Objective: To synthesize current evidence on the role of renal Doppler ultrasonography and blood/urinary biomarkers in complementing KDIGO-based strategies for early detection and risk stratification of chronic kidney disease (CKD).
Methods: We searched PubMed, the Cochrane Library and major guideline repositories (01/2010–10/2025), with emphasis on studies published from 2019 onwards, for randomized trials, observational cohorts, systematic reviews and guidelines evaluating renal Doppler indices particularly the renal resistive index (RRI) and biomarkers including albuminuria (ACR), cystatin C, NGAL, KIM-1, MCP-1/EGF ratio, soluble TNF receptors (TNFR1/2) and suPAR in relation to CKD onset, progression and outcomes.
Results: KDIGO 2024 recommends combining eGFR and albuminuria as the cornerstone for CKD detection and staging. Within this framework, elevated RRI (commonly ≥0.70–0.80) in CKD and diabetic kidney disease correlates with lower eGFR, higher albuminuria, arterial stiffness and adverse renal/mortality outcomes; several cohorts suggest that RRI may predict CKD progression even in patients without overt proteinuria. Novel tubular-injury and immune-inflammatory biomarkers (KIM-1, NGAL, MCP-1, TNFRs, suPAR) show promising incremental value for predicting incident CKD and progression, although effect sizes, cut-offs and the consistency of evidence particularly for TNFRs remain heterogeneous and largely based on observational data. Combining Doppler parameters (RRI, intrarenal venous flow) with biomarker panels and KDIGO risk categories may enhance early identification of high-risk patients, but requires standardized measurement protocols, age/comorbidity-specific reference ranges and pragmatic trials testing biomarker-guided strategies.
Conclusion: Renal Doppler ultrasonography especially RRI and selected blood/urinary biomarkers appear to augment KDIGO-recommended eGFR/albuminuria-based screening, potentially improving early detection and risk stratification in CKD. Multimodal algorithms integrating imaging, biomarkers and clinical variables warrant validation in real-world interventional studies.