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Record W4400765331 · doi:10.1097/aog.0000000000005658

Long-Term Risk of Type 2 Diabetes After Preterm Delivery or Hypertensive Disorders of Pregnancy

2024· article· en· W4400765331 on OpenAlexaboutno aff
Lionel Carbillon, Amélie Benbara

Bibliographic record

VenueObstetrics and Gynecology · 2024
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPregnancyMedicinePreterm deliveryType 2 diabetesTerm (time)Term pregnancyDiabetes mellitusObstetricsEndocrinologyGestation

Abstract

fetched live from OpenAlex

We read with interest the recent article by Crump, Jan Sundquist, and Kristina Sundquist,1 who conclude from their analysis of a Swedish national cohort that “...preterm delivery and hypertensive disorders of pregnancy [HDP] were associated with increased later risk for type 2 diabetes...” However, clearly (Table 1), rates of gestational diabetes mellitus (GDM), body mass index (BMI), and birth weight less than the 10th percentile were higher in the preterm delivery group and even higher in the HDP group, whereas birth weight above the 90th percentile was lower in the same order in these latter groups compared with the total population. These associations strongly suggest that the combination of metabolic factors (GDM, overweight, and obesity) with placental dysfunction could explain both the higher rates of small-for-gestational-age neonates and HDP (now more exactly called “preeclampsia”) and the lower rate of large-for-gestational-age neonates despite a higher GDM rate and higher BMI in these subgroups. A number of previous studies support such analysis. Hildén et al2 recently published a nested case–control study comprising a case group of 2,639 individuals with later cardiovascular events and a control group of 13,310 individuals without later cardiovascular events, provided by the Swedish National Council of Health and Welfare for the years 1991–2008. They demonstrate that the primary risk factor for subsequent cardiovascular events was the combination of GDM with preeclampsia. Wang et al3 analyzed a follow-up period of 2,609,753 person-years among 91,426 U.S. female nurses aged 25–42 years with previous GDM across their reproductive lifespans. They found from cause-specific mortality analyses that GDM, “was directly associated with the risk of mortality due to cardiovascular disease” and highlight a strong association between preeclampsia and cardiovascular disease (CVD). Sovio et al,4 using serial measures of anti-angiogenic ratio (a predictor of preeclampsia), show that placental dysfunction was associated with the risk of spontaneous preterm birth. Lastly, Ray et al5 have long demonstrated unambiguously from a Canadian population-based study that the future risk of CVD was highest with the combination of preeclampsia or related complications (that they gathered in the generic term “placental syndrome”) with the metabolic syndrome. This clear message must be conveyed, because these patients should benefit best from drastic lifestyle change measures and targeted follow-up.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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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