William G. Herrington

Background and hypothesis: The associations of polygenic scores (PGSs) for eGFR with clinical outcomes in prevalent CKD populations have not been well evaluated. Collider bias may distort associations of exposures (including PGS) with outcomes in populations with prevalent disease. Methods: Analyses included European ancestry participants from UK Biobank and three CKD cohorts (SHARP, CRIC, and EMPA-KIDNEY) without ESKD at baseline. The exposure was a PGS for low eGFR derived from a genome-wide association study (GWAS) of eGFR in the FinnGen cohort. The primary outcomes were eGFR slope (ml/min/1.73m2/year) and kidney disease progression (KDP; defined as first occurrence of incident dialysis, transplantation, sustained eGFR decline by ≥40%, or sustained eGFR <10 ml/min/1.73 m2). Associations between the PGS and eGFR slope were assessed by linear regression and meta-analysed across studies. Associations with KDP were assessed using Cox proportional hazards models. Results: A total of 107 536 participants in UKB and 6846 participants from CKD cohorts were included in analyses. In UKB, higher PGS was associated with lower baseline eGFR (−5.09 [−5.31; −4.86] ml/min/1.73 m2 for those in the top 10% of the PGS vs. the rest), with greater risk of KDP (HR 1.36 [1.20–1.55]) and faster eGFR decline (−0.12 [−0.14; −0.09] ml/min/1.73m2/year). However, in CKD cohorts, higher PGS was not associated with baseline eGFR (0.15 [−0.73; 1.03] ml/min/1.73 m2) but was associated with lower risk of KDP (HR 0.76 [0.66–0.89]), with a directionally consistent (though non-significant) effect on eGFR decline (+0.19 [−0.01; 0.39] ml/min/1.73m2/year). Conclusions: A PGS for low eGFR is associated with faster eGFR decline and higher risk of KDP in a population unselected for CKD, but when applied in populations with CKD, non-causal associations are introduced which artificially reverse the associations seen. Applying PGS in populations selected for prevalent disease may lead to qualitatively misleading conclusions on the direction of associations with clinical outcomes.

Key Points: The kidney failure risk equation showed excellent discrimination for treated kidney failure outcome in patients with CKD. In a wide range of causes of CKD, urine tubular biomarkers improved prediction of kidney disease progression beyond established clinical risk models. EGF, kidney injury molecule-1, and monocyte chemoattractant protein-1 added the most prognostic value. Background: Urine tubular biomarkers have previously been associated with chronic kidney disease (CKD) progression independently of eGFR and albuminuria. We evaluated whether nine urine tubular biomarkers could improve prediction of kidney failure beyond serum creatinine and urine albumin‑creatinine ratio among adults with CKD. Methods: Among 5100 participants in Study of Heart and Kidney Protection With Empagliflozin, a randomized trial that assessed the effects of empagliflozin 10 mg versus matching placebo among patients with CKD at risk of progression, nine urine creatinine-indexed tubular biomarkers were measured, including α ‐1 microglobulin, dickkopf-3, EGF, interleukin-18, kidney injury molecule‐1 (KIM‐1), monocyte chemoattractant protein‐1 (MCP‐1), neutrophil gelatinase-associated lipocalin, uromodulin, and human cartilage glycoprotein-40. Outcomes were kidney disease progression (treated kidney failure, renal death, or sustained ≥40% eGFR decline) and treated kidney failure (maintenance dialysis or kidney transplant). Relative to models based on the linear predictor of the kidney failure risk equation (KFRE), improvements in discrimination were estimated with further addition of the nine tubular biomarkers using absolute difference in Uno C-index. Results: Over a median 3.5 years of follow-up, 1191 participants experienced kidney disease progression and 461 experienced treated kidney failure. KFRE demonstrated excellent discrimination for treated kidney failure (C-index 0.858 [0.840–0.874]) and moderate discrimination for kidney disease progression (0.724 [0.705–0.743]). Addition of three biomarkers (MCP-1, KIM-1, and EGF) most strongly associated with CKD outcomes yielded some further improvement in predicting kidney failure (absolute difference 0.014 [0.009–0.024]) and kidney disease progression (0.038 [0.026–0.053]). Prediction of kidney outcomes was only slightly improved when further expanding from three to all nine tubular biomarkers. Conclusion: In a wide range of causes of CKD, adding three urine tubular biomarkers (MCP-1, KIM-1, and EGF) to KFRE improves estimates of risk of CKD progression. Clinical Trial registry name and registration number: NCT03594110.

Background: The causal relevance of different adiposity markers to infection-related death remains uncertain. We estimated the lifelong associations of genetically predicted adiposity traits with infection-related mortality among 126 491 Mexican adults. Methods: Mendelian randomization (MR) using trans-ancestry instruments for body mass index (BMI), fat-mass percentage (FM%), and waist and hip circumference was used to estimate causal effects on infection-related mortality at ages 35–74 years, overall and across six infection subtypes. Multivariable MR was used to assess independent associations. Results: Genetic instruments explained 1.6%–3.5% of the variance in adiposity markers and the strengths of their associations with the markers were similar in men and women. Over 20 years, 2592 infection-related deaths occurred at ages 35–74 years. Genetically predicted adiposity was strongly associated with infection mortality, with the strongest association for FM% [hazard ratio (HR) per 1-SD higher level: 2.54; 95% confidence interval (CI) 1.91–3.38] and the weakest for hip circumference (HR 1.61; 95% CI 1.30–2.01). Associations were consistent across six subtypes: respiratory; urinary tract; gastrointestinal; sepsis; skin, bone, and connective tissue; and other infections. For BMI and FM%, mutual adjustment for waist and hip circumference reduced the associations by nearly one-third. For waist circumference, adjustment for BMI and hip circumference reduced the association by more than half, while, for hip circumference, mutual adjustment for BMI and waist circumference almost eliminated its association. Sensitivity analyses supported the main conclusions. Conclusion: Higher adiposity is causally associated with a higher risk of infection-related mortality in Mexican adults. Strategies to reduce the population levels of adiposity in Mexico could contribute to lowering mortality from infection.

The American Heart Association’s PREVENT equations estimate risk of total cardiovascular disease (CVD), atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF) to guide lipid-lowering and blood pressure-lowering therapy in people ages 30−79 years in the United States. The SCORE2 risk algorithm is used to estimate CVD risk for similar purposes in people ages 40 and older in Europe. Neither set of equations has been comprehensively validated in global observational cohorts and randomized trials. In this study, in 44 observational cohorts and 18 randomized trials, we assessed discrimination and calibration of the two risk algorithms across geographical regions (North America, Europe and Asia/Other or multiregional trials). We also created scaling factors for risk prediction over 1–9 years using the PREVENT equations, enabling shorter-term risk prediction for research purposes or to facilitate clinical trial enrollment. Over 5.1 years of mean follow-up, 293,737 PREVENT total CVD events (fatal and non-fatal ASCVD or HF) and 258,086 SCORE2 CVD events (myocardial infarction, stroke or cardiovascular death) were observed among 6,422,714 and 5,437,384 individuals, respectively. Despite differences in CVD outcome definitions, target populations and predictor variables, overall discrimination and calibration were similar for both equations, with generally good performance across regions, including in multiregional randomized trials. These findings lend support for adoption of PREVENT or SCORE2 for cardiovascular risk stratification across diverse settings.

Albuminuria and proteinuria are key biomarkers for diagnosing, staging, and monitoring of chronic kidney disease. Both are also strongly associated with renal and cardiovascular risk. Given their important diagnostic role, reliable measurement of urinary albumin and protein is essential for clinical practice and trials. However, variability in the methodology presently used to collect, store and prepare urine samples may influence assay performance and contribute to inconsistent results. In response to a call for harmonization, the United States of America National Kidney Foundation organized a workshop in April 2025 to define optimal methods and practices for measuring albuminuria and proteinuria. As part of this initiative, the available evidence on pre-analytical factors affecting urinary albumin and protein measurements was discussed. For this purpose, a narrative literature review was conducted to identify relevant studies focusing on urine collection strategies, including 24-hour versus single-void samples, timing of sample collection, the need for repeated measurements, clinical conditions that may cause false-positive results, and the effects of sample storage and handling under various temperature conditions. Given heterogeneity in study designs and outcome measures, findings were synthesized qualitatively. This consensus report summarizes the findings of this literature review and the resulting discussion. It highlights the importance of standardized urine collection and handling procedures to minimize biological and analytical variability. In addition, it provides practical recommendations.

Background and Hypothesis: Urinary albumin-to-creatinine ratio (UACR) and urinary protein-to-creatinine ratio (UPCR) are widely used markers for chronic kidney disease (CKD) risk stratification. While albuminuria is considered a sensitive predictor of kidney outcomes, proteinuria remains in use due to cost considerations or traditions. To assess their relative utility in prediction, we compared associations of UACR and UPCR with kidney failure in CKD clinical trials. Methods: We conducted a pooled analysis of individual participant-level data from four landmark CKD trials: DAPA-CKD, EMPA-KIDNEY, RENAAL, and IDNT. Overall, these trials included 6 219 participants with CKD, with and without diabetes, with available UACR and UPCR measurements derived from spot or 24-hour urine samples. Associations with kidney failure—defined as dialysis, transplantation, or sustained estimated glomerular filtration rate (eGFR) decline < 15 or <10 mL/min/1.73 m2—were assessed using Cox proportional hazards models adjusted for demographic and clinical covariates. Hazard ratios (HRs) per standard deviation increase in log-transformed UACR and UPCR were compared using relative ratios (RRs) and pooled across trials using random-effects models. Results: Across trials, both UACR and UPCR were strongly associated with kidney failure. Pooled HRs per standard deviation increment in log-transformed UACR or UPCR were 3.29 (95% CI: 2.91, 3.72) and 2.89 (95% CI: 2.52, 3.32), respectively, yielding an overall RR of 1.10 (95% CI: 1.03, 1.18), favouring UACR. This difference was clearest in the subgroup with type 2 diabetes and CKD (RR: 1.12; 95% CI: 1.04, 1.20), with less clear evidence for a difference among participants with glomerular disease (RR: 0.97; 95% CI: 0.83, 1.13). Conclusions: Both UACR and UPCR are robust predictors of kidney failure, but UACR demonstrated stronger associations, particularly in diabetic CKD. With a standardized urinary albumin assay becoming clinically available in 2027, these results support UACR as the preferred urinary protein measure for risk stratification.