‘U’r-in(e)!—urine albumin-creatinine ratio: the old kid on a new block!

This editorial refers to ‘Longitudinal trajectories of albuminuria and risk of subclinical and clinical heart failure, atrial fibrillation, and coronary heart disease: the MESA study’, by S. Masrouri et al., https://doi.org/10.1093/eurjpc/zwaf699. In this era of precision medicine, increasing emphasis has been placed on developing cardiovascular disease (CVD) risk predictors to guide primary prevention. However, these often rely on single-time-point measurements, which may obscure dynamic temporal patterns that may offer additional insight into CVD risk prediction. In this issue of the European Journal of Preventive Cardiology, Masrouri et al.1 examine the associations between 5- and 10-year longitudinal trajectories of urine albumin-to-creatinine ratio (UACR) and both clinical outcomes (heart failure [HF], atrial fibrillation [AF], coronary heart disease [CHD], and their composite) and subclinical markers of CVD (N-terminal pro–B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin T [hsTnT], and interstitial myocardial fibrosis [IMF]) in participants in the Multi-Ethnic Study of Atherosclerosis (MESA). The authors report several important findings. First, they identified three distinct UACR trajectories over the 5-year (‘slow rise at low level,’ ‘slow rise at medium level,’ and ‘sustained medium to high’) and 10-year (‘slow rise at low level,’ ‘slow rise at medium level,’ and ‘rapid rise’) periods. Second, trajectories were associated with a stepwise increase in risk of outcomes. The trajectories characterized by the greatest UACR increases—the sustained-medium-to-high and rapid-rise patterns, henceforth referred to as high-risk trajectories—were associated with the highest risk of outcomes over 5 and 10 years: 145% and 191% higher risk of HF, 101% and 90% higher risk of HF and mortality, 77% and 87% higher risk of AF, and 53% and 19% higher risk of CHD, respectively. Third, these associations were mostly independent of baseline UACR levels, except for the 5-year associations with HF with reduced ejection fraction and CHD, which were attenuated by baseline UACR, suggesting some potential mediation. Even among participants with normal baseline UACR, those with a high-risk trajectory had significantly worse outcomes, highlighting the need for repeat measurements as this group would have been overlooked by traditional single-measurement approaches to UACR assessment. In secondary analyses, the high-risk group had the highest adjusted mean hsTnT and NT-proBNP as well as a greater magnitude of adjusted IMF, suggesting that this UACR trajectory effectively identified myocardial injury and stress. Albuminuria, defined as urinary albumin excretion ≥30 mg per day, is present in 15–20% of adults older than age 65 years and is a well-established marker of CVD and kidney disease.2 Albuminuria reflects systemic endothelial dysfunction and microvascular injury, core mechanisms underlying HF, AF, and CHD.2,3 However, single-spot urine protein measurements are unreliable because urinary protein excretion varies widely with hydration status, circadian rhythm, and physical activity.2,4 Although 24-h urine collection is the gold standard, its impracticality limits routine clinical use. The spot UACR, computed by the ratio of urine albumin (in mg) to urine creatinine (in g), overcomes these limitations by accounting for urine concentration and provides an estimate of albumin excretion that closely correlates with 24-h measurements. Given its ability to identify renal injury, UACR has routinely been used to risk-stratify patients and identify higher-risk individuals with diabetes and chronic kidney disease who require closer monitoring and intensified management. Recently, there has been a growing interest in using biomarkers that identify end-organ damage and thereby more accurately prognosticate outcomes and identify higher-CVD-risk individuals. One such paradigm is the definition of cardio–kidney–metabolic syndrome (CKM), which highlights the shared pathophysiology linking renal disease and CVD.5 Along with this classification for CKM stages, a risk prediction equation—the American Heart Association′s Predicting Risk of CVD Events (PREVENT) equation—was developed.6 Importantly, UACR is one of the variables used in the PREVENT equation, a recognition of its clinical relevance in CVD risk assessment. UACR is relatively stable, making its use as a risk predictor advantageous; both the 2024 European and 2025 U.S. guidelines for hypertension include UACR and other markers of kidney function in screening for CVD risk in patients with hypertension.7,8 Like UACR, other biomarkers that reflect downstream organ injury rather than upstream risk alone are commonly used in other disease states: NT-proBNP captures hemodynamic myocardial stretch, hs-TnT reflects myocardial injury, and high-sensitivity C-reactive protein, though nonspecific, serves as a marker of inflammation and atherosclerotic risk. Strictly speaking, these biomarkers do not identify isolated organ injury but rather signal a broader systemic injury in which damage to one organ implies vulnerability of others. This is exemplified in diabetes, in which the severity of albuminuria parallels the extent of retinopathy, reflecting shared microvascular pathology.9 The use of biomarkers enhances risk stratification, particularly in patients with overlapping cardiometabolic conditions. Recognizing that end-organ damage rarely occurs in isolation should prompt earlier, more comprehensive, and multidisciplinary intervention (Figure 1). The finding of increased NT-proBNP, hsTnT, and IMF in the high-risk-UACR-trajectory group is concerning, considering their potentially additive, or worse, synergistic effect on the pathogenesis of CVD. UACR is already utilized in CVD risk prediction, and we recently called for NT-proBNP and hsTnT to be elevated to CVD risk enhancers, a status which they definitely deserve.10 Patterns of biomarker changes reflect the progression of end-organ damage in one organ, which is associated with damage to other organs. These risks could potentially be additive, and the use of biomarkers may aid in personalized risk prediction. Abbreviations: AHA-PREVENT, American Heart Association′s Predicting Risk of CVD Events; hsTnT, high-sensitivity troponin T; NT-proBNP, N-terminal pro–B-type natriuretic peptide; UACR, urine albumin-to-creatinine ratio. This study highlights the added value of serial UACR measurements for CVD risk prediction. Longitudinal UACR trajectories provide prognostic information beyond a single measurement, with more than one-third of individuals in the high-risk trajectory exhibiting normal baseline UACR. High-risk trajectories were more common among individuals who were male, non-White, uninsured, less educated, or current or former smokers, or who had higher baseline UACR, blood pressure, or body mass index. Prevalence was also greater among people with diabetes or obesity. Routine serial UACR measurement in cardiology practice, therefore, represents a low-cost, widely available opportunity to refine cardiovascular risk stratification and advance preventive care, particularly because albuminuria can be treated. Traditionally, drugs resulting in renin–angiotensin–aldosterone system (RAAS) blockade have been used to reduce albuminuria. Newer agents acting on the RAAS pathway (such as the nonsteroidal mineralocorticoid receptor antagonist finerenone) and alternative pathways (such as sodium–glucose transporter–2 inhibitors) also reduce albuminuria and improve cardio-kidney-metabolic health. Although the study is informative, a critical question is how to implement the findings in clinical practice. For example, although high-risk trajectories were identified statistically, how does one identify them for clinical use? Furthermore, the optimal interval for serial UACR assessment is unclear. Finally, UACR measurement is not without limitations, including variability related to muscle mass, physical activity, diet, hydration, and posture. These factors are mitigated through first morning urine sampling and serial measurements, as employed in MESA. Additionally, because the included participants in the analyses were healthier than those excluded, effect estimates may be conservative. Cross-cohort validation in more diverse populations is therefore essential. Nonetheless, the message is clear and timely: when measured serially, UACR is a powerful and practical tool for cardiovascular risk assessment. The verdict is clear: UACR, you’re in! Yakubu Bene-Alhasan (MD MPH (Visualization [equal]; Writing—original draft [lead]; Writing—review & editing [equal])), Vijay Nambi (MD PhD (Conceptualization [equal]; Writing—review & editing [lead])), and Christie Ballantyne (MD (Conceptualization [equal]; Writing—review & editing [equal])) No new data were generated or analyzed in support of this research.