{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T04:42:02Z","timestamp":1786250522363,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007658","name":"Baxter Healthcare Corporation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007658","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004881","name":"Instituto Mexicano del Seguro Social","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004881","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/03xddgg98","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>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\u2019s 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\u20130.74) 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\u20130.81), followed by HbA1c (r = 0.65; 95% CI: 0.51\u20130.75); 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.<\/jats:p>","DOI":"10.3390\/jcm15166137","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T09:14:28Z","timestamp":1786094068000},"page":"6137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Correlation Between Glycemic Biomarkers and Continuous Glucose Monitoring Metrics in Chronic Kidney Disease: A Systematic Review and Correlation Meta-Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3379-0147","authenticated-orcid":false,"given":"Miguel Angel","family":"Cuevas-Budhart","sequence":"first","affiliation":[{"name":"Unidad de Investigaci\u00f3n M\u00e9dica en Enfermedades Nefrol\u00f3gicas CMN Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06600, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"G\u00f3mez-Rosas","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica Escuela Nacional de Medicina y Homeopat\u00eda, Instituto Polit\u00e9cnico Nacional, Mexico City 07320, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rub\u00e9n David","family":"Saavedra-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica Escuela Nacional de Medicina y Homeopat\u00eda, Instituto Polit\u00e9cnico Nacional, Mexico City 07320, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8855-9682","authenticated-orcid":false,"given":"Marcela","family":"\u00c1vila-D\u00edaz","sequence":"additional","affiliation":[{"name":"Unidad de Investigaci\u00f3n M\u00e9dica en Enfermedades Nefrol\u00f3gicas CMN Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06600, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7672-9787","authenticated-orcid":false,"given":"Erwin","family":"Campos","sequence":"additional","affiliation":[{"name":"Macrotech, Santo Domingo 10902, Dominican Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5159-9856","authenticated-orcid":false,"given":"Aldo","family":"Ferreira-Hermosillo","sequence":"additional","affiliation":[{"name":"Unidad de Investigaci\u00f3n M\u00e9dica en Enfermedades Endocrinol\u00f3gicas CMN Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06600, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2156-3553","authenticated-orcid":false,"given":"Alfonso","family":"Ramos-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Macrotech, Santo Domingo 10902, Dominican Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2617-0430","authenticated-orcid":false,"given":"Iv\u00e1n","family":"Cavero-Redondo","sequence":"additional","affiliation":[{"name":"CarVasCare Research Group, Instituto de Biomedicina y Facultad de Enfermer\u00eda de Cuenca, Universidad de Castilla-La Mancha, 16002 Cuenca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3577-9707","authenticated-orcid":false,"given":"Ram\u00f3n","family":"Paniagua","sequence":"additional","affiliation":[{"name":"Unidad de Investigaci\u00f3n M\u00e9dica en Enfermedades Nefrol\u00f3gicas CMN Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City 06600, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S2213-8587(25)00299-2","article-title":"11th edition of the IDF Diabetes Atlas: Global, regional, and national diabetes prevalence estimates for 2024 and projections for 2050","volume":"14","author":"Genitsaridi","year":"2026","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1093\/ndt\/gfaf177","article-title":"IDF Diabetes Atlas 11th edition 2025: Global prevalence and projections for 2050","volume":"41","author":"Duncan","year":"2026","journal-title":"Nephrol. 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