The Role of Adverse Pregnancy Outcomes in Conventional Cardiovascular Risk Prediction.

OBJECTIVE: Approximately one-third of women in the U.S. experience an adverse pregnancy outcome (APO), which are recognized as sex-specific cardiovascular disease (CVD) risk factors. We examine if APOs confer additional CVD risk beyond that of traditional CVD risk factors. METHODS: Women, age 40-79, with a pregnancy history and no pre-existing CVD were identified in the electronic health record of one health system (n = 2306). APOs included any APO, hypertensive disease of pregnancy (HDP), and gestational diabetes (GDM). Hazard ratios of time to CVD event were estimated from survival models using Cox proportional hazard regression. Discrimination, calibration, and net reclassification of re-estimated CVD risk prediction models including APOs were examined. RESULTS: There was no significant association between any APO, HDP, or GDM and time to CVD outcome in survival models (95% confidence intervals all include 1). Including any APO, HDP, GDM in the CVD risk prediction model did not significantly improve discrimination and there were no clinically relevant changes in net reclassification of cases and non-cases. The strongest predictor of time to CVD event in the survival models was Black race, with hazard ratios ranging from 1.59 to 1.62, statistically significant for all three models. CONCLUSION: Women with APOs did not have an additional risk of CVD, controlling for traditional risk factors in the PCE and this sex-specific factor did not improve risk prediction. Black race was consistently a strong predictor of CVD even with data limitations. Further study of APOs can help determine how to best use this information for CVD prevention in women.

Rectal Contrast CT Scans of Limited Utility in Diagnosing Colonic Injuries in Penetrating Trauma: A Meta-Analysis.

OBJECTIVES: Using rectal contrast computed tomography (CT) to identify traumatic colorectal injuries has become commonplace; however, these injuries remain relatively infrequent findings on CTs obtained for penetrating back and flank trauma. We conducted a meta-analysis to ascertain the efficacy of rectal contrast CT in identifying such injuries in victims penetrating injuries. METHODS: PubMed and Embase were queried for relevant articles between 1974 and 2022. Review articles, case studies, and non-English manuscripts were excluded. Studies without descriptive CT and operative findings were excluded. Positive scans refer to rectal contrast extravasation. Sensitivity and specificity of rectal contrast CT scans were calculated with aggregated CT findings that were cross-referenced with laparotomy findings. RESULTS: Only 8 manuscripts representing 506 patients quantified colorectal injuries and specified patients with rectal contrast extravasation. Seven patients with true colorectal injuries had no contrast extravasation on CT. There was one true positive scan. Another scan identified contrast extravasation, but laparotomy revealed no colorectal injury. Rectal contrast had sensitivity of 12.5%, specificity 99.8%, positive predictive value (PPV) 50%, negative predictive value (NPV) 99%, and a false negative rate of 88% in identifying colonic injuries. DISCUSSION: The summation of 8 manuscripts suggest that the addition of rectal contrast in identifying colonic and rectal injuries may be of limited utility given its poor sensitivity and may be unnecessary. In its absence, subtle clues such as hematomas, extraluminal air, IV-dye extravasation, and trajectory may be additional indicators of injury. Further investigations are required to demonstrate a true benefit for the addition of rectal contrast.

Impact of gonadectomy on maturational changes in brain volume in adolescent macaques.

Adolescence is a transitional period between childhood and adulthood characterized by significant changes in global and regional brain tissue volumes. It is also a period of increasing vulnerability to psychiatric illness. The relationship between these patterns and increased levels of circulating sex steroids during adolescence remains unclear. The objective of the current study was to determine whether gonadectomy, prior to puberty, alters adolescent brain development in male rhesus macaques. Ninety-six structural MRI scans were acquired from 12 male rhesus macaques (8 time points per animal over a two-year period). Six animals underwent gonadectomy and 6 animals underwent a sham operation at 29 months of age. Mixed-effects models were used to determine whether gonadectomy altered developmental trajectories of global and regional brain tissue volumes. We observed a significant effect of gonadectomy on the developmental trajectory of prefrontal gray matter (GM), with intact males showing peak volumes around 3.5 years of age with a subsequent decline. In contrast, prefrontal GM volumes continued to increase in gonadectomized males until the end of the study. We did not observe a significant effect of gonadectomy on prefrontal white matter or on any other global or regional brain tissue volumes, though we cannot rule out that effects might be detected in a larger sample. Results suggest that the prefrontal cortex is more vulnerable to gonadectomy than other brain regions.

Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood.

INTRODUCTION: Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups. RESEARCH DESIGN AND METHODS: Participants with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively. RESULTS: The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01). CONCLUSIONS: There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.

Estimating individualized treatment regimes from crossover designs.

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a prespecified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine since they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.

Longitudinal Phenotypes of Type 1 Diabetes in Youth Based on Weight and Glycemia and Their Association With Complications.

CONTEXT: Subclinical and clinical complications emerge early in type 1 diabetes (T1D) and may be associated with obesity and hyperglycemia. OBJECTIVE: Test how longitudinal "weight-glycemia" phenotypes increase susceptibility to different patterns of early/subclinical complications among youth with T1D. DESIGN: SEARCH for Diabetes in Youth observational study. SETTING: Population-based cohort. PARTICIPANTS: Youth with T1D (n = 570) diagnosed 2002 to 2006 or 2008. MAIN OUTCOME MEASURES: Participants were clustered based on longitudinal body mass index z score and HbA1c from a baseline visit and 5+ year follow-up visit (mean diabetes duration: 1.4 ± 0.4 years and 8.2 ± 1.9 years, respectively). Logistic regression modeling tested cluster associations with seven early/subclinical diabetes complications at follow-up, adjusting for sex, race/ethnicity, age, and duration. RESULTS: Four longitudinal weight-glycemia clusters were identified: The Referent Cluster (n = 195, 34.3%), the Hyperglycemia Only Cluster (n = 53, 9.3%), the Elevated Weight Only Cluster (n = 206, 36.1%), and the Elevated Weight With Increasing Hyperglycemia (EWH) Cluster (n = 115, 20.2%). Compared with the Referent Cluster, the Hyperglycemia Only Cluster had elevated odds of dyslipidemia [adjusted odds ratio (aOR) 2.22, 95% CI: 1.15 to 4.29], retinopathy (aOR 9.98, 95% CI: 2.49 to 40.0), and diabetic kidney disease (DKD) (aOR 4.16, 95% CI: 1.37 to 12.62). The EWH Cluster had elevated odds of hypertension (aOR 2.18, 95% CI: 1.19 to 4.00), dyslipidemia (aOR 2.36, 95% CI: 1.41 to 3.95), arterial stiffness (aOR 2.46, 95% CI: 1.09 to 5.53), retinopathy (aOR 5.11, 95% CI: 1.34 to 19.46), and DKD (aOR 3.43, 95% CI: 1.29 to 9.11). CONCLUSIONS: Weight-glycemia phenotypes show different patterns of complications, particularly markers of subclinical macrovascular disease, even in the first decade of T1D.

Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c.

BACKGROUND/OBJECTIVE: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as "dysglycemia phenotypes." METHODS: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). RESULTS: The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P < .001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P < .001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P < .05). CONCLUSIONS: There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment.