Clinical predictive factors in diabetic kidney disease progression

Abstract

Diabetic kidney disease ( DKD) represents a major component of the health burden associated with type 1 and type 2 diabetes. Recent advances have produced an explosion of ‘novel’ assay‐based risk markers for DKD, though clinical use remains restricted. Although many patients with progressive DKD follow a classical albuminuria‐based pathway, non‐albuminuric DKD progression is now well recognized. In general, the following clinical and biochemical characteristics have been associated with progressive DKD in both type 1 and type 2 diabetes: increased hemoglobin A1c, systolic blood pressure, albuminuria grade, early glomerular filtration rate decline, duration of diabetes, age (including pubertal onset) and serum uric acid; the presence of concomitant microvascular complications; and positive family history. The same is true in type 2 diabetes for male sex category, in patients following an albuminuric pathway to DKD, and also true for the presence of increased pulse wave velocity. The following baseline clinical characteristics have been proposed as risk factors for DKD progression, but with further research required to assess the nature of any relationship: dyslipidemia (including low‐density lipoprotein, total and high‐density lipoprotein cholesterol); elevated body mass index; smoking status; hyperfiltration; decreases in vitamin D, hemoglobin and uric acid excretion (all known consequences of advanced DKD); and patient test result visit‐to‐visit variability (hemoglobin A1c, blood pressure and high‐density lipoprotein cholesterol). The development of multifactorial ‘renal risk equations’ for type 2 diabetes has the potential to simplify the task of DKD prognostication; however, there are currently none for type 1 diabetes‐specific populations. Significant progress has been made in the prediction of DKD progression using readily available clinical data, though further work is required to elicit the role of several variables, and to consolidate data to facilitate clinical implementation.