Evidence map›Paper›PMID 41805834›Full record

ArticleDiabetes care2026

Accuracy, Variability, and Bias of Glycemic Biomarkers in Patients Treated With Maintenance Dialysis.

Leila R Zelnick, Subbulaxmi Trikudanathan, Yoshio N Hall, Ernest Ayers, Lisa Anderson, Nathaniel Ashford, Evelin Jones, Andrea C Rivas-Nieto, Simon Hsu, Kuanysh Kabytaev and 4 more

Abstract read
In one paragraph

Article in Diabetes care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Leila R ZelnickKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.ORCID 0000-0002-8461-5111
Subbulaxmi TrikudanathanDivision of Metabolism, Endocrinology, and Nutrition, University of Washington, Seattle, WA.ORCID 0000-0001-7397-9238
Yoshio N HallKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Ernest AyersKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Lisa AndersonKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Nathaniel AshfordKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Evelin JonesKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Andrea C Rivas-NietoKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Simon HsuKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Kuanysh KabytaevDepartment of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO.
Zhuo WangDepartment of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO.
Andrew N HoofnagleKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.
Irl B HirschDivision of Metabolism, Endocrinology, and Nutrition, University of Washington, Seattle, WA.ORCID 0000-0003-1675-8417
Ian H de BoerKidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA.

Funding

Hypoglycemia and glycemic control in patients treated with dialysisR01DK126373 · NIDDK · UNIVERSITY OF WASHINGTON · PI Leila R Zelnick, Ian H de Boer · 2020 to 2026
$4.2M
NIDDK NIH HHS R01 DK126373NIDDK NIH HHS R01DK126373
6 · The paper itself

Abstract

objectiveAccurate assessment of glycemia in patients treated with maintenance dialysis is imperative and hampered by known biases of glycated hemoglobin (HbA1c) in kidney failure (KF). This study evaluated the accuracy, variability, and covariate bias of three glycemic biomarkers compared with glycemia measured by continuous glucose monitor (CGM) among people with and without diabetes treated with maintenance dialysis. RESEARCH DESIGN AND

methodsIn a prospective community-based cohort study, 251 participants treated with maintenance dialysis wore a Dexcom G6 Pro CGM for 10 days. We compared correlations of HbA1c, glycated albumin (GA), and fructosamine with CGM-derived mean glycemia and examined sources of bias.

resultsParticipants (43% women; 63% with diabetes) had a median of 9.3 (interquartile range 8.5-9.4) valid days of CGM data. Mean (SD) HbA1c, GA, fructosamine, and mean CGM glucose were 6.2% (1.4%), 19.6% (6.3%), 351 (99) µmol/L, and 170 (63) mg/dL, respectively. HbA1c, GA, and fructosamine all strongly correlated with mean CGM blood glucose, with HbA1c and GA more correlated than fructosamine (overall, r = 0.85, r = 0.87, and r = 0.70, respectively; in diabetes, r = 0.84, r = 0.84, and r = 0.64, respectively). Compared with mean CGM glucose, HbA1c was significantly biased by erythropoiesis-stimulating agent dose, BMI, hemoglobin, and serum albumin; GA and fructosamine were biased by dialysis modality and vintage, residual kidney function, and BMI.

conclusionsHbA1c and GA were strongly correlated with mean CGM blood glucose, but all biomarkers had substantial bias by relevant clinical characteristics. HbA1c and GA may be useful assessments of average glycemia in patients treated with maintenance dialysis, if bias can be adequately addressed.

Indexed as

BiomarkersBlood GlucoseRenal DialysisAgedContinuous Glucose MonitoringFemaleFructosamineGlycated HemoglobinGlycated Serum AlbuminGlycation End Products, AdvancedHumansMaleMiddle AgedProspective StudiesSerum AlbuminBiomarkersBlood GlucoseFructosamineGlycated HemoglobinGlycated Serum AlbuminGlycation End Products, AdvancedSerum Albumin

Identifiers

PMID41805834
PMCPMC13094876

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.