Developments in albuminuria testing: A key biomarker for detection, prognosis and surveillance of kidney and cardiovascular disease-A practical update for clinicians

Abstract

Albuminuria, the abnormal presence of albumin in urine, is a key marker of kidney damage and a strong predictor of kidney and cardiovascular outcomes. Its clinical significance has evolved from early historical observations to its current role in chronic kidney disease (CKD) detection, risk stratification and treatment monitoring. Advances in measurement techniques and evidence from large-scale studies have reinforced its prognostic value, particularly in guiding interventions such as renin-angiotensin-aldosterone system blockade, sodium-glucose cotransporter-2 inhibitors and non-steroidal mineralocorticoid receptor antagonists. Albuminuria assessment is now integrated into CKD staging, cardiovascular risk prediction models and therapy selection. Despite its established utility, challenges remain, including measurement variability, assay standardisation and under-utilisation in clinical practice. This review provides a practical update on albuminuria testing, summarising its historical development, technical aspects and clinical implications, and emphasising its role in CKD management and cardiovascular risk assessment.

Keywords: GLP‐1 analogue; SGLT2 inhibitor; diabetes complicationscardiovascular disease; macrovascular disease; type 2 diabetes.

FIGURE 1

Pathophysiology of albuminuria (A: Brenner hypothesis; B: Remuzzi hypothesis; C: Steno hypothesis). P_GC, glomerular capillary pressure; SNGFR, single‐nephron glomerular filtration rate.

FIGURE 2

Cardiovascular outcomes with omission of kidney disease measures and traditional risk factors in a chronic kidney disease (CKD) population. Figure shows the difference in C statistics and 95% CIs for four cardiovascular outcomes with omission of kidney disease measures and traditional risk factors from a model with all risk factors in a CKD population. The discriminatory capacity, as reflected by the C‐statistic, of the full model including all clinical parameters is shown in the top row of the forest plot for each of the four cardiovascular outcomes. Moreover, the corresponding reduction in the C‐statistic is displayed after the removal of individual variables (estimated glomerular filtration rate [eGFR], albumin‐to‐creatinine ratio [ACR], eGFR and ACR, diabetes, etc.). For each cardiovascular outcome, the largest reduction (or one of the largest reductions) in the C‐statistic was observed after the removal of ACR. These findings suggest that ACR has a stronger predictive performance for cardiovascular outcomes than most traditional cardiovascular risk markers.

FIGURE 3

Hazard ratios of all‐cause (upper panels) and cardiovascular (lower panels) mortality according to albumin‐to‐creatinine ratio (ACR) in patients with and without diabetes (left two panels), and with and without hypertension (right two panels) (117, 118). Panels A and B represent results of all‐cause and panels C and D of cardiovascular mortality. Significant interactions between ACR and hypertension status or diabetes status are indicated by ‘x’ signs. These data indicate that the predictive value of ACR for all‐cause and cardiovascular mortality is similar in patients with or without hypertension, and similar in patients with or without diabetes. The fact that in all panels the line of subjects with diabetes (or hypertension) lies higher than the line of subjects without diabetes (or hypertension) reflects the intrinsic higher risk for mortality in patients with diabetes (or hypertension).

FIGURE 4

Trial‐level analyses for the association between initial treatment effects on change in albuminuria and long‐term treatment effects on the clinical end‐point for participants who had a baseline albumin‐to‐creatinine ratio (ACR) of more than 30 mg/g (171). The composite clinical end‐point was the incidence of kidney failure, the doubling of serum creatinine concentration or eGFR of less than 15 mL/min per 1.73 m². The coloured circles indicate different intervention types, and each circle represents a separate individual clinical trial, with the size of the circle proportional to the number of events. The line of regression through the studies, Bayesian confidence bands and Bayesian prediction bands from the model are shown. The correlation with a statistically significant slope indicates that larger early treatment effects on albuminuria are associated with larger treatment effects in reducing the risk of the clinical kidney end‐point. These data support a role for albuminuria as a reasonably likely valid surrogate end‐point in trials of CKD progression. BCI, Bayesian credible interval; eGFR, estimated glomerular filtration rate; HR, hazard ratio; RAAS, renin‐angiotensin‐aldosterone system.