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Record W4403153541 · doi:10.1210/jendso/bvae163.1558

8570 Association Between Preconception Lifestyle and Anthropometric Modifications and Fertility outcomes in Women with Infertility and Obesity

2024· article· en· W4403153541 on OpenAlexaff
Alexandra Thibodeau, CHRISTOPHE RICHER DIT LAFLÈCHE, Gérard Ngueta, Matea Bélan, Farrah Jean-Denis, Marie‐France Langlois, Marie-Hélène Pesant, Belina Carranza Mamane, Jean‐Patrice Baillargeon

Bibliographic record

VenueJournal of the Endocrine Society · 2024
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsInfertilityAnthropometryFertilityObesityMedicineAssociation (psychology)GynecologyObstetricsEnvironmental healthPsychologyPregnancyEndocrinologyInternal medicinePopulationBiologyGeneticsPsychotherapist

Abstract

fetched live from OpenAlex

Abstract Disclosure: A. Thibodeau: None. C. Richer dit Laflèche: None. G. Ngueta: None. M. Belan: None. F. Jean-Denis: None. M. Langlois: None. M. Pesant: None. B. Carranza Mamane: None. J. Baillargeon: Grant Recipient; Self; Ferring Pharmaceuticals. Introduction: Infertility is a common problem that is affected by many lifestyle behaviors, and preconception lifestyle modifications are recommended in women with infertility and obesity. This study thus aims to explore whether preconception lifestyle or anthropometric changes are associated with a viable pregnancy or a live birth in this population. Methods: This prospective cohort study, nested within a randomized controlled trial that recruited 127 women with infertility and obesity at an academic fertility clinic, reports on 70 women who had at least one research visit during their follow-up until the onset of pregnancy or a maximum of 18 months. Lifestyles were assessed using a questionnaire. Logistic regression was used to examine the association between changes in lifestyle or anthropometry and the occurrence of a viable pregnancy or live birth. Changes were defined by the incremental area under the curve (divided by follow-up time; iAUC) and the difference between baseline and the minimum or maximum measure achieved during follow-up. Regressions were corrected for baseline measures of the age of both partners, waist circumference, multiparity, socioeconomic status and multivitamin use. Results: The odds of a pregnancy and live birth were higher among women who had a greater minimum change (improvement) in their weekly consumption of fruits (p=.028/.014) or dairy products (p=.005/.003), in their reduction of tobacco use (p=.038/NS), in their sleep duration (p=.044/NS), or in their loss of weight, waist circumference or fat mass (p=.007/.007; .006/.006; .045/.019). Unexpectedly, a higher maximum change in energy expenditure was associated with lower odds of pregnancy (p=.013). Multivariate analyses showed that a higher minimum change in fruit consumption (p=.008), in tobacco use reduction (p=.033), and waist circumference loss (p=.002), and a lower maximum change in energy expenditure (p=.008) were all independently associated with the occurrence of pregnancy, after adjustment for the abovementioned potential confounders. For live birth, a higher minimum change in dairy products consumption (p=.012), in tobacco use reduction (p=.017), and in weight loss (p=.003) were all independently associated with a live birth. Conclusion: Our exploratory analyses are among the first to show that preconception lifestyle improvements are associated with the occurrence of a viable pregnancy and live birth in women with infertility and obesity, particularly higher minimal and sustained changes in fruit and dairy product intake and tobacco reduction. Higher minimal and sustained weight or waist circumference losses also contributed to improve their fertility, as expected. However, the negative association between higher energy expenditure and pregnancy was unexpected and requires further evaluation. Presentation: 6/1/2024

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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