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Modeling Dyadic Interdependence in Endocrine Functioning: A Multilevel Machine Learning Study of Adults with Cancer
IEEE Transactions on Bio-Medical Engineering
|July 6, 2026
Summary
Cancer patients and caregivers show linked stress hormone patterns. Machine learning revealed distinct ways partner stress levels influence individual cortisol and DHEAS, but not alpha-amylase, for mean and variability measures.
Area of Science:
- Psychoneuroendocrinology
- Computational Biology
- Oncology Support
Background:
- Dyadic interdependence is crucial in chronic illness, particularly in cancer patient-caregiver relationships.
- Understanding shared physiological stress responses can inform supportive interventions.
- Biomarkers like cortisol, alpha-amylase, and DHEAS offer objective measures of stress.
Purpose of the Study:
- To evaluate dyadic endocrine interdependence between cancer patients and spousal caregivers.
- To assess how partner-level stress biomarker data improves individual stress response prediction using machine learning (ML).
- To identify distinct patterns of endocrine interdependence for mean and variability of stress biomarkers.
Main Methods:
- A multilevel ML framework was applied to 149 patient-caregiver dyads.
- Predictive performance was evaluated for diurnal slopes of cortisol, alpha-amylase, and DHEAS.
- SHAP analysis characterized predictor importance and identified structural signatures of dyadic influence.
Main Results:
- Nonlinear ML models significantly predicted 11 of 12 stress biomarker outcomes.
- Partner signals significantly influenced cortisol and DHEAS mean outcomes, but not alpha-amylase.
- Variability outcomes showed partner influence across all three biomarkers, with distinct unidirectional and bilateral patterns identified via SHAP analysis.
Conclusions:
- Dyadic interdependence in endocrine regulation is evident, with unique structural signatures for mean and variability measures.
- This ML framework provides a novel approach to identify psychobiological pathways for enhancing health in cancer populations.
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