MétaCan
Menu
Back to cohort
Record W6998326230

Addressing gaps in cardiometabolic health and nutrition in women of reproductive age

2022· dissertation· en· W6998326230 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Repository UCD (University College Dublin) · 2022
Typedissertation
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPregnancyOverweightObesityReproductive healthDiseaseOvernutritionPublic healthMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

Most women do not meet dietary guidelines before or during pregnancy, and overweight or obesity are becoming the predominant presentation in antenatal services. Novel strategies to improve health before pregnancy are of interest, but research with women outside of pregnancy is impacted by issues with recruitment and retention. Clinical risk categorisation schema such as the Edmonton Obesity Staging System (EOSS) and the Cardiometabolic Disease Staging System (CMDS), show promise in guiding treatment prioritisation in the general population, but their use in pregnancy has not been adequately considered. The World Health Organisation recommends that all women receive nutrition and weight counselling during pregnancy. Current antenatal practices, however, do not address nutrition as standard. There is also no consensus on which outcomes are most important for pregnancy nutrition interventions, with little consideration for the ‘patient voice’ in what is evaluated. The aims of this thesis are to investigate the potential of pre-existing clinical practice tools to address cardiometabolic health and nutrition in women of reproductive age and to explore the priorities for nutrition research from the perspective of key stakeholders, including women before and during pregnancy. Outside of pregnancy, we found the EOSS characterised more women with obesity (81.3%) as metabolically unhealthy. This high prevalence potentially limits the clinical utility of the tool in delineating risk. Conversely, we found the CMDS characterised 46.9% of these women as metabolically unhealthy. We also found a relationship between inflammatory marker C3 Complement protein and cardiometabolic phenotype. In our mixed-methods study, we women of reproductive age reported altruistic motivations for taking part in preconception research, were recruited mostly by digital means and prioritised wellbeing over traditional health measures. In pregnancy, the limitations of the EOSS system were also highlighted, given the high prevalence of “at risk” categorisation, especially in late pregnancy (98.9%). We found an antenatal lifestyle intervention that of healthy eating and low glycaemic index (GI) dietary advice was successful in reducing the dietary inflammatory potential of women with overweight or obesity. This suggests that a low GI and healthy eating intervention may be useful in improving inflammation and cardiometabolic health. Our data suggests that the International Federation of Gynaecology and Obstetrics Nutrition (FIGO) Checklist is an acceptable and likely feasible resource to facilitate conversations on nutrition and weight during routine antenatal care. Finally, we identified 13 core outcomes for pregnancy nutrition research. These are pregnancy complications, gestational weight change; maternal vitamin and mineral status including anaemia; mental health; diet quality; nutritional intakes; need for treatments, interventions, medications, and supplements; pregnancy loss or perinatal death; birth defects or congenital anomalies, neonatal complications, new-born anthropometry and body composition; maternal wellbeing and delivery complications. Measurement of this core outcome set as a standard support will assist in the advancement of antenatal nutrition, by generating evidence for outcomes most important to stakeholders, including pregnant women. In conclusion, our data suggests a greater focus on well-being is needed in women’s health. C3 complement protein may hold potential as a novel risk marker in obesity and the FIGO Nutrition Checklist may support clinicians in appropriately addressing healthy eating and weight women of reproductive age. This may have benefits for reducing inflammation in women with overweight or obesity.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0000.000
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 head0.057
GPT teacher head0.366
Teacher spread0.309 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueResearch Repository UCD (University College Dublin)Same topicGestational Diabetes Research and ManagementFrench-language works237,207