Examining patterns of gestational diabetes and gestational hypertension as risk indicators of cardiometabolic disease following two consecutive pregnancies
Bibliographic record
Abstract
Cardiometabolic disease (CMD) refers to a cluster of interrelated conditions that affect the cardiovascular system and metabolism, and arises from a combination of genetic predisposition, lifestyle factors (e.g., suboptimal dietary habits, physical inactivity, and smoking), and environmental influences (e.g., social environment, resources, neighborhood walkability, access to healthcare, etc.). Insulin resistance is a key CMD driver, contributing to elevated glucose levels, abnormal lipid levels, and elevated blood pressure, among other metabolic abnormalities. Among women of reproductive age, gestational diabetes (GDM) and gestational hypertension (GHTN) are pregnancy-related indicators of diabetes, hypertension, and cardiovascular disease (CVD), which are included under the umbrella term, CMD. However, knowledge gaps exist in terms of their implications beyond an ever occurrence/never occurrence dichotomy that is applied when performing risk assessments in clinical practice, irrespective of the number of occurrences and the number of pregnancies. This thesis focuses on women with at least two consecutive singleton livebirth pregnancies (between April 1, 1990 and December 31, 2012; followed up to April 1, 2019), to enhance comparability in terms of baseline CMD risk, which can vary with parity and is increased with infertility. The overarching aim is to examine all patterns of absence, new onset, and recurrence of GDM and/or GHTN (with or without preeclampsia) and their associations with maternal diabetes, hypertension, and CVD (myocardial infarction, stroke and unstable angina) development. Across the three retrospective cohort studies that I conducted, I used the province of Quebec’s health administrative and vital statistics (birth, stillbirth, and death registries, as applicable) data from nearly half a million women, their two offspring, and their partners (fathers of the offspring pair), and evaluated outcomes over a median of 11 years (hypertension), 11.5 years (diabetes), and 16.5 years (CVD). The first manuscript examined the association of GDM patterns across two pregnancies (none, first pregnancy only, second pregnancy only, both pregnancies) with the development of diabetes. I examined 431,980 Quebec women with two consecutive singleton livebirth deliveries and no history of diabetes, hypertension, or CVD before or between pregnancies. I built Cox proportional hazards (PH) models that accounted for GHTN and other pregnancy complications, among other co-variates. Limitations of Quebec’s health administrative and vital statistics databases include lack of information on adiposity and health behaviors, such as smoking status. To address limitations in these health administrative data, I incorporated simple sensitivity bias analyses to perform indirect adjustments for obesity and smoking, using external cohort data from the 2004 Canadian Community Health Survey (CCHS), Cycle 2.2. I demonstrated conclusive associations (hazard ratios [HR] and 95% confidence intervals [CI]) of GDM patterns and subsequent diabetes development that indicated increased hazards with GDM in the first pregnancy only, higher with GDM in the second pregnancy only, and highest with GDM in both (GDMFIRST=4.35 [4.06-4.67]; GDMSECOND=7.68 [7.31-8.07]; GDMBOTH=15.8 [15.0-16.6], compared to women without GDM in either pregnancy). Furthermore, conclusive differences across exposure groups persisted when I modified the reference group to allow other direct comparisons between exposure groups to be drawn. History of a GHTN occurrence was also associated with diabetes development, as were preterm delivery, large for gestational age (LGA) offspring, and partner history of diabetes. Overall, indirect adjustments for obesity slightly attenuated HRs, while indirect adjustments for smoking did not importantly affect HRs.I applied a similar approach using the same cohort to examine associations of GHTN with incident chronic hypertension in the second manuscript. GHTN represents new-onset blood pressure elevation in pregnancy and may occur with preeclampsia, which is characterized by placental and systemic vascular dysfunction causing organ injury, or without it. Therefore, in addition to examining GHTN with or without preeclampsia (combined), I also created two additional subcohorts of women, one which excluded those with GHTN without preeclampsia (N=412,735; women without any GHTN in either pregnancy, and those with preeclampsia in either pregnancy), and the other which excluded those with preeclampsia (N=414,875; women without any GHTN in either pregnancy, and those with GHTN without preeclampsia in either pregnancy). I used Cox PH models to estimate HRs, accounting for GDM and other adverse pregnancy occurrences, among other co-variates. I demonstrated conclusive associations between GHTN, with or without preeclampsia, and chronic hypertension, with elevated hazards for GHTN in the first pregnancy, higher with GHTN in the second, and highest for GHTN in both (GHTNFIRST=2.67 [2.57-2.78]; GHTNSECOND=4.85 [4.61-5.11]; GHTNBOTH=7.25 [6.90-7.63], compared to women without GHTN in either pregnancy), paralleling the findings I delineated for associations between GDM and diabetes. Patterns and estimates were similar for the two subcohorts described above. History of a GDM occurrence was also associated with hypertension development, as were preterm delivery, small for gestational age (SGA) and LGA offspring, and partner history of hypertension, diabetes or CVD. Similar to Manuscript 1, indirect adjustments for obesity slightly attenuated HRs, while indirectly adjusting for smoking did not importantly influence my effect estimates.The third manuscript examined the associations of GDM and GHTN (with or without preeclampsia) across two pregnancies with the development of CVD. Considering the presence or absence of GDM and of GHTN across two pregnancies resulted in 16 exposure categories. I opted to evaluate these categories as a secondary analysis and instead focused on the cumulative number of GDM and GHTN occurrences across two pregnancies in relationship to CVD for my primary analyses. I made this decision in recognition of the challenges that readers may face in interpreting 16 unique exposure categories and their respective HRs. In the same study cohort, utilizing Cox PH models, I observed that an increased number of occurrences of GDM and GHTN were associated with elevated hazards for CVD, in a stepwise pattern (1 occurrence=1.47 [1.35-1.61]; 2 occurrences=1.91 [1.68-2.17]; ≥3 occurrences=2.93 [2.20-3.90], compared to women without GDM and GHTN in either pregnancy). Indirect adjustments for obesity and smoking slightly attenuated these HRs. In the fourth manuscript, I conducted a scoping review that addresses the evolving algorithms for GDM screening, by collating guidelines released by major Canadian obstetric and diabetes organizations, highlighting shifts in their recommendations over time. This scoping review documents that variations in screening and diagnostic approaches existed between Diabetes Canada and the Society of Obstetricians and Gynecologists of Canada. Through the influence of the Hyperglycemia and Adverse Pregnancy Outcome study, these disparities have diminished, and many Canadian physicians now adhere to recent recommendations, as I demonstrated through a physician survey. Furthermore, given that the use of diagnostic codes to identify GDM (in Manuscripts 1 through 3) may be influenced by these temporal trends in guideline recommendations that I identified, I conducted additional analyses to examine if including the calendar year of each pregnancy (at 20 weeks’ gestation) impacted the effect estimates in each of my models. I observed no important differences in the associations of GDM with each of the assessed outcomes when attempting to account for temporal trends in the screening and diagnosis of GDM. In conclusion, this thesis underscores that in women who have two or more singleton livebirth deliveries, representing over half of women globally, consideration of GDM and GHTN occurrences or absences in each pregnancy can further nuance estimates of future diabetes, hypertension, and CVD risk. These findings may permit personalized risk estimation, enabling clinicians and patients to determine the urgency and importance of preventive interventions and close surveillance
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".