Diabetes care is largely driven by surrogates. The US Institute of Medicine defines surrogates as “biomarker[s] intended to substitute for a clinical endpoint [and] expected to predict clinical benefit (or harm . . .) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence.”1 In diabetes, concentrations of glycated haemoglobin (HbA_1c) are used as a surrogate marker for outcomes that are important to patients, such as blindness or amputation. Other surrogates such as blood pressure, lipids, albumin excretion rates, and C reactive protein have been used to predict outcomes of cardiovascular disease and to guide clinical practice in people with or without diabetes. Much of the evidence for clinical interventions is based on their effect on surrogate outcomes rather than those that matter to patients such as quality of life or avoidance of vision loss or renal failure. Moreover, because these “hard” end points generally show much smaller responses to interventions than surrogate markers, many of the widely accepted strategies for diabetes may be based on artificially inflated expectations.

Recent studies have challenged the assumption that reliance on surrogates can accurately predict the effect of treatment on hard outcomes. There are the oral hypoglycaemic drugs that reduce HbA_1c but increase the risk of cardiovascular events,2 antihypertensive drugs that do not reduce the risk of stroke,3 and drugs that improve cholesterol profiles but do not reduce cardiovascular events.4 Explanations for such phenomena include unwanted effects of the drug or an incomplete understanding of the pathophysiology of the disease.5 But why have …