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Record W4399827474 · doi:10.1093/humrep/deae118

The association between reproductive history and abdominal adipose tissue among postmenopausal women: results from the Women’s Health Initiative

2024· article· en· W4399827474 on OpenAlexaff
Hailey R. Banack, Claire E. Cook, Sonia M. Grandi, Natalie V. Scime, Rana Andary, Shawna Follis, Matthew Allison, JoAnn E. Manson, Su Yong Jung, Robert A. Wild, Leslie V. Farland, Aladdin H. Shadyab, Jennifer W. Bea, Andrew Odegaard

Bibliographic record

VenueHuman Reproduction · 2024
Typearticle
Languageen
FieldMedicine
TopicHypothalamic control of reproductive hormones
Canadian institutionsThe Scarborough HospitalSickKids FoundationPublic Health OntarioUniversity of Toronto
FundersNational Institute on Aging
KeywordsMenarcheMenopauseMedicineBody mass indexGynecologyObstetricsAnthropometryReproductive healthDemographyPopulationInternal medicine

Abstract

fetched live from OpenAlex

STUDY QUESTION: What is the association between reproductive health history (e.g. age at menarche, menopause, reproductive lifespan) with abdominal adiposity in postmenopausal women? SUMMARY ANSWER: Higher visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) tissue levels were observed among women with earlier menarche, earlier menopause, and greater parity. WHAT IS KNOWN ALREADY: Postmenopausal women are predisposed to accumulation of VAT and SAT. Reproductive health variables are known predictors of overall obesity status in women, defined by BMI. STUDY DESIGN, SIZE, DURATION: This study is a secondary analysis of data collected from the baseline visit of the Women's Health Initiative (WHI). The WHI is a large prospective study of postmenopausal women, including both a randomized trial and observational study. There were 10 184 women included in this analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS: Data were collected from a reproductive health history questionnaire, dual-energy x-ray absorptiometry scans, and anthropometric measures at WHI baseline. Reproductive history was measured via self-report, and included age at menarche, variables related to pregnancy, and age at menopause. Reproductive lifespan was calculated as age at menopause minus age at menarche. Statistical analyses included descriptive analyses and multivariable linear regression models to examine the association between reproductive history with VAT, SAT, total body fat, and BMI. MAIN RESULTS AND THE ROLE OF CHANCE: Women who reported early menarche (<10 years) or early menopause (<40 years) had the highest levels of VAT. Adjusted multivariable linear regression results demonstrate women who experienced menarche >15 years had 23 cm2 less VAT (95% CI: -31.4, -14.4) and 47 cm2 less SAT (95% CI: -61.8, -33.4) than women who experienced menarche at age 10 years or earlier. A similar pattern was observed for age at menopause: compared to women who experienced menopause <40 years, menopause at 50-55 years was associated with 19.3 cm2 (95% CI: -25.4, -13.3) less VAT and 27.4 cm2 (-29.6, 10.3) less SAT. High parity (>3 pregnancies) was also associated with VAT and SAT. For example, adjusted beta coefficients for VAT were 8.36 (4.33, 12.4) and 17.9 (12.6, 23.2) comparing three to four pregnancies with the referent, one to two pregnancies. LIMITATIONS, REASONS FOR CAUTION: The WHI reproductive health history questionnaire may be subject to poor recall owing to a long look-back window. Residual confounding may be present given lack of data on early life characteristics, such as maternal and pre-menarche characteristics. WIDER IMPLICATIONS OF THE FINDINGS: This study contributes to our understanding of reproductive lifespan, including menarche and menopause, as an important predictor of late-life adiposity in women. Reproductive health has also been recognized as a sentinel marker for chronic disease in late life. Given established links between adiposity and cardiometabolic outcomes, this research has implications for future research, clinical practice, and public health policy that makes use of reproductive health history as an opportunity for chronic disease prevention. STUDY FUNDING/COMPETING INTEREST(S): HRB and AOO are supported by the National Institute of Health National Institute of Aging (R01AG055018-04). JWB reports royalties from 'ACSM'S Body Composition Assessment Book' and consulting fees from the WHI. The remaining authors have no competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.282
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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