{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T10:36:30Z","timestamp":1782297390055,"version":"3.54.5"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Glycated albumin may provide complementary information to hemoglobin A1c (HbA1c). We compared cross-sectional associations of HbA1c and glycated albumin with chronic kidney disease (CKD) in US adults.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We included 10\u200a923 adults (9955 without diagnosed diabetes, 968 with a diabetes diagnosis) from the National Health and Nutrition Examination Survey 1999\u20132004. We examined continuous associations and clinical cut points for HbA1c among those without diabetes (&amp;lt;5.0%, 5.0%\u20135.6% (reference), 5.7%\u20136.4%, \u22656.5%) and among those with diagnosed diabetes (&amp;lt;7.0%, 7.0%\u20138.9%, \u22659.0%) and percentile equivalents for glycated albumin. We used logistic regression to compare associations with prevalent CKD, adjusting for traditional risk factors. We used likelihood ratio tests to assess whether adding glycated albumin improved the model with HbA1c.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>There were J-shaped associations for both glycated albumin and HbA1c with CKD. Persons without a history of diabetes and very low glycated albumin or HbA1c were more likely to have CKD compared to those without diabetes and normoglycemia. The odds ratios (ORs) for CKD were 1.32 (95% CI, 1.12\u20131.55) for HbA1c 5.7% to 6.4% and 2.04 (95% CI, 1.28\u20133.25) for HbA1c \u22656.5%. The ORs for glycated albumin were 1.27 (95% CI, 1.06\u20131.51) and 2.48 (95% CI, 1.50\u20134.08) for glycated albumin 14.4% to 17.8% and \u226517.9%, respectively. The inclusion of glycated albumin in the model with HbA1c and traditional risk factors modestly but significantly improved the model fit (P value = 0.006).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Glycated albumin and HbA1c were similarly associated with prevalent CKD. Glycated albumin provides complementary information to HbA1c for prevalent CKD.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jalm\/jfac006","type":"journal-article","created":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T20:14:24Z","timestamp":1643400864000},"page":"842-853","source":"Crossref","is-referenced-by-count":5,"title":["Associations of Glycated Albumin and HbA1c with Chronic Kidney Disease in US Adults"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1707-7018","authenticated-orcid":false,"given":"Hyunju","family":"Kim","sequence":"first","affiliation":[{"name":"Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University , Baltimore, MD , USA"},{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olive","family":"Tang","sequence":"additional","affiliation":[{"name":"Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University , Baltimore, MD , USA"},{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Casey M","family":"Rebholz","sequence":"additional","affiliation":[{"name":"Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University , Baltimore, MD , USA"},{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morgan E","family":"Grams","sequence":"additional","affiliation":[{"name":"Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University , Baltimore, MD , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Coresh","sequence":"additional","affiliation":[{"name":"Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University , Baltimore, MD , USA"},{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert H","family":"Christenson","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Maryland School of Medicine , Baltimore, MD , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6923-7151","authenticated-orcid":false,"given":"Elizabeth","family":"Selvin","sequence":"additional","affiliation":[{"name":"Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University , Baltimore, MD , USA"},{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , 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