Liver Enzymes in Early to Mid-pregnancy, Insulin Resistance, and Gestational Diabetes Risk: A Longitudinal Analysis

Background: Liver enzymes may be implicated in glucose homeostasis; liver enzymes progressively change during pregnancy but longitudinal data during pregnancy in relation to insulin resistance and gestational diabetes (GDM) risk are lacking. We investigated longitudinal associations of γ-glutamyl transferase (GGT) and alanine aminotransferase (ALT) with insulin secretion and resistance markers across early to mid-pregnancy and subsequent GDM risk.Methods: Within the prospective Pregnancy Environment and Lifestyle Study cohort, 117 GDM cases were ascertained and matched to 232 non-GDM controls in a nested case-control study. Fasting blood samples were collected at two clinic visits (CV1, gestational weeks 10–13; CV2, gestational weeks 16–19). Linear mixed model and conditional logistic regression were used, adjusting for major risk factors for GDM.Results: In repeated measure analysis, after adjusting for confounders including body mass index and waist-to-hip ratio, GGT per standard deviation increment was associated with elevated fasting glucose and HOMA-IR (% change = 1.51%, 95% CI 0.56–2.46% and 7.43%, 95% CI 1.76–13.11%, respectively) and decreased adiponectin (% change = −2.86%, 95% CI−5.53 to −0.20%) from CV1 to CV2. At CV1 and CV2, GGT levels comparing the highest versus lowest quartile were associated with 3.01-fold (95% CI 1.32–6.85) and 3.51-fold (95% CI 1.37–8.97) increased risk of GDM, respectively. Progressively increased (<median at CV1, ≥median at CV2) and stably high (≥median at both CV1 and CV2) GGT levels were associated with 3.89- and 2.39-fold increased risk of GDM, compared to stably low levels (<median at both CV1 and CV2), respectively (both P < 0.05). Similar but non-significant trends were observed for ALT.Conclusion: Elevated levels of GGT in early and mid-pregnancy, even within the conventional normal range, and its progressive increase from early to mid-pregnancy may be implicated in the pathogenesis of GDM, highlighting its potential to inform early screening or preventive strategies to mitigate subsequent risk of GDM

Predictive Metabolomic Markers in Early to Mid-pregnancy for Gestational Diabetes Mellitus: A Prospective Test and Validation Study.

Gestational diabetes mellitus (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (GDM n = 91 and non-GDM n = 180; discovery set), a random PETALS subsample (GDM n = 42 and non-GDM n = 372; validation set 1), and a case-control sample within the GLOW trial (GDM n = 35 and non-GDM n = 70; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry. Multivariate enrichment analysis examined associations between metabolites and GDM. Ten-fold cross-validated LASSO regression identified predictive metabolomic markers at gestational weeks (GW) 10-13 and 16-19 for GDM. Purinone metabolites at GW 10-13 and 16-19 and amino acids, amino alcohols, hexoses, indoles, and pyrimidine metabolites at GW 16-19 were positively associated with GDM risk (false discovery rate <0.05). A 17-metabolite panel at GW 10-13 outperformed the model using conventional risk factors, including fasting glycemia (area under the curve: discovery 0.871 vs. 0.742, validation 1 0.869 vs. 0.731, and validation 2 0.972 vs. 0.742; P < 0.01). Similar results were observed with a 13-metabolite panel at GW 17-19. Dysmetabolism is present early in pregnancy among individuals progressing to GDM. Multimetabolite panels in early pregnancy can predict GDM risk beyond conventional risk factors

Healthy preconception and early-pregnancy lifestyle and risk of preterm birth: a prospective cohort study.

BackgroundPreterm birth (PTB) remains a leading cause of neonatal mortality and long-term morbidity. Individual factors have been linked to PTB risk. The impact of a healthy lifestyle, with multiple modifiable prenatal factors, remains unknown.ObjectivesWe aimed to examine the associations of preconceptional and early-pregnancy low-risk modifiable factors (individually and in combination) with PTB risk.MethodsThis prospective cohort study included 2449 women with singleton pregnancies in the Pregnancy Environment and Lifestyle Study. PTB was defined as ultrasound-confirmed obstetric estimate-based gestational age at delivery <37 wk. A set of low-risk modifiable factors were identified: healthy weight (prepregnancy BMI: 18.5-24.9 kg/m2) based on clinical measurements and high-quality diet (Alternate Healthy Eating Index-Pregnancy score ≥75th percentile) and low-to-moderate stress during early pregnancy (Perceived Stress Scale score <75th percentile) assessed at gestational weeks 10-13. Poisson regression estimated adjusted relative risk (aRR) of PTB in association with individual and combined low-risk modifiable prenatal factors, adjusting for sociodemographic, clinical, and other prenatal factors.ResultsOne hundred and sixty women (6.5%) delivered preterm. Risk of PTB was lower among women who had a healthy weight (aRR: 0.58; 95% CI: 0.39, 0.86), high-quality diet (aRR: 0.68; 95% CI: 0.39, 0.99), and low-to-moderate stress (aRR: 0.60; 95% CI: 0.41, 0.88). Women with 1, 2, or 3 low-risk modifiable prenatal factors compared with none had a 38% (aRR: 0.72; 95% CI: 0.45, 1.16), 51% (aRR: 0.49; 95% CI: 0.29, 0.84), or 70% (aRR: 0.30; 95% CI: 0.13, 0.70) lower PTB risk, respectively. Associations of having ≥1 low-risk factor with PTB risk were more pronounced for medically indicated than for spontaneous PTB and for late than for early or moderate PTB. Associations also varied by race or ethnicity, although with overlapping 95% CIs.ConclusionsA healthy prenatal lifestyle with multiple low-risk modifiable factors was associated with lower risk of PTB. Our findings may inform multicomponent preconceptional or early-pregnancy prevention strategies to mitigate PTB risk

Uptake of guideline-recommended postpartum diabetes screening among diverse women with gestational diabetes: associations with patient factors in an integrated health system in USA.

IntroductionClinical guidelines urge timely postpartum screening for diabetes among women with gestational diabetes mellitus (GDM), yet patient factors associated with screening uptake remain unclear. We aimed to identify patient factors associated with completed postpartum diabetes screening (2-hour oral glucose tolerance test within 4-12 weeks postpartum), as recommended by the American Diabetes Association (ADA).Research design and methodsWithin the context of Gestational Diabetes' Effects on Moms (GEM), a pragmatic cluster randomized trial (2011-2012), we examined survey and electronic health record data to assess clinical and sociodemographic factors associated with uptake of ADA-recommended postpartum screening. Participants included 1642 women (76% racial/ethnic minorities) identified with GDM according to the Carpenter and Coustan criteria in a health system that deploys population-level strategies to promote screening. To contextualize these analyses, screening rates derived from the GEM trial were compared with those in the health system overall using registry data from a concurrent 10-year period (2007-2016, n=21 974).ResultsOverall 52% (n=857) completed recommended postpartum screening in the analytic sample, comparable to 45.7% (n=10 040) in the registry. Screening in the analytic sample was less likely among women at elevated risk for type 2 diabetes, assessed using items from an ADA risk test (vs non-elevated; adjusted rate ratio (aRR)=0.86 (95% CI 0.75 to 0.98)); perinatal depression (0.88 (0.79 to 0.98)); preterm delivery (0.84 (0.72 to 0.98)); parity ≥2 children (vs 0; 0.80 (0.69 to 0.93)); or less than college education (0.79 (0.72 to 0.86)). Screening was more likely among Chinese Americans (vs White; 1.31 (1.15 to 1.49)); women who attended a routine postpartum visit (5.28 (2.99 to 9.32)); or women who recalled receiving healthcare provider advice about screening (1.31 (1.03 to 1.67)).ConclusionsGuideline-recommended postpartum diabetes screening varied by patient clinical and sociodemographic factors. Findings have implications for developing future strategies to improve postpartum care

Intrinsic motivation for physical activity, healthy eating, and self-weighing in association with corresponding behaviors in early pregnancy.

Healthy lifestyle behaviors influence maternal cardiovascular health, but motivation for them in pregnancy is poorly understood. We examined whether intrinsic motivation (assessed on 5-point scales for each behavior) is associated with three lifestyle behaviors in early pregnancy: physical activity, by intensity level; healthy eating, quantified with the Alternate Healthy Eating Index for Pregnancy (AHEI-P); and weight self-monitoring, a standard weight management technique. Participants in the Northern California Pregnancy, Lifestyle and Environment Study (PETALS) population-based cohort completed validated surveys in early pregnancy (2017-18; N = 472; 22 % Asian, 6 % Black, 30 % Hispanic, 13 % multiracial, 30 % White). Cross-sectional data were analyzed in 2021-22. Overall, 40.7 % (n = 192) met United States national physical activity guidelines; the average AHEI-P score was 62.3 out of 130 (SD 11.4); and 36.9 % reported regular self-weighing (≥once/week; n = 174). In models adjusted for participant characteristics, 1-unit increases in intrinsic motivation were associated with increased likelihood of meeting physical activity guidelines (risk ratio [95 % CI]: 1.66 [1.48, 1.86], p < 0.0001); meeting sample-specific 75th percentiles for vigorous physical activity (1.70 [1.44, 1.99], p < 0.0001) and AHEI-P (1.75 [1.33, 2.31], p < 0.0001); and regular self-weighing (2.13 [1.92, 2.37], p < 0.0001). A 1-unit increase in intrinsic motivation lowered the risk of meeting the 75th percentile for sedentary behavior (0.79 [0.67, 0.92], p < 0.003). Intrinsic motivation was not associated with reaching 75th percentiles for total, light, or moderate activity. Intrinsic motivation is associated with physical activity, healthy eating, and self-weighing among diverse individuals in early pregnancy. Results can inform intervention design to promote maternal health via increased enjoyment of lifestyle behaviors

PFAS concentrations in early and mid-pregnancy and risk of gestational diabetes mellitus in a nested case-control study within the ethnically and racially diverse PETALS cohort

Abstract Background Per- and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals and are commonly found in everyday items. PFAS have been linked to disrupting glucose homeostasis, however, whether they are associated with gestational diabetes mellitus (GDM) risk remains inconclusive. We examined prospective associations of PFAS concentrations measured twice in pregnancy with GDM risk. Methods In the PETALS pregnancy cohort, a nested case–control study which included 41 GDM cases and 87 controls was conducted. PFAS analytes were measured in blood serum collected in both early and mid-pregnancy (mean [SD]: 13.9 [2.2] and 20.2 [2.2] gestational weeks, respectively), with cumulative exposure calculated by the area-under-the-curve (AUC) to integrate both the PFAS concentration and the timing of the exposure. Individual adjusted weighted unconditional logistic regression models examined seven PFAS in association with GDM risk. P-values were corrected using the false-discovery-rate (FDR). Mixture models were analyzed with Bayesian kernel machine regression (BKMR). Results PFDA, PFNA and PFOA were individually associated with higher GDM risk per interquartile range (IQR) in early pregnancy (OR [95% CI]: 1.23 [1.09, 1.38]), 1.40 [1.24, 1.58]), and 1.15 [1.04, 1.27], respectively), mid-pregnancy (1.28 [1.15, 1.43], 1.16 [1.05, 1.28], and 1.20 [1.09, 1.33], respectively), and with cumulative exposure (1.23 [1.09, 1.38], 1.21 [1.07, 1.37], and 1.19 [1.09, 1.31], respectively). PFOS in mid-pregnancy and with cumulative exposure was associated with increased GDM risk (1.41 [1.17, 1.71] and 1.33 [1.06, 1.58], respectively). PFUnDA in early pregnancy was associated with lower GDM risk (0.79 [0.64, 0.98]), whereas mid-pregnancy levels were associated with higher risk (1.49 [1.18, 1.89]). PFHxS was associated with decreased GDM risk in early and mid-pregnancy (0.48 [0.38, 0.60] and 0.48 [0.37, 0.63], respectively) and with cumulative exposure (0.49 [0.38,0.63]). PFPeA was not associated with GDM. Similar conclusions were observed in BKMR models; however, overall associations in these models were not statistically significant. Conclusions Higher risk of GDM was consistently observed in association with PFDA, PFNA, and PFOA exposure in both early and mid-pregnancy. Results should be corroborated in larger population-based cohorts and individuals of reproductive age should potentially avoid known sources of PFAS

Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study

BackgroundGestational diabetes (GDM) is prevalent and benefits from timely and effective treatment, given the short window to impact glycemic control. Clinicians face major barriers to choosing effectively among treatment modalities [medical nutrition therapy (MNT) with or without pharmacologic treatment (antidiabetic oral agents and/or insulin)]. We investigated whether clinical data at varied stages of pregnancy can predict GDM treatment modality.MethodsAmong a population-based cohort of 30,474 pregnancies with GDM delivered at Kaiser Permanente Northern California in 2007-2017, we selected those in 2007-2016 as the discovery set and 2017 as the temporal/future validation set. Potential predictors were extracted from electronic health records at different timepoints (levels 1-4): (1) 1-year preconception to the last menstrual period, (2) the last menstrual period to GDM diagnosis, (3) at GDM diagnosis, and (4) 1 week after GDM diagnosis. We compared transparent and ensemble machine learning prediction methods, including least absolute shrinkage and selection operator (LASSO) regression and super learner, containing classification and regression tree, LASSO regression, random forest, and extreme gradient boosting algorithms, to predict risks for pharmacologic treatment beyond MNT.ResultsThe super learner using levels 1-4 predictors had higher predictability [tenfold cross-validated C-statistic in discovery/validation set: 0.934 (95% CI: 0.931-0.936)/0.815 (0.800-0.829)], compared to levels 1, 1-2, and 1-3 (discovery/validation set C-statistic: 0.683-0.869/0.634-0.754). A simpler, more interpretable model, including timing of GDM diagnosis, diagnostic fasting glucose value, and the status and frequency of glycemic control at fasting during one-week post diagnosis, was developed using tenfold cross-validated logistic regression based on super learner-selected predictors. This model compared to the super learner had only a modest reduction in predictability [discovery/validation set C-statistic: 0.825 (0.820-0.830)/0.798 (95% CI: 0.783-0.813)].ConclusionsClinical data demonstrated reasonably high predictability for GDM treatment modality at the time of GDM diagnosis and high predictability at 1-week post GDM diagnosis. These population-based, clinically oriented models may support algorithm-based risk-stratification for treatment modality, inform timely treatment, and catalyze more effective management of GDM