014; 14 S Taponen (pone.0235478.ref012) 2003; 88 |
| References_xml | – volume: 29 start-page: 835 issue: 4 year: 2014 ident: pone.0235478.ref020 article-title: Is unilateral oophorectomy associated with age at menopause? A population study (the HUNT2 Survey) publication-title: Hum Reprod doi: 10.1093/humrep/deu026 contributor: fullname: EK Bjelland – volume: 96 start-page: 746 issue: 3 year: 2011 ident: pone.0235478.ref004 article-title: Change in follicle-stimulating hormone and estradiol across the menopausal transition: effect of age at the final menstrual period publication-title: The Journal of clinical endocrinology and metabolism doi: 10.1210/jc.2010-1746 contributor: fullname: JF Randolph – volume: 16 start-page: 385 issue: 2 year: 2009 ident: pone.0235478.ref019 article-title: Is age at menopause increasing across Europe? 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volume-title: Clinical gynecologic endocrinology and infertility year: 2011 ident: pone.0235478.ref022 contributor: fullname: L Speroff – volume: 45 start-page: 585 issue: 4 year: 2018 ident: pone.0235478.ref003 article-title: Onset of the Menopause Transition: The Earliest Signs and Symptoms publication-title: Obstet Gynecol Clin North Am doi: 10.1016/j.ogc.2018.07.002 contributor: fullname: CR Gracia – volume: 15 start-page: 105 issue: 2 year: 2012 ident: pone.0235478.ref026 article-title: Executive summary of the Stages of Reproductive Aging Workshop +10: addressing the unfinished agenda of staging reproductive aging publication-title: Climacteric doi: 10.3109/13697137.2011.650656 contributor: fullname: SD Harlow – volume: 29 start-page: 2339 issue: 10 year: 2014 ident: pone.0235478.ref013 article-title: Irregular menstruation and hyperandrogenaemia in adolescence are associated with polycystic ovary syndrome and infertility in later life: Northern Finland Birth Cohort 1986 study 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| Snippet | Objective Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive... Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a... Objective Most women live to experience menopause and will spend 4–8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive... Objective: Most women live to experience menopause and will spend 4–8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive... Objective: Most women live to experience menopause and will spend 4–8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive... Objective Most women live to experience menopause and will spend 4–8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive... Equation 6. Quadratic logistic function approximating the function mu(B)(with age in years). Equation 1. Proportion of women who have regular menstruation for... ObjectiveMost women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive... |
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| SubjectTerms | 17β-Estradiol Adult Age Aged Aging Aging (Biology) Biology and Life Sciences Blood Clinical trials Cohort Studies Confidence intervals cycles Endocrinology and metabolism Environmental health Environmental Health and Occupational Health Epidemiology Estradiol Female Follicle Stimulating Hormone Gynecology and obstetrics Health aspects Health surveys Hormones Hospitals Human health and pathology Humans Life Sciences Longitudinal Studies Mathematical analysis Medical personnel Medical research Medicine Medicine and Health Sciences Menopause menopause transition menstrual Menstruation Middle Aged Miljömedicin och yrkesmedicin Observations People and Places Perimenopause Population Post-menopause Postmenopause Primary care Public health quality-of-life Questionnaires Questions Reproduction Reproductive health Research and Analysis Methods Researchers Rodriguez, Francisco Science & Technology - Other Topics Sex Sex hormones Surveys and Questionnaires unfinished agenda Womens health |
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| Title | Describing the status of reproductive ageing simply and precisely: A reproductive ageing score based on three questions and validated with hormone levels |
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