Relationships of Copper, Zinc, and the Cu/Zn Ratio with Human Chronic Diseases

Abstract

Relationships of Copper, Zinc, and the Cu/Zn Ratio with Human Chronic Diseases: Biological Mechanisms, Clinical Evidence, and an Integrated Theoretical Model for AI-DMRS Across Eight Chronic Disease Clusters Author: T. Aydın Doğan, MDAffiliation: Istanbul Okmeydanı Training and Research Hospital, Department of Internal Medicine, Istanbul, TürkiyeORCID: 0009-0002-2838-4926Publication Date: October 14, 2026Publication Type: Extended Theoretical Review and Clinical Validation Hypothesis Overview This theoretical review examines the relationships between copper (Cu), zinc (Zn), and the serum copper-to-zinc (Cu/Zn) ratio in human chronic diseases, emphasizing their biological roles in trace element homeostasis, inflammation, oxidative stress, immune regulation, aging, and multimorbidity. Drawing upon human observational studies, prospective cohorts, and meta-analyses, the article critically evaluates the clinical significance of Cu and Zn alterations across eight interconnected chronic disease clusters: Cardiometabolic diseases Cardiorenal diseases Hepatometabolic diseases Respiratory diseases Autoimmune and inflammatory diseases Musculoskeletal diseases Neurovascular and neurodegenerative diseases Psychiatric disorders Scientific Rationale Human studies have reported associations between altered Cu and Zn concentrations, elevated Cu/Zn ratios, inflammatory activity, and several chronic disease outcomes. Evidence includes prospective investigations of chronic obstructive pulmonary disease (COPD), longitudinal studies of sarcopenia, and meta-analyses involving rheumatoid arthritis, heart failure, and neurodegenerative disorders. However, these associations vary according to disease stage, nutritional status, inflammatory activity, organ function, and other clinical factors. A central concept of this article is that identical Cu/Zn ratios may arise from different absolute Cu and Zn concentrations, potentially representing distinct biological phenotypes. Accordingly, the Cu/Zn ratio should be interpreted alongside its individual components rather than treated as an independent or disease-specific diagnostic marker. Original Theoretical Contribution: AI-DMRS The article introduces an integrated theoretical framework for the Artificial Intelligence–Dynamic Multimorbidity Risk System (AI-DMRS). The proposed framework combines individual Cu and Zn measurements, the Cu/Zn ratio, high-sensitivity C-reactive protein (hs-CRP), homocysteine (HCY), demographic characteristics, clinical risk factors, and longitudinal biomarker changes. Its central hypothesis is that integrating these biological and clinical variables through explainable artificial intelligence may improve the prediction of future chronic disease clustering, multimorbidity development, and transitions between disease clusters. The framework also considers potential interactions involving copper homeostasis, S-adenosylhomocysteine hydrolase (AHCY), methylation metabolism, and redox regulation. These mechanistic relationships are presented as testable hypotheses rather than established clinical causal pathways. Clinical and Research Significance This article distinguishes established biological functions, observational human associations, mechanistic hypotheses, and unvalidated clinical predictions. It proposes a research strategy for assessing whether Cu/Zn-related biomarkers provide incremental prognostic information beyond conventional clinical risk factors. The AI-DMRS framework is intended to support future prospective clinical validation, model calibration, external validation, and the development of explainable clinical decision-support approaches. The proposed system is investigational and is not intended to replace clinical diagnosis, established risk assessment tools, or evidence-based treatment decisions. Conclusion Copper and zinc homeostasis represents a biologically relevant area of investigation across multiple chronic diseases. The Cu/Zn ratio may provide a useful summary of relative trace element concentrations, but its interpretation requires consideration of individual Cu and Zn levels, inflammation, nutritional status, and clinical context. The AI-DMRS hypothesis proposes that integrating these variables with longitudinal clinical information may offer a new approach to investigating the early prediction of multimorbidity. Its clinical utility and predictive performance remain to be established through rigorous prospective validation.