DOI: 10.3390/jcm15166137 ISSN: 2077-0383

Correlation Between Glycemic Biomarkers and Continuous Glucose Monitoring Metrics in Chronic Kidney Disease: A Systematic Review and Correlation Meta-Analysis

Miguel Angel Cuevas-Budhart, Karen Gómez-Rosas, Rubén David Saavedra-Fernández, Marcela Ávila-Díaz, Erwin Campos, Aldo Ferreira-Hermosillo, Alfonso Ramos-Sánchez, Iván Cavero-Redondo, Ramón Paniagua

Background/Objectives: Accurate assessment of glycemic control in patients with diabetes and chronic kidney disease (CKD) is challenging because alterations associated with CKD affect the performance of conventional glycemic biomarkers, such as glycated hemoglobin (HbA1c), glycated albumin (GA), and fructosamine, while continuous glucose monitoring (CGM) offers a more comprehensive assessment of glycemic exposure; however, a quantitative synthesis of the correlation between these biomarkers and CGM-derived metrics across CKD stages is lacking. To systematically evaluate the correlation between HbA1c, GA, and fructosamine and CGM-derived glycemic metrics in adults with diabetes and CKD, using CGM as the reference standard. Methods: A systematic review with correlation meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, including studies published between January 2016 and March 2025, indexed in PubMed, Scopus, and Web of Science. Correlation coefficients were transformed using Fisher’s z transformation and pooled using a random-effects model; heterogeneity was assessed using the I2 statistic. Results: Ten studies met the eligibility criteria; six provided sufficient data for meta-analysis. The pooled correlation was moderate (r = 0.66; 95% confidence interval [CI]: 0.56–0.74) with substantial heterogeneity (I2 = 58.9%). In exploratory biomarker-specific subgroup analyses, GA showed the numerically highest correlation with CGM-derived metrics (r = 0.74; 95% CI: 0.65–0.81), followed by HbA1c (r = 0.65; 95% CI: 0.51–0.75); fructosamine was more variable. Conclusions: No single biomarker demonstrated consistent superiority across CKD stages and treatment modalities. These correlation analyses measure linear association, not clinical interchangeability. Biomarker selection should be individualized according to CKD stage, dialysis status, and CGM availability.

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