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Schaaf, G. W.

Publications and source records attributed to Schaaf, G. W..

2 recordsLinked to original sources
bioRxiv2026

Serum pro-N-cadherin: a biomarker of cardiac fibrosis and diastolic dysfunction in irradiated non-human primates

BackgroundThe delayed effects of radiation exposure on the heart often manifest as cardiac fibrosis and diastolic dysfunction, which can develop years after exposure. However, no FDA-approved serological biomarker is available to assess the risk of individuals for developing radiation-related heart disease (RRHD). ObjectivesSerum pro-N-cadherin (PNC) has shown promise as a marker for predicting the onset of heart failure in the general population. We hypothesize that serum PNC levels will correlate with the risk of RRHD following radiation exposure. MethodsWe examined male non-human primates (NHPs) exposed to total-body irradiation (TBI) and unirradiated controls from the Wake Forest University radiation late effects cohort. NHPs exhibited cardiac fibrosis scores ranging from less severe (F0-1) to more severe (F2-3). Cardiac tissue samples collected at necropsy, median 6.8 years post-irradiation, were stained for PNC by immunohistochemistry. PNC was quantified in longitudinal serum samples collected 2, 1 and 0 years before necropsy. The associations of serum PNC levels with cardiac fibrosis scores and echocardiographic parameters were examined. ResultsHistological examinations showed aberrant localization of PNC in NHPs with cardiac fibrosis. Elevated serum PNC levels significantly correlated with severe cardiac fibrosis (AUC = 0.81, p = 0.006) and echocardiogram parameters of diastolic dysfunction. Cardiac fibrosis was the only measured comorbidity with a significant difference in serum PNC. ConclusionsOur results demonstrate that serum PNC significantly correlates with cardiac fibrosis and diastolic dysfunction in irradiated NHPs. These findings pave the way for future clinical studies to develop serum PNC as a biomarker of RRHD in humans. HIGHLIGHTSO_LIRadiation-related heart disease is an often under-recognized complication of radiation exposure and radiation therapy, which has no FDA-approved biomarkers for assessing risk. C_LIO_LIOur results reveal that serum pro-N-cadherin is a biomarker of cardiac fibrosis and diastolic dysfunction in non-human primates that survived radiation exposure. C_LIO_LIThis study lays the foundation for further research into the development of serum pro-N-cadherin as a biomarker for assessing the risk of radiation-related heart disease in humans. C_LI

physiology↗
bioRxiv2026

Developing And Internally Validating AI-Based Aging Resilience Biomarkers in Non-Human Primates

Quantifying biological aging is crucial for understanding functional decline before the onset of morbidity. While many accelerated aging and frailty measures based on clinical data exist for humans and several for rodent models of aging, there are few options for non-human primates (NHPs). NHP clinical data has several unique features including a lack of clinically delineated normative values for features and variability in data collection over long lifespans. There are also wide discrepancies in the number of available clinical measures and number of animals across data sets. To address these challenges, we developed and validated "Aging Resilience" (AR) metrics using longitudinal, routine clinical data from two distinct non-human primate cohorts: 4,328 baboons and 281 rhesus macaques. We trained five computational models--including Linear Mixed-Effects Models, Random Forest, and Recurrent Neural Networks (RNN)--to predict chronological age, subsequently deriving AR metrics that represent the velocity (Rate of Aging) and cumulative burden (Normalized Cumulative Aging) of physiological deviation. While linear models achieved high precision in predicting chronological age (test R2 up to 0.99), they correlated poorly with actual lifespan. In contrast, AR metrics derived from non-linear models (RNN and Random Forest) displayed strong predictive validity for mortality (Pearsons r > 0.8). These findings highlight a critical paradox: models that best predict chronological age do not necessarily capture the biological resilience determining healthspan. This study establishes a scalable framework for monitoring biological aging in translational models using standard veterinary records.

bioinformatics↗