MRPC: An R Package for Inference of Causal Graphs

Symptom contributors to quality of life in schizophrenia: Exploratory factor and network analyses

Individuals with schizophrenia and other associated disorders experience significant disturbance to their quality of life (QoL) due to a multitude of co-occurring symptoms. Popular evidence-based practices (EBPs) devote significant effort to reduce positive symptomatology in order to prevent relapse, while emerging research posits that other symptoms (cognitive deficits, negative and affective symptoms) are more indicative of QoL disturbance. This study sought to examine the impact of symptom constructs on QoL and attempt to infer directionality of influence via network analysis. A total of 102 recovery phase adult outpatients with schizophrenia spectrum disorders were assessed on positive, negative, and affective symptomatology, in addition to QoL and cognitive abilities. Exploratory factor analysis and network analysis were performed to identify associations and infer directed influence between symptom constructs, and a directed acyclic graph was constructed to observe associations between symptom domains and QoL. Factor analysis results indicated that individual measures align with their respective symptom constructs. Strong factor correlations were found between QoL and the negative and affective symptom constructs, with weaker associations found between positive symptoms and cognition. Visualization of the network structure illustrated QoL as the central cluster of the network, and examination of the weighted edges found the strongest connectivity between QoL, negative symptomatology, and affective symptoms. More severe negative and affective symptoms were most directly linked with poorer QoL and may prove to be integral in attaining positive outcomes in schizophrenia treatment. Incorporation of psychosocial treatments in addition to pharmacotherapy may prove effective in targeting negative and affective symptoms.

Performance of a validated spontaneous preterm delivery predictor in South Asian and Sub-Saharan African women: a nested case control study

OBJECTIVES

To address the disproportionate burden of preterm birth (PTB) in low- and middle-income countries, this study aimed to (1) verify the performance of the United States-validated spontaneous PTB (sPTB) predictor, comprised of the IBP4/SHBG protein ratio, in subjects from Bangladesh, Pakistan and Tanzania enrolled in the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, and (2) discover biomarkers that improve performance of IBP4/SHBG in the AMANHI cohort.

STUDY DESIGN

The performance of the IBP4/SHBG biomarker was first evaluated in a nested case control validation study, then utilized in a follow-on discovery study performed on the same samples. Levels of serum proteins were measured by targeted mass spectrometry. Differences between the AMANHI and U.S. cohorts were adjusted using body mass index (BMI) and gestational age (GA) at blood draw as covariates. Prediction of sPTB < 37 weeks and < 34 weeks was assessed by area under the receiver operator curve (AUC). In the discovery phase, an artificial intelligence method selected additional protein biomarkers complementary to IBP4/SHBG in the AMANHI cohort.

RESULTS

The IBP4/SHBG biomarker significantly predicted sPTB < 37 weeks (n = 88 vs. 171 terms ≥ 37 weeks) after adjusting for BMI and GA at blood draw (AUC= 0.64, 95% CI: 0.57-0.71, p < .001). Performance was similar for sPTB < 34 weeks (n = 17 vs. 184 ≥ 34 weeks): AUC = 0.66, 95% CI: 0.51-0.82, p = .012. The discovery phase of the study showed that the addition of endoglin, prolactin, and tetranectin to the above model resulted in the prediction of sPTB < 37 with an AUC= 0.72 (95% CI: 0.66-0.79, p-value < .001) and prediction of sPTB < 34 with an AUC of 0.78 (95% CI: 0.67-0.90, p < .001).

CONCLUSION

A protein biomarker pair developed in the U.S. may have broader application in diverse non-U.S. populations.