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.

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.

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.

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.

Determining venous thromboembolism risk in patients with adult-type diffuse glioma.

Venous thromboembolism (VTE) is a life-threating condition that is common in patients with adult-type diffuse gliomas, yet thromboprophylaxis is controversial because of possible intracerebral hemorrhage. Effective VTE prediction models exist for other cancers, but not glioma. Our objective was to develop a VTE prediction tool to improve glioma patient care, incorporating clinical, blood-based, histologic, and molecular markers. We analyzed preoperative arterial blood, tumor tissue, and clinical-pathologic data (including next-generation sequencing data) from 258 patients with newly diagnosed World Health Organization (WHO) grade 2 to 4 adult-type diffuse gliomas. Forty-six (17.8%) experienced VTE. Tumor expression of tissue factor (TF) and podoplanin (PDPN) each positively correlated with VTE, although only circulating TF and D-dimers, not circulating PDPN, correlated with VTE risk. Gliomas with mutations in isocitrate dehydrogenase 1 (IDH1) or IDH2 (IDHmut) caused fewer VTEs; multivariable analysis suggested that this is due to IDHmut suppression of TF, not PDPN. In a predictive time-to-event model, the following predicted increased VTE risk in newly diagnosed patients with glioma: (1) history of VTE; (2) hypertension; (3) asthma; (4) white blood cell count; (5) WHO tumor grade; (6) patient age; and (7) body mass index. Conversely, IDHmut, hypothyroidism, and MGMT promoter methylation predicted reduced VTE risk. These 10 variables were used to create a web-based VTE prediction tool that was validated in 2 separate cohorts of patients with adult-type diffuse glioma from other institutions. This study extends our understanding of the VTE landscape in these tumors and provides evidence-based guidance for clinicians to mitigate VTE risk in patients with glioma.

Urinary metal profiles in mother-offspring pairs and their association with early dysglycemia in the International Hyperglycemia and Adverse Pregnancy Outcome Follow Up Study (HAPO-FUS).

BACKGROUND: Variations in dietary intake and environmental exposure patterns of essential and non-essential trace metals influence many aspects of human health throughout the life span. OBJECTIVE: To examine the relationship between urine profiles of essential and non-essential metals in mother-offspring pairs and their association with early dysglycemia. METHODS: Herein, we report findings from an ancillary study to the international Hyperglycemia and Adverse Pregnancy Outcome Follow-Up Study (HAPO-FUS) that examined urinary essential and non-essential metal profiles from mothers and offspring ages 10-14 years (1012 mothers, 1013 offspring, 968 matched pairs) from 10 international sites. RESULTS: Our analysis demonstrated a diverse exposure pattern across participating sites. In multiple regression modelling, a positive association between markers of early dysglycemia and urinary zinc was found in both mothers and offspring after adjustment for common risk factors for diabetes. The analysis showed weaker, positive, and negative associations of the 2-h glucose value with urinary selenium and arsenic respectively. A positive association between 2-h glucose values and cadmium was found only in mothers in the fully adjusted model when participants with established diabetes were excluded. There was a high degree of concordance between mother and offspring urinary metal profiles. Mother-to-offspring urinary metal ratios were unique for each metal, providing insights into changes in their homeostasis across the lifespan. SIGNIFICANCE: Urinary levels of essential and non-essential metals are closely correlated between mothers and their offspring in an international cohort. Urinary levels of zinc, selenium, arsenic, and cadmium showed varying degrees of association with early dysglycemia in a comparatively healthy cohort with a low rate of preexisting diabetes. IMPACT STATEMENT: Our data provides novel evidence for a strong correlation between mother and offspring urinary metal patterns with a unique mother-to-offspring ratio for each metal. The study also provides new evidence for a strong positive association between early dysglycemia and urinary zinc, both in mothers and offspring. Weaker positive associations with urinary selenium and cadmium and negative associations with arsenic were also found. The low rate of preexisting diabetes in this population provides the unique advantage of minimizing the confounding effect of preexisting, diabetes related renal changes that would alter the relationship between dysglycemia and renal metal excretion.

Ventricular Assist Device Self-care Education at Tertiary Care Medical Centers.

Ventricular assist device (VAD) implantation requires patients and caregivers to attain self-care knowledge and skills before discharge from implant hospitalization. Inability to perform these skills can have devastating outcomes (i.e., death from pump malfunction, driveline site infections, and stroke). No standard-of-care guiding VAD self-care education exists. We sought to describe how select tertiary care VAD implant centers across the United States currently educate VAD patients and their caregivers. Using a multiple case studies design with a purposive sampling strategy, we conducted semistructured interviews of VAD coordinators responsible for VAD education at 18 centers. From audio recording of interviews, we used rapid qualitative analysis to organize and analyze the data. All centers spent significant time and effort educating patients and caregivers on VAD self-care. Although centers teach similar content, the rigor of assessments and follow-up education differed vastly. Only 3/18 (17%) centers performed competency-based assessments with a skills checklist and minimum passing standard assessing readiness to perform VAD self-care independently. Twelve of 18 (67%) centers provided formal follow-up education to address skills decay, yet wide variation existed in timing and content of education. Due to the diversity among centers regarding VAD self-care education, more prescriptive practice guidelines are needed.

Gaussian graphical models with applications to omics analyses.

Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical foundations of GGMs are introduced with the goal of enabling the researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed for high-dimensional applications such as genomics, proteomics, or metabolomics. The application of these methods is illustrated using a publicly available dataset of gene expression profiles from 578 participants with ovarian cancer in The Cancer Genome Atlas. Stand-alone code for the demonstration is available as an RMarkdown file at https://github.com/katehoffshutta/ggmTutorial.

Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies.

The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal-offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant-metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal-offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal-offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.

Associations of glycemia and lipid levels in pregnancy with dyslipidemia 10-14 years later: The HAPO follow-up study.

AIMS: To examine associations of pregnancy glycemia with future dyslipidemia. METHODS: We analyzed data from Hyperglycemia and Adverse Pregnancy Outcome Follow-Up Study participants. We examined associations of gestational diabetes (GDM), sum of fasting, 1-hour, and 2-hour glucose z-scores after 75-g load, insulin sensitivity, and lipid levels at 24-32 weeks' gestation with dyslipidemia 10-14 years postpartum. RESULTS: Among 4,693 women, 14.3% had GDM. At follow-up, mean (SD) age was 41.7 (5.7) years, 32.3% had total cholesterol (TC) ≥ 5.17, 27.2% had HDL cholesterol < 1.29, 22.4% had LDL cholesterol (LDL-C) ≥ 3.36, 10.9% had triglycerides ≥ 1.69 mmol/L, and 2.9% had type 2 diabetes. After covariate adjustment, pregnancy glycemic measures were associated with all follow-up dyslipidemias. After additional adjustment for pregnancy lipids, GDM remained associated with TC ≥ 5.17 mmol/L (odds ratio [95% CI], 1.63 [1.22-2.18]) and LDL-C ≥ 3.36 mmol/L (1.63 [1.20-2.22]), even in the absence of type 2 diabetes development (1.55 [1.15-2.10] and 1.56 [1.13-2.16], respectively). Continuous glycemic measures in pregnancy were significantly associated with all follow-up dyslipidemias, independent of pregnancy lipids and type 2 diabetes. CONCLUSIONS: Pregnancy glycemia was associated with dyslipidemia 10-14 years later, independent of pregnancy lipid levels and in the absence of type 2 diabetes development. Lipid screening after GDM deserves special consideration.

Path-level interpretation of Gaussian graphical models using the pair-path subscore.

BACKGROUND : Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS: We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps . CONCLUSIONS: The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.

Effect of Ventricular Assist Device Self-care Simulation-Based Mastery Learning on Driveline Exit Site Infections: A Pilot Study.

BACKGROUND: Ventricular assist device simulation-based mastery learning (SBML) results in better patient and caregiver self-care skills compared with usual training. OBJECTIVE: The aim of this study was to evaluate the effect of SBML on driveline exit site infections. METHODS: We compared the probability of remaining infection free at 3 and 12 months between patients randomized to SBML or usual training. RESULTS: The SBML-training group had no infections at 3 months and 2 infections at 12 months, yielding a Kaplan-Meier estimate of the probability of remaining infection free of 0.857 (95% confidence interval [CI], 0.692-1.00) at 12 months. The usual-training group had 6 infections at 3 months with no additional infections by 12 months. Kaplan-Meier estimates of remaining infection free at 3 and 12 months were 0.878 (95% CI, 0.758-1.00) and 0.748 (95% CI, 0.591-0.946), respectively. Time-to-infection distributions for SBML versus usual training showed a difference in 12-month infection rates of 0.109 (P = .07). CONCLUSIONS: Ventricular assist device self-care SBML resulted in fewer 12-month infections.

Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight.

BACKGROUND: Higher birthweight is associated with higher adult body mass index (BMI). Alleles that predispose to greater adult adiposity might act in fetal life to increase fetal growth and birthweight. Whether there are fetal effects of recently identified adult metabolically favorable adiposity alleles on birthweight is unknown. AIM: We aimed to test the effect on birthweight of fetal genetic predisposition to higher metabolically favorable adult adiposity and compare that with the effect of fetal genetic predisposition to higher adult BMI. METHODS: We used published genome wide association study data (n = upto 406 063) to estimate fetal effects on birthweight (adjusting for maternal genotype) of alleles known to raise metabolically favorable adult adiposity or BMI. We combined summary data across single nucleotide polymorphisms (SNPs) with random effects meta-analyses. We performed weighted linear regression of SNP-birthweight effects against SNP-adult adiposity effects to test for a dose-dependent association. RESULTS: Fetal genetic predisposition to higher metabolically favorable adult adiposity and higher adult BMI were both associated with higher birthweight (3 g per effect allele (95% CI: 1-5) averaged over 14 SNPs; P = 0.002; 0.5 g per effect allele (95% CI: 0-1) averaged over 76 SNPs; P = 0.042, respectively). SNPs with greater effects on metabolically favorable adiposity tended to have greater effects on birthweight (R2 = 0.2912, P = 0.027). There was no dose-dependent association for BMI (R2 = -0.0019, P = 0.602). CONCLUSIONS: Fetal genetic predisposition to both higher adult metabolically favorable adiposity and BMI is associated with birthweight. Fetal effects of metabolically favorable adiposity-raising alleles on birthweight are modestly proportional to their effects on future adiposity, but those of BMI-raising alleles are not.

Benign tumors in TSC are amenable to treatment by GD3 CAR T cells in mice.

Mutations underlying disease in tuberous sclerosis complex (TSC) give rise to tumors with biallelic mutations in TSC1 or TSC2 and hyperactive mammalian target of rapamycin complex 1 (mTORC1). Benign tumors might exhibit de novo expression of immunogens, targetable by immunotherapy. As tumors may rely on ganglioside D3 (GD3) expression for mTORC1 activation and growth, we compared GD3 expression in tissues from patients with TSC and controls. GD3 was overexpressed in affected tissues from patients with TSC and also in aging Tsc2+/- mice. As GD3 overexpression was not accompanied by marked natural immune responses to the target molecule, we performed preclinical studies with GD3 chimeric antigen receptor (CAR) T cells. Polyfunctional CAR T cells were cytotoxic toward GD3-overexpressing targets. In mice challenged with Tsc2-/- tumor cells, CAR T cells substantially and durably reduced the tumor burden, correlating with increased T cell infiltration. We also treated aged Tsc2+/- heterozygous (>60 weeks) mice that carry spontaneous Tsc2-/- tumors with GD3 CAR or untransduced T cells and evaluated them at endpoint. Following CAR T cell treatment, the majority of mice were tumor free while all control animals carried tumors. The outcomes demonstrate a strong treatment effect and suggest that targeting GD3 can be successful in TSC.