Journal of evaluation in clinical practice · 2026
What Do We Actually Measure? The Epistemic Limits of Surrogate Markers in Chronic Disease.
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Fleming Richard M, Fleming Matthew R
Аннотация
BACKGROUND: Contemporary chronic disease evaluation relies heavily on surrogate markers such as LDL cholesterol, hemoglobin A1c, blood pressure thresholds, weight and body mass index. Although these measures are reproducible and supported by population level outcome data, they do not directly quantify the biologic processes that drive patient level instability. This mismatch creates an epistemic gap between what surrogate markers measure and what clinicians believe they measure.
OBJECTIVE: To examine the epistemic status of surrogate markers, contrast them with biologic disease activity, and clarify how measurement misalignment distorts clinical judgment, guideline development, and evaluation science.
METHODS: We conducted a conceptual and evidentiary analysis integrating findings from multiomics inflammation research, endothelial biology, metabolic flux studies, immunothrombosis, emergency medical services registries, neonatal intensive care monitoring paradigms, and community food insecurity research. A revised conceptual model was developed to illustrate the predictive (surrogate) versus physiologic (biologic) measurement domains and the feedback loops that perpetuate misalignment.
RESULTS: Evidence demonstrates that surrogate markers fail to capture dynamic biologic processes-including inflammatory signaling, endothelial dysfunction, metabolic variability, and thrombotic activation-that determine clinical instability. Patients frequently experience myocardial infarction, stroke, sepsis, or metabolic crisis despite "controlled" surrogate markers. Institutional reliance on surrogate endpoints creates self reinforcing feedback loops that obscure biologic instability and systematically misclassify risk.
CONCLUSIONS: Surrogate markers function as epistemic artifacts: administratively convenient proxies that do not quantify the biologic substrates of chronic disease. A biologically grounded epistemology requires integrating direct measures of physiologic instability into clinical evaluation while acknowledging practical, economic, and technological constraints. Aligning measurement with biologic reality is essential for improving diagnostic accuracy, therapeutic decision making, and patient outcomes.
OBJECTIVE: To examine the epistemic status of surrogate markers, contrast them with biologic disease activity, and clarify how measurement misalignment distorts clinical judgment, guideline development, and evaluation science.
METHODS: We conducted a conceptual and evidentiary analysis integrating findings from multiomics inflammation research, endothelial biology, metabolic flux studies, immunothrombosis, emergency medical services registries, neonatal intensive care monitoring paradigms, and community food insecurity research. A revised conceptual model was developed to illustrate the predictive (surrogate) versus physiologic (biologic) measurement domains and the feedback loops that perpetuate misalignment.
RESULTS: Evidence demonstrates that surrogate markers fail to capture dynamic biologic processes-including inflammatory signaling, endothelial dysfunction, metabolic variability, and thrombotic activation-that determine clinical instability. Patients frequently experience myocardial infarction, stroke, sepsis, or metabolic crisis despite "controlled" surrogate markers. Institutional reliance on surrogate endpoints creates self reinforcing feedback loops that obscure biologic instability and systematically misclassify risk.
CONCLUSIONS: Surrogate markers function as epistemic artifacts: administratively convenient proxies that do not quantify the biologic substrates of chronic disease. A biologically grounded epistemology requires integrating direct measures of physiologic instability into clinical evaluation while acknowledging practical, economic, and technological constraints. Aligning measurement with biologic reality is essential for improving diagnostic accuracy, therapeutic decision making, and patient outcomes.