Evolution of the Umbilical Cord Blood Proteome Across Gestational Development.

Neonatal health is dependent on early risk stratification, diagnosis, and timely management of potentially devastating conditions, particularly in the setting of prematurity. Many of these conditions are poorly predicted in real-time by clinical data and current diagnostics. Umbilical cord blood may represent a novel source of molecular signatures that provides a window into the state of the fetus at birth. In this study, we comprehensively characterized the cord blood proteome of infants born between 24 to 42 weeks using untargeted mass spectrometry and functional enrichment analysis. We determined that the cord blood proteome at birth varies significantly across gestational development. Proteins that function in structural development and growth (e.g., extracellular matrix organization, lipid particle remodeling, and blood vessel development) are more abundant earlier in gestation. In later gestations, proteins with increased abundance are in immune response and inflammatory pathways, including complements and calcium-binding proteins. Furthermore, these data contribute to the knowledge of the physiologic state of neonates across gestational age, which is crucial to understand as we strive to best support postnatal development in preterm infants, determine mechanisms of pathology causing adverse health outcomes, and develop cord blood biomarkers to help tailor our diagnosis and therapeutics for critical neonatal conditions.

Elevated Blood Pressure in Pregnancy and Long-Term Cardiometabolic Health Outcomes.

OBJECTIVE: To examine the association between elevated blood pressure (BP) in the early third trimester and cardiometabolic health 10-14 years after delivery. METHODS: This is a secondary analysis from the prospective HAPO FUS (Hyperglycemia and Adverse Pregnancy Outcome Follow-Up Study). Blood pressure in the early third trimester was categorized per American College of Cardiology/American Heart Association thresholds for: normal BP below 120/80 mm Hg (reference), elevated BP 120-129/below 80 mm Hg, stage 1 hypertension 130-139/80-89 mm Hg, and stage 2 hypertension 140/90 mm Hg or higher. Cardiometabolic outcomes assessed 10-14 years after the index pregnancy were type 2 diabetes mellitus and measures of dyslipidemia, including low-density lipoprotein (LDL) cholesterol 130 mg/dL or higher, total cholesterol 200 mg/dL or higher, high-density lipoprotein (HDL) cholesterol 40 mg/dL or lower, and triglycerides 200 mg/dL or higher. Adjusted analysis was performed with the following covariates: study field center, follow-up duration, age, body mass index (BMI), height, family history of hypertension and diabetes, smoking and alcohol use, parity, and oral glucose tolerance test glucose z score. RESULTS: Among 4,692 pregnant individuals at a median gestational age of 27.9 weeks (interquartile range 26.6-28.9 weeks), 8.5% (n=399) had elevated BP, 14.9% (n=701) had stage 1 hypertension, and 6.4% (n=302) had stage 2 hypertension. At a median follow-up of 11.6 years, among individuals with elevated BP, there was a higher frequency of diabetes (elevated BP: adjusted relative risk [aRR] 1.88, 95% CI, 1.06-3.35; stage 1 hypertension: aRR 2.58, 95% CI, 1.62-4.10; stage 2 hypertension: aRR 2.83, 95% CI, 1.65-4.95) compared with those with normal BP. Among individuals with elevated BP, there was a higher frequency of elevated LDL cholesterol (elevated BP: aRR 1.27, 95% CI, 1.03-1.57; stage 1 hypertension: aRR 1.22, 95% CI, 1.02-1.45, and stage 2 hypertension: aRR 1.38, 95% CI, 1.10-1.74), elevated total cholesterol (elevated BP: aRR 1.27, 95% CI, 1.07-1.52; stage 1 hypertension: aRR 1.16, 95% CI, 1.00-1.35; stage 2 hypertension: aRR 1.41 95% CI, 1.16-1.71), and elevated triglycerides (elevated BP: aRR 2.24, 95% CI, 1.42-3.53; stage 1 hypertension: aRR 2.15, 95% CI, 1.46-3.17; stage 2 hypertension: aRR 3.24, 95% CI, 2.05-5.11) but not of low HDL cholesterol. CONCLUSION: The frequency of adverse cardiometabolic outcomes at 10-14 years after delivery was progressively higher among pregnant individuals with BP greater than 120/80 in the early third trimester.

Genome-Wide Polygenic Risk Score Predicts Incident Type 2 Diabetes in Women With History of Gestational Diabetes.

OBJECTIVE: Women with a history of gestational diabetes mellitus (GDM) are at increased risk of developing type 2 diabetes (T2D). It remains unclear whether genetic information improves prediction of incident T2D in these women. RESEARCH DESIGN AND METHODS: Using five independent cohorts representing four different ancestries (n = 1,895), we investigated whether a genome-wide T2D polygenic risk score (PRS) is associated with increased risk of incident T2D. We also calculated the area under the receiver operating characteristics curve (AUROC) and continuous net reclassification improvement (NRI) following the incorporation of T2D PRS into clinical risk models to assess the diagnostic utility. RESULTS: Among 1,895 women with previous history of GDM, 363 (19.2%) developed T2D in a range of 2 to 30 years. T2D PRS was higher in those who developed T2D (-0.08 vs. 0.31, P = 2.3 × 10-11) and was associated with an increased risk of incident T2D (odds ratio 1.52 per 1-SD increase, 95% CI 1.05-2.21, P = 0.03). In a model that includes age, family history of diabetes, systolic blood pressure, and BMI, the incorporation of PRS led to an increase in AUROC for T2D from 0.71 to 0.74 and an intermediate improvement of NRI (0.32, 95% CI 0.15-0.49, P = 3.0 × 10-4). Although there was variation, a similar trend was observed across study cohorts. CONCLUSIONS: In cohorts of GDM women with diverse ancestry, T2D PRS was significantly associated with future development of T2D. A significant but small improvement was observed in AUROC when T2D PRS was integrated into clinical risk models to predict incident T2D.

Statistical analysis plan for the NU IMPACT stepped-wedge cluster randomized trial.

BACKGROUND: As part of the IMPACT Consortium of three effectiveness-implementation trials, the NU IMPACT trial was designed to evaluate implementation and effectiveness outcomes for an electronic health record (EHR)-embedded symptom monitoring and management program for outpatient cancer care. NU IMPACT uses a unique stepped-wedge cluster randomized design, involving six clusters of 26 clinics, for evaluation of implementation outcomes with an embedded patient-level randomized trial to evaluate effectiveness outcomes. Collaborative, consortium-wide efforts to ensure use of the most robust and recent analytic methodologies for stepped-wedge trials motivated updates to the statistical analysis plan for implementation outcomes in the NU IMPACT trial. METHODS: In the updated statistical analysis plan for NU IMPACT, the primary implementation outcome patient adoption, as measured by clinic-level monthly proportions of patient engagement with the EHR-based cancer symptom monitoring system, will be analyzed using generalized least squares linear regression with auto-regressive errors and adjustment for cluster and time effects (underlying secular trends). A similar strategy will be used for secondary patient and provider implementation outcomes. DISCUSSION: The analytic updates described here resulted from highly iterative, collaborative efforts among statisticians, implementation scientists, and trial leads in the IMPACT Consortium. This updated statistical analysis plan will serve as the a priori specified approach for analyzing implementation outcomes for the NU IMPACT trial.

Impact of hypertensive disorders of pregnancy and gestational diabetes mellitus on offspring cardiovascular health in early adolescence.

BACKGROUND: Adverse pregnancy outcomes, including hypertensive disorders of pregnancy and gestational diabetes mellitus, influence maternal cardiovascular health long after pregnancy, but their relationship to offspring cardiovascular health following in-utero exposure remains uncertain. OBJECTIVE: To examine associations of hypertensive disorders of pregnancy or gestational diabetes mellitus with offspring cardiovascular health in early adolescence. STUDY DESIGN: This analysis used data from the prospective Hyperglycemia and Adverse Pregnancy Outcome Study from 2000 to 2006 and the Hyperglycemia and Adverse Pregnancy Outcome Follow-Up Study from 2013 to 2016. This analysis included 3317 mother-child dyads from 10 field centers, comprising 70.8% of Hyperglycemia and Adverse Pregnancy Outcome Follow-Up Study participants. Those with pregestational diabetes and chronic hypertension were excluded. The exposures included having any hypertensive disorders of pregnancy or gestational diabetes mellitus vs not having hypertensive disorders of pregnancy or gestational diabetes mellitus, respectively (reference). The outcome was offspring cardiovascular health when aged 10-14 years, on the basis of 4 metrics: body mass index, blood pressure, total cholesterol level, and glucose level. Each metric was categorized as ideal, intermediate, or poor using a framework provided by the American Heart Association. The primary outcome was defined as having at least 1 cardiovascular health metric that was nonideal vs all ideal (reference), and the second outcome was the number of nonideal cardiovascular health metrics (ie, at least 1 intermediate metric, 1 poor metric, or at least 2 poor metrics vs all ideal [reference]). Modified poisson regression with robust error variance was used and adjusted for covariates at pregnancy enrollment, including field center, parity, age, gestational age, alcohol or tobacco use, child's assigned sex at birth, and child's age at follow-up. RESULTS: Among 3317 maternal-child dyads, the median (interquartile) ages were 30.4 (25.6-33.9) years for pregnant individuals and 11.6 (10.9-12.3) years for children. During pregnancy, 10.4% of individuals developed hypertensive disorders of pregnancy, and 14.6% developed gestational diabetes mellitus. At follow-up, 55.5% of offspring had at least 1 nonideal cardiovascular health metric. In adjusted models, having hypertensive disorders of pregnancy (adjusted risk ratio, 1.14 [95% confidence interval, 1.04-1.25]) or having gestational diabetes mellitus (adjusted risk ratio, 1.10 [95% confidence interval, 1.02-1.19]) was associated with a greater risk that offspring developed less-than-ideal cardiovascular health when aged 10-14 years. The above associations strengthened in magnitude as the severity of adverse cardiovascular health metrics increased (ie, with the outcome measured as ≥1 intermediate, 1 poor, and ≥2 poor adverse metrics), albeit the only statistically significant association was with the "1-poor-metric" exposure. CONCLUSION: In this multinational prospective cohort, pregnant individuals who experienced either hypertensive disorders of pregnancy or gestational diabetes mellitus were at significantly increased risk of having offspring with worse cardiovascular health in early adolescence. Reducing adverse pregnancy outcomes and increasing surveillance with targeted interventions after an adverse pregnancy outcome should be studied as potential avenues to enhance long-term cardiovascular health in the offspring exposed in utero.

Metabolomic and genetic architecture of gestational diabetes subtypes.

AIMS/HYPOTHESIS: Physiological gestational diabetes mellitus (GDM) subtypes that may confer different risks for adverse pregnancy outcomes have been defined. The aim of this study was to characterise the metabolome and genetic architecture of GDM subtypes to address the hypothesis that they differ between GDM subtypes. METHODS: This was a cross-sectional study of participants in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study who underwent an OGTT at approximately 28 weeks' gestation. GDM was defined retrospectively using International Association of Diabetes and Pregnancy Study Groups/WHO criteria, and classified as insulin-deficient GDM (insulin secretion <25th percentile with preserved insulin sensitivity) or insulin-resistant GDM (insulin sensitivity <25th percentile with preserved insulin secretion). Metabolomic analyses were performed on fasting and 1 h serum samples in 3463 individuals (576 with GDM). Genome-wide genotype data were obtained for 8067 individuals (1323 with GDM). RESULTS: Regression analyses demonstrated striking differences between the metabolomes for insulin-deficient or insulin-resistant GDM compared to those with normal glucose tolerance. After adjustment for covariates, 33 fasting metabolites, including 22 medium- and long-chain acylcarnitines, were uniquely associated with insulin-deficient GDM; 23 metabolites, including the branched-chain amino acids and their metabolites, were uniquely associated with insulin-resistant GDM; two metabolites (glycerol and 2-hydroxybutyrate) were associated with the same direction of association with both subtypes. Subtype differences were also observed 1 h after a glucose load. In genome-wide association studies, variants within MTNR1B (rs10830963, p=3.43×10-18, OR 1.55) and GCKR (rs1260326, p=5.17×10-13, OR 1.43) were associated with GDM. Variants in GCKR (rs1260326, p=1.36×10-13, OR 1.60) and MTNR1B (rs10830963, p=1.22×10-9, OR 1.49) demonstrated genome-wide significant association with insulin-resistant GDM; there were no significant associations with insulin-deficient GDM. The lead SNP in GCKR, rs1260326, was associated with the levels of eight of the 25 fasting metabolites that were associated with insulin-resistant GDM and ten of 41 1 h metabolites that were associated with insulin-resistant GDM. CONCLUSIONS/INTERPRETATION: This study demonstrates that physiological GDM subtypes differ in their metabolome and genetic architecture. These findings require replication in additional cohorts, but suggest that these differences may contribute to subtype-related adverse pregnancy outcomes.

Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes.

BACKGROUND: Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS: We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS: All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS: Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING: Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.

Leptin and adiposity measures from birth to later childhood: Findings from the Hyperglycemia and Adverse Pregnancy Outcomes Follow-Up Study.

BACKGROUND: Cord blood (CB) leptin is positively associated with adiposity at birth, but the association with child adiposity is unclear. OBJECTIVES: We hypothesized that CB leptin is positively associated with adiposity in peripubertal children and with childhood leptin. METHODS: Leptin was measured in 986 CB and 931 childhood stored samples from a prospective birth cohort. Adiposity measures were collected at birth and mean age 11.5 years. Linear and logistic regression analyses were used to evaluate associations between log-transformed CB leptin and neonatal and childhood adiposity measures as continuous and categorical variables, respectively. RESULTS: CB leptin was positively associated with neonatal and childhood adiposity. Childhood associations were attenuated when adjusted for maternal body mass index (BMI) and glucose, but remained statistically significant for childhood body fat percentage (ß = 1.15%, confidence interval [CI] = 0.46-1.84), body fat mass (ß = 0.69 kg, 95% CI = 0.16-1.23), sum of skin-folds (ß = 1.77 mm, 95% CI = 0.31-3.24), log-transformed child serum leptin (ß = 0.13, 95% CI = 0.06-0.20), overweight/obesity (OR = 1.21, 95% CI = 1.03-1.42), obesity (OR = 1.31, 95% CI = 1.04-1.66) and body fat percentage >85th percentile (OR = 1.38, 95% CI = 1.12-1.73). Positive associations between newborn adiposity measures and CB leptin confirmed previous reports. CONCLUSION: CB leptin is positively associated with neonatal and childhood adiposity and child leptin levels, independent of maternal BMI and maternal hyperglycemia. CB leptin may be a biomarker of future adiposity risk.

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis.

BACKGROUND: Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM. METHODS: Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA). RESULTS: A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers. CONCLUSIONS: Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Gestational Diabetes (GDM) is high blood sugar that develops during pregnancy and may cause complications. GDM diagnosis is centered on blood sugar levels. Despite everyone receiving standard treatment, the clinical outcomes may vary from one individual to another. This indicates a need to identify factors that may help GDM diagnosis and result in improved classification of those at greatest risk for complications. Here, we systematically analyzed all published evidence for potential markers that could identify those with GDM who have greater risk of complications. We find that high maternal weight is a risk factor for offspring born larger for their gestational age. Other promising markers were identified, but further analysis is needed before they can be applied in the clinic.

Use of fasting plasma glucose to determine the approach for diagnosing gestational diabetes mellitus.

AIMS: Estimate the impact of OGTTs only on women with a screening FPG of 4.5-5.0 mmol/L using data from HAPO. METHODS: HAPO participants had 75-g OGTTs (24-32 weeks' gestation). At follow-up, children had adiposity assessed (overweight/obesity, obesity) and mothers and children had OGTTs. GDM was defined retrospectively using IADPSG criteria. Odds for neonatal (birthweight, percent neonatal fat, sum of skinfolds, cord C-peptide > 90th percentiles) and follow-up outcomes were assessed in those with HAPO FPG ≤ 4.4 or > 4.4 mmol/L and GDM or no GDM focusing on women with FPG > 4.4 and no GDM (Group 3) vs women with GDM and FPG ≤ 4.4 (Group 2). RESULTS: This strategy would miss a diagnosis of GDM in 14.7%. Odds for neonatal outcomes in Groups 2 and 3 were not different (ORs: 1.14 to 1.29). Odds at follow-up for type 2 diabetes and disorders of glucose metabolism in mothers were higher in Group 2 (ORs: 3.51, 2.57). Odds for childhood impaired glucose tolerance or adiposity outcomes were not different for Groups 2 and 3. CONCLUSIONS: HAPO mothers whose GDM diagnosis would be missed were not at greater risk for adverse neonatal and childhood outcomes than mothers with FPG of 4.5-5.0 without GDM.

Racial and ethnic differences in the association between pregnancy dysglycemia and cardiometabolic risk factors 10-14 years' postpartum in the HAPO follow-up study.

Associations between pregnancy dysglycemia and subsequent maternal cardiometabolic factors 10-14 years postpartum were largely similar across self-identified racial and ethnic groups among birthing people in the U.S. enrolled in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Follow-up Study.

Haploinsufficiency of NFKBIA reshapes the epigenome antipodal to the IDH mutation and imparts disease fate in diffuse gliomas.

Genetic alterations help predict the clinical behavior of diffuse gliomas, but some variability remains uncorrelated. Here, we demonstrate that haploinsufficient deletions of chromatin-bound tumor suppressor NFKB inhibitor alpha (NFKBIA) display distinct patterns of occurrence in relation to other genetic markers and are disproportionately present at recurrence. NFKBIA haploinsufficiency is associated with unfavorable patient outcomes, independent of genetic and clinicopathologic predictors. NFKBIA deletions reshape the DNA and histone methylome antipodal to the IDH mutation and induce a transcriptome landscape partly reminiscent of H3K27M mutant pediatric gliomas. In IDH mutant gliomas, NFKBIA deletions are common in tumors with a clinical course similar to that of IDH wild-type tumors. An externally validated nomogram model for estimating individual patient survival in IDH mutant gliomas confirms that NFKBIA deletions predict comparatively brief survival. Thus, NFKBIA haploinsufficiency aligns with distinct epigenome changes, portends a poor prognosis, and should be incorporated into models predicting the disease fate of diffuse gliomas.

Maternal and Cord Blood Serum Metabolite Associations with Childhood Adiposity and Body Composition Outcomes.

Maternal metabolites influence the size of newborns independently of maternal body mass index (BMI) and glycemia, highlighting the importance of maternal metabolism on offspring outcomes. This study examined associations of maternal metabolites during pregnancy with childhood adiposity, and cord blood metabolites with childhood adiposity using phenotype and metabolomic data from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and the HAPO Follow-Up Study. The maternal metabolites analyses included 2324 mother-offspring pairs, while the cord blood metabolites analyses included 937 offspring. Multiple logistic and linear regression were used to examine associations between primary predictors, maternal or cord blood metabolites, and childhood adiposity outcomes. Multiple maternal fasting and 1 hr metabolites were significantly associated with childhood adiposity outcomes in Model 1 but were no longer significant after adjusting for maternal BMI and/or maternal glycemia. In the fully adjusted model, fasting lactose levels were negatively associated with child BMI z-scores and waist circumference, while fasting urea levels were positively associated with waist circumference. One-hour methionine was positively associated with fat-free mass. There were no significant associations between cord blood metabolites and childhood adiposity outcomes. Few metabolites were associated with childhood adiposity outcomes after adjusting for maternal BMI and glucose, suggesting that maternal BMI accounts for the association between maternal metabolites and childhood adiposity.

Association of Maternal Metabolites and Metabolite Networks with Newborn Outcomes in a Multi-Ancestry Cohort.

The in utero environment is important for newborn size at birth, which is associated with childhood adiposity. We examined associations between maternal metabolite levels and newborn birthweight, sum of skinfolds (SSF), and cord C-peptide in a multinational and multi-ancestry cohort of 2337 mother-newborn dyads. Targeted and untargeted metabolomic assays were performed on fasting and 1 h maternal serum samples collected during an oral glucose tolerance test performed at 24-32 week gestation in women participating in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Anthropometric measurements were obtained on newborns at birth. Following adjustment for maternal BMI and glucose, per-metabolite analyses demonstrated significant associations between maternal metabolite levels and birthweight, SSF, and cord C-peptide. In the fasting state, triglycerides were positively associated and several long-chain acylcarnitines were inversely associated with birthweight and SSF. At 1 h, additional metabolites including branched-chain amino acids, proline, and alanine were positively associated with newborn outcomes. Network analyses demonstrated distinct clusters of inter-connected metabolites significantly associated with newborn phenotypes. In conclusion, numerous maternal metabolites during pregnancy are significantly associated with newborn birthweight, SSF, and cord C-peptide independent of maternal BMI and glucose, suggesting that metabolites in addition to glucose contribute to newborn size at birth and adiposity.

SpiderLearner: An ensemble approach to Gaussian graphical model estimation.

Gaussian graphical models (GGMs) are a popular form of network model in which nodes represent features in multivariate normal data and edges reflect conditional dependencies between these features. GGM estimation is an active area of research. Currently available tools for GGM estimation require investigators to make several choices regarding algorithms, scoring criteria, and tuning parameters. An estimated GGM may be highly sensitive to these choices, and the accuracy of each method can vary based on structural characteristics of the network such as topology, degree distribution, and density. Because these characteristics are a priori unknown, it is not straightforward to establish universal guidelines for choosing a GGM estimation method. We address this problem by introducing SpiderLearner, an ensemble method that constructs a consensus network from multiple estimated GGMs. Given a set of candidate methods, SpiderLearner estimates the optimal convex combination of results from each method using a likelihood-based loss function. K $$ K $$ -fold cross-validation is applied in this process, reducing the risk of overfitting. In simulations, SpiderLearner performs better than or comparably to the best candidate methods according to a variety of metrics, including relative Frobenius norm and out-of-sample likelihood. We apply SpiderLearner to publicly available ovarian cancer gene expression data including 2013 participants from 13 diverse studies, demonstrating our tool's potential to identify biomarkers of complex disease. SpiderLearner is implemented as flexible, extensible, open-source code in the R package ensembleGGM at https://github.com/katehoffshutta/ensembleGGM.

Implementation and evaluation of an expanded electronic health record-integrated bilingual electronic symptom management program across a multi-site Comprehensive Cancer Center: The NU IMPACT protocol.

BACKGROUND: People with cancer experience symptoms that adversely affect quality of life. Despite existing interventions and clinical guidelines, timely symptom management remains uneven in oncology care. We describe a study to implement and evaluate an electronic health record (EHR)-integrated symptom monitoring and management program in adult outpatient cancer care. METHODS: Our cancer patient-reported outcomes (cPRO) symptom monitoring and management program is a customized EHR-integrated installation. We will implement cPRO across all Northwestern Memorial HealthCare (NMHC) hematology/oncology clinics. We will conduct a cluster randomized modified stepped-wedge trial to evaluate patient and clinician engagement with cPRO. Further, we will embed a patient-level randomized clinical trial to evaluate the impact of an additional enhanced care (EC; cPRO plus web-based symptom self-management intervention) relative to usual care (UC; cPRO alone). The project uses a Type 2 hybrid effectiveness-implementation approach. The intervention will be implemented across seven regional clusters within the healthcare system comprising 32 clinic sites. A 6-month prospective pre-implementation enrollment period will be followed by a post-implementation enrollment period, during which newly enrolled, consenting patients will be randomly assigned (1:1) to EC or UC. We will follow patients for 12 months post-enrollment. Patients randomized to EC will receive evidence-based symptom-management content on cancer-related concerns and approaches to enhance quality of life, using a web-based tool ("MyNM Care Corner"). This design allows for within- and between-site evaluation of implementation plus a group-based comparison to demonstrate effectiveness on patient-level outcomes. DISCUSSION: The project has potential to guide implementation of future healthcare system-level cancer symptom management programs. http://ClinicalTrials.gov # NCT03988543.

Effectiveness and implementation of an electronic health record-integrated digital health intervention for managing depressive symptoms in ambulatory oncology: The My Well-Being Guide study rationale and protocol.

BACKGROUND: Rates of clinically elevated depressive symptoms among ambulatory oncology patients are higher than in the general population and are associated with poorer health-related quality of life. Furthermore, a reduction in depressive symptoms may be associated with improved cancer survival. Several interventions have demonstrated efficacy in reducing oncologic depressive symptoms, including cognitive-behavioral stress management (CBSM). However, more work is needed to understand how to best implement CBSM into practice, such as through stepped-care approaches and digital health interventions linked to electronic health records (EHR). This manuscript presents the protocol of the My Well-Being Guide study, a pragmatic type 1 effectiveness-implementation hybrid study. This trial will test the effectiveness of My Well-Being Guide, a seven-week structured, CBSM-based digital health intervention designed to reduce depressive symptoms. This trial will also evaluate My Well-Being Guide's implementation across two health systems. METHODS: The final sample (N = 4561) will be oncology patients at Northwestern Medicine or University of Miami Health System who are ≥18 years of age; have a cancer diagnosis; elevated depressive symptoms on the Patient-Reported Outcomes Measurement Information System Depression; and primary language is English or Spanish. Data collection will occur at baseline, and 2-, 6-, and 12-months post baseline. Outcome domains include depressive symptoms and implementation evaluation. DISCUSSION: This study may provide valuable data on the effectiveness of our depressive symptom management digital health intervention linked to the EHR and the scalability of digital health interventions in general.