The value of Bayesian predictive projection for variable selection: an example of selecting lifestyle predictors of young adult well-being

Determinants of maternal breast milk cortisol increase: Examining dispositional and situational factors

BACKGROUND

Breast milk is a rich nutritional source, containing numerous proteins, carbohydrates, and hormones that impact long-term offspring development. Strikingly, predictors and correlates of breast milk composition remain largely unknown. Building on a previously discovered increase in breast milk cortisol concentration from 2 to 12 weeks postpartum, we investigated potential predictors of maternal breast milk cortisol in the first three months post-delivery by examining a suite of maternal dispositional (e.g., attachment, adverse childhood experiences or ACEs) and situational factors (e.g., partner support, self-efficacy).

METHODS

Data from 73 mothers were collected prenatally, at birth, and 2-, 6- and 12 weeks postpartum. The analyses, which sought to predict postnatal changes in breast milk cortisol, included a pool of theoretically-sound constructs (Table 1) and an exploratory data-driven approach. We fit models differing in complexity as preregistered: 1) Random Forest models, capable of modeling interactions and non-linear relationships, and 2) Bayesian linear models, allowing to model change over time while less prone to overfitting.

RESULTS

Overall, we found that no single variable had strong predictive value beyond the known predictors of cortisol, such as time since awakening and time of collection. However, results from both models suggest that ACEs carry information that warrants future investigations, pointing towards a negative relationship with cortisol concentration in breast milk, albeit with a minimal effect size.

CONCLUSION

Using sophisticated models, we found that early life stress may play a role in physiological stress markers in breast milk in the first three months postpartum, with potential implications for offspring development.

Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, granzyme B and fibronectin), immune suppression (eg, forkhead box P3), exhaustion and cytotoxicity (eg, CD8), Programmed death ligand 1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.

Predictors of positive mental health in higher education students. A cross-sectional predictive study

PURPOSE

To describe positive mental health (PMH) psychological vulnerability (PV) and identify predictors of PMH in higher education students (HES).

DESIGN AND METHODS

A cross-sectional, predictive study was performed with a convenience sample of 3322 students, using an online questionnaire with sociodemographic information, the PMH Questionnaire, and the PV Scale.

FINDINGS

The majority scored a flourishing level, and 67.7% of the participants scored high levels of PV. The Regression Model found a significant predictive variable for PMH.

PRACTICE IMPLICATIONS

Gender, age, regular exercise, healthy diet, number of meals per day, and leisure activities are significant positive predictors of PMH. PV is the sole significant negative predictor. Therefore, improving mental health literacy can be a strategy to support HES.

Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers

Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods† may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.