Claudia Pedroza

Childbirth remains the leading cause of inpatient hospital admissions in the United States, accounting for approximately 12% of all hospitalizations [1]. Cesarean delivery comprises 32% of all deli...

Preeclampsia is a leading cause of maternal death during pregnancy, and the role of circadian rhythms in predicting preeclampsia is not well understood. We aimed to determine whether glucose circadian rhythm disruption is associated with preeclampsia and can be used to predict this disorder. We analyzed a dataset of 92 pregnant individuals recruited with Continuous Glucose Monitoring (CGM). To study rhythmicity, we performed a cosinor analysis using the packages cosinor and cosinor2, and we calculated the non-parametric circadian rhythm variables using the nparACT package in R. Furthermore, we performed multiple-component cosinor analysis to detect internal oscillations and identify glucose postprandial peaks using the package CosinorPy in Python. Seventy-one participants (20 women with preeclampsia) had sufficient data for studying glucose circadian rhythmicity and performing all the chronobiological analyses. All the participants exhibited a significant circadian rhythm in their glucose oscillation. We developed a model including the time difference between the first postprandial peak and the last one, L5 start-time, and age that was predictive for preeclampsia. Patients diagnosed with preeclampsia from this model had a reduced amplitude and less robust glucose rhythmicity. We conclude that identifying abnormal glucose circadian rhythm during pregnancy may help to anticipate pregnancy-related disorders like preeclampsia.

Objective: Gastrointestinal (GI) complications are common in systemic sclerosis (SSc), yet the value of early symptoms as predictors of progression remains poorly defined. We aimed to determine whether baseline GI symptoms in early SSc predict progression to significant GI outcomes. Methods: We studied 450 participants from the GENISOS cohort with early SSc (≤5 years from first non-Raynaud’s symptom). Baseline assessments included demographics and clinical variables, including individual GI symptoms (GERD, dysphagia, bloating, constipation, diarrhea, peptic ulcer). Significant GI involvement was defined as Medsger GI severity score ≥2. Cox models evaluated associations between baseline symptoms and progression to significant GI disease and all-cause mortality. Additional models examined total baseline GI symptom burden and outcomes. Results: Participants were predominantly female (84%) with a mean age of 47.7 ± 13.3 years. Baseline GI symptoms were frequent, most commonly GERD (76%) and dysphagia (41%). 13% of the patients had significant GI involvement, with 4% presenting with significant GI disease at baseline. Baseline GERD (HR 4.15, P=0.019) and diarrhea (HR 2.25, P=0.021) were strong predictors of GI progression. When symptom burden was analyzed, each additional baseline GI symptom increased the hazard of significant GI progression by 53% (P=0.003). On the other hand, peptic ulcer disease (HR 2.21, P=0.007) and diarrhea (HR 1.48, P=0.045) predicted higher mortality, with each additional symptom raising mortality risk by 29% (P<0.001). Conclusions: Early GI symptoms and overall symptom burden predict GI progression and mortality in SSc. Symptom-based profiling may offer a practical strategy for clinical risk stratification and trial enrichment. Key messages: Key message 1: Baseline GERD and diarrhea strongly predict progression to significant gastrointestinal involvement in systemic sclerosis. Key message 2: Greater baseline gastrointestinal symptom burden increases risk of severe GI disease progression and all-cause mortality. Key message 3: Simple symptom profiles may guide early risk stratification and patient selection for GI-focused clinical trials.

Small sample sizes are a common problem in disability research. Here, we show how Bayesian methods can be applied in small sample settings and the advantages that they provide. To illustrate, we provide a Bayesian analysis of employment status (employed vs. unemployed) for those with disability. Specifically, we apply empirically informed priors, based on large-sample (N = 95,593) July 2019 Current Population Survey (CPS) microdata to small subsamples (average n = 26) from July 2021 CPS microdata, defined by six specific difficulties (i.e., hearing, vision, cognitive, ambulatory, independent living, and self-care). We also conduct a sensitivity analysis, to illustrate how various priors (i.e., theory-driven, neutral, noninformative, and skeptical) impact Bayesian results (posterior distributions). Bayesian findings indicate that people with at least one difficulty (especially ambulatory, independent living, and cognitive difficulties) are less likely to be employed than people with no difficulties. Overall, results suggest that Bayesian analyses allow us to incorporate known information (e.g., previous research and theory) as priors, allowing researchers to learn more from small sample data than when conducting a traditional frequentist analysis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).