Subclinical Cardiac Dysfunction in Women With Prior Gestational Diabetes: The Hispanic Community Study/Study of Latinos
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes (GD) is associated with heart failure risk. However, the association of GD and postpartum early stages of myocardial dysfunction (a robust predictor of heart failure) as assessed by imaging has seldom been examined, especially among Hispanic women, who represent the fastest-growing ethnic minority population and have the highest prevalence of GD in US women. METHODS: We examined Hispanic women recruited to the Hispanic Community Study/Study of Latinos cohort, who reported at least 1 prior pregnancy and GD history at either visit 1 (2008-2011) or visit 2 (2014-2017) and echocardiographic assessments at visit 2. We used multivariable linear and logistic regression models to evaluate the associations between GD history and echocardiographic parameters. RESULTS: Among 2894 participants (mean age 53±9 years), 9.3% (n=270) had a GD history. After adjusting for cardiovascular disease risk factors, including current diabetes status, Hispanic women with GD history had higher mean adjusted left ventricular relative wall thickness (β=0.01 [95% CI, 0.00-0.02]), lateral peak early mitral inflow velocity to early diastolic velocity of the mitral annulus ratio (β=0.49 [95% CI, 0.09-0.89]), abnormal left ventricular diastolic function (adjusted odds ratio, 1.41 [95% CI, 1.04-1.91]), and lower mean adjusted left ventricular end-diastolic diameter (β=-0.07 [95% CI, -0.12 to -0.02]) compared with those without prior GD history. Similar associations were observed in results stratified by the most recent glycemic status. CONCLUSIONS: History of GD was associated with a higher frequency and severity of myocardial diastolic abnormalities. Echocardiographic-based screening for myocardial dysfunction in women with GD history has the potential to help avert overt cardiovascular disease in this high-risk population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".