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

BACKGROUND: Gestational 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. METHODS: Among 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. RESULTS: The 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)]. CONCLUSIONS: Clinical 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.

In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study.

BACKGROUND: Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear. OBJECTIVES: We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk. METHODS: In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM). RESULTS: Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values <0.05). Adding 7 of 17 metabolites to a conventional risk factor-based prediction model increased the C-statistic for GDM risk in the discovery set from 0.71 (95% CI: 0.64, 0.77) to 0.77 (95% CI: 0.71, 0.83) and in the validation set from 0.77 (95% CI: 0.69, 0.86) to 0.81 (95% CI: 0.74, 0.89), both with P-for-difference <0.05. CONCLUSIONS: Clusters of glycerolipids, glycerophospholipids, and sphingolipids may be implicated in the association between refined grain intake and GDM risk, as demonstrated by the significant associations of these metabolites with both refined grains and GDM risk and the incremental predictive value of these metabolites for GDM risk beyond the conventional risk factors. These findings provide evidence on the potential biological underpinnings linking refined grain intake to the risk of GDM and help identify novel disease-related dietary biomarkers to inform diet-related preventive strategies for GDM.

Glycemic Control Trajectories and Risk of Perinatal Complications Among Individuals With Gestational Diabetes.

Importance: Glycemic control is the cornerstone of gestational diabetes management. Glycemic control trajectories account for differences in longitudinal patterns throughout pregnancy; however, studies on glycemic control trajectories are scarce. Objective: To examine whether glycemic control trajectories from gestational diabetes diagnosis to delivery were associated with differential risk of perinatal complications. Design, Setting, and Participants: This population-based cohort study included individuals with gestational diabetes with longitudinal electronic health record data from preconception to delivery who received prenatal care at Kaiser Permanente Northern California (KPNC) and were enrolled in KPNC's telemedicine-based gestational diabetes care program between January 2007 and December 2017. Data analysis was conducted from September 2021 to January 2022. Exposures: Glycemic control trajectories were derived using latent class modeling based on the American Diabetes Association's recommended self-monitoring of blood glucose measurements. Optimal glycemic control was defined as at least 80% of all measurements meeting the targets at KPNC clinical settings. Main Outcomes and Measures: Multivariable Poisson regression models were used to estimate the associations of glycemic control trajectories with cesarean delivery, preterm birth, shoulder dystocia, large- and small-for-gestational-age, and neonatal intensive care unit admission and stay of 7 days or longer. Results: Among a total of 26 774 individuals (mean [SD] age, 32.9 [5.0] years; 11 196 Asian or Pacific Islander individuals [41.8%], 1083 Black individuals [4.0%], 7500 Hispanic individuals [28.0%], and 6049 White individuals [22.6%]), 4 glycemic control trajectories were identified: stably optimal (10 528 individuals [39.3%]), rapidly improving to optimal (9151 individuals [34.2%]), slowly improving to near-optimal (4161 individuals [15.5%]), and slowly improving to suboptimal (2934 individuals [11.0%]). In multivariable models with the rapidly improving to optimal trajectory group as the reference group, glycemic control trajectories were associated with perinatal complications with a gradient across stably optimal to slowly improving to suboptimal. For individuals in the stably optimal trajectory group, there were lower risks of cesarean delivery (adjusted relative risk [aRR], 0.93 [95% CI, 0.89-0.96]), shoulder dystocia (aRR, 0.75 [95% CI, 0.61-0.92]), large-for-gestational age (aRR, 0.74 [95% CI, 0.69-0.80]), and neonatal intensive care unit admission (aRR, 0.90 [95% CI, 0.83-0.97]), while for patients in the slowly improving to suboptimal glycemic control trajectory group, risks were higher for cesarean delivery (aRR, 1.18 [95% CI, 1.12-1.24]; (P for trend < .001), shoulder dystocia (aRR, 1.41 [95% CI, 1.12-1.78]; P for trend < .001), large-for-gestational-age (aRR, 1.42 [95% CI, 1.31-1.53]; P for trend < .001), and neonatal intensive care unit admission (aRR, 1.33 [95% CI, 1.20-1.47]; P for trend < .001). The risk of small-for-gestational-age was higher in patients in the stably optimal group (aRR, 1.10 [95% CI, 1.02-1.20]) and lower in the slowly improving to suboptimal group (aRR, 0.63 [95% CI, 0.53-0.75]). Conclusions and Relevance: These findings suggest that slowly improving to near-optimal and slowly improving to suboptimal glycemic control trajectories were associated with increased risk of perinatal complications. Future interventions should help individuals achieve glycemic control early after gestational diabetes diagnosis and throughout pregnancy to decrease the risk of perinatal complications.

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.

Perinatal Complications in Individuals in California With or Without SARS-CoV-2 Infection During Pregnancy.

Importance: Additional research from population-based studies is needed to inform the treatment of SARS-CoV-2 infection during pregnancy and to provide health risk information to pregnant individuals. Objective: To assess the risk of perinatal complications associated with SARS-CoV-2 infection and to describe factors associated with hospitalizations. Design, Setting, and Participants: This population-based cohort study included 43 886 pregnant individuals with longitudinal electronic health record data from preconception to delivery who delivered at Kaiser Permanente Northern California between March 1, 2020, and March 16, 2021. Individuals with diagnostic codes for COVID-19 that did not have a confirmatory polymerase chain reaction test for SARS-CoV-2 were excluded. Exposures: SARS-CoV-2 infection detected by polymerase chain reaction test (from 30 days before conception to 7 days after delivery) as a time varying exposure. Main Outcomes and Measures: Severe maternal morbidity including 21 conditions (eg, acute myocardial infarction, acute renal failure, acute respiratory distress syndrome, and sepsis) that occurred at any time during pregnancy or delivery; preterm birth; pregnancy hypertensive disorders; gestational diabetes; venous thromboembolism (VTE); stillbirth; cesarean delivery; and newborn birth weight and respiratory conditions. Standardized mean differences between individuals with and without SARS-CoV-2 were calculated. Cox proportional hazards regression was used to estimate the hazard ratios (HRs) and 95% CIs for the association between SARS-CoV-2 infection and perinatal complications and hospitalization and to consider the timing of SARS-CoV-2 infection relative to outcomes. Results: In this study of 43 886 pregnant individuals (mean [SD] age, 30.7 [5.2] years), individuals with a SARS-CoV-2 infection (1332 [3.0%]) were more likely to be younger, Hispanic, multiparous individuals with a higher neighborhood deprivation index and obesity or chronic hypertension. After adjusting for demographic characteristics, comorbidities, and smoking status, individuals with SARS-CoV-2 infection had higher risk for severe maternal morbidity (HR, 2.45; 95% CI, 1.91-3.13), preterm birth (<37 weeks; HR, 2.08; 95% CI, 1.75-2.47), and VTE (HR, 3.08; 95% CI, 1.09-8.74) than individuals without SARS-CoV-2. SARS-CoV-2 infection was also associated with increased risk of medically indicated preterm birth (HR, 2.56; 95% CI, 2.06-3.19); spontaneous preterm birth (HR, 1.61; 95% CI, 1.22-2.13); and early (HR, 2.52; 95% CI, 1.49-4.24), moderate (HR, 2.18; 95% CI, 1.25-3.80), and late (HR, 1.95; 95% CI, 1.61-2.37) preterm birth. Among individuals with SARS-CoV-2 infection, 76 (5.7%) had a hospitalization; pregestational diabetes (HR, 7.03; 95% CI, 2.22-22.2) and Asian or Pacific Islander (HR, 2.33; 95% CI, 1.06-5.11) and Black (HR, 3.14; 95% CI, 1.24-7.93) race and ethnicity were associated with an increased risk of hospitalization. Conclusions and Relevance: In this cohort study, SARS-CoV-2 infection was associated with increased risk of severe maternal morbidity, preterm birth, and VTE. The study findings inform clinicians and patients about the risk of perinatal complications associated with SARS-CoV-2 infection in pregnancy and support vaccination of pregnant individuals and those planning conception.

Associations of household food insufficiency with childhood depression and anxiety: a nationwide cross-sectional study in the USA.

OBJECTIVE: Household food insufficiency (HFIS) is a major public health threat to children. Children may be particularly vulnerable to HFIS as a psychological stressor due to their rapid growth and accelerated behavioural and cognitive states, whereas data focusing on HFIS and childhood mental disorders are as-yet sparse. We aimed to examine the associations of HFIS with depression and anxiety in US children. DESIGN: Cross-sectional study. SETTING: The 2016-2018 National Survey of Children's Health, a nationally-representative study. PARTICIPANTS: Primary caregivers of 102 341 children in the USA. PRIMARY AND SECONDARY OUTCOME MEASURES: Physician diagnosed depression and anxiety were assessed by questionnaires administered to primary caregivers of 102 341 children. Multivariable logistic regression models estimated adjusted OR (aOR) for current depression or anxiety associated with HFIS measured through a validated single-item instrument. RESULTS: Among children aged 3-17 years, 3.2% and 7.4% had parent-reported physician-diagnosed current depression and anxiety, respectively. Compared with children without HFIS, children with HFIS had approximately twofold higher weighted prevalence of anxiety or depression. After adjusting for covariates, children with versus without HFIS had a 1.53-fold (95% CI 1.15 to 2.03) and 1.48-fold (95% CI 1.20 to 1.82) increased odds of current depression and anxiety, respectively. Associations were slightly more pronounced among girls (aOR (95% CI): depression 1.69 (1.16 to 2.48); anxiety 1.78 (1.33 to 2.38)) than boys (1.42 (0.98 to 2.08); 1.32 (1.00 to 1.73); both P-for-interaction <0.01). The associations did not vary by children's age or race/ethnicity. CONCLUSIONS: HFIS was independently associated with depression and anxiety among US children. Girls presented slightly greater vulnerability to HFIS in terms of impaired mental health. Children identified as food-insufficient may warrant mental health assessment and possible intervention. Assessment of HFIS among children with impaired mental health is also warranted. Our findings also highlight the importance of promptly addressing HFIS with referral to appropriate resources and inform its potential to alleviate childhood mental health issues.

Household mold, pesticide use, and childhood asthma: A nationwide study in the U.S.

OBJECTIVES: To investigate the associations of household mold and pesticide use with risk of childhood asthma and examine the potential effect modification by child's sex at a national level in the U.S. METHODS: Nationally representative data were drawn from the cross-sectional 2017 and 2018 National Surveys of Children's Health. Household mold and pesticide exposures during the past 12 months and physician-diagnosed childhood asthma were assessed by standard questionnaires administered to primary caregivers. Multivariable logistic regression models were used to calculate adjusted odds ratios (aOR) for current asthma, adjusting for child, caregiver, and household covariates. We also examined potential effect modification by child's sex. Sampling weights accounted for the complex survey design. RESULTS: Among 41,423 U.S. children in 2017-2018, the weighted prevalence of current asthma was 10.8% in household mold-exposed children, compared with 7.2% in non-exposed children (P < 0.001). After adjusting for covariates including child's obesity, children with household mold exposure compared to those with no household mold exposure had a 1.41-fold (95% CI: 1.07, 1.87) higher odds of current asthma. Associations between household mold and current asthma were pronounced among boys (aOR 1.57; 95% CI: 1.03-2.38) but not girls (aOR 1.28; 0.90-1.83; P for interaction <0.001). No significant associations were observed between household pesticide use and current asthma, after adjusting for covariates. CONCLUSIONS: Our findings suggest that household mold is associated with current asthma among children, independent of other major risk factors including child's obesity status. Our findings may inform strategies targeting mitigation of household mold as an important indoor environment factor to address childhood asthma.

Racial, Ethnic, and Geographic Differences in Vaginal Birth After Cesarean Delivery in the US, 2011-2021.

This cross-sectional study examines racial, ethnic, and geographic differences in vaginal birth after cesarean delivery in the US, from 2011 to 2021.