Evidence map›Paper›PMID 37096377›Full record

ArticleDiabetes & metabolism journal2023

Glycemic Control and Adverse Clinical Outcomes in Patients with Chronic Kidney Disease and Type 2 Diabetes Mellitus: Results from KNOW-CKD.

Ga Young Heo, Hee Byung Koh, Hyung Woo Kim, Jung Tak Park, Tae-Hyun Yoo, Shin-Wook Kang, Jayoun Kim, Soo Wan Kim, Yeong Hoon Kim, Su Ah Sung and 2 more

Open access · goldAbstract read
In one paragraph

Article in Diabetes & metabolism journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 9% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Observational
  3. Review
  4. Article
  5. Article
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  7. Article
  8. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 6 institutions in 3 countries.

Ga Young HeoDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea.
Hee Byung KohDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea.
Hyung Woo KimDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea.
Jung Tak ParkDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea.
Tae-Hyun YooDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea.
Shin-Wook KangDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea.
Jayoun KimMedical Research Collaborating Center, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Soo Wan KimDepartment of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea.
Yeong Hoon KimDepartment of Internal Medicine, Inje University Busan Paik Hospital, Busan, Korea.
Su Ah SungDepartment of Internal Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Korea.
Kook-Hwan OhDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
Seung Hyeok HanDepartment of Internal Medicine, Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea.
Yonsei University · KRChonnam National University · KREulji University · KRInje University Busan Paik Hospital · KRNew Generation University College · ETSeoul National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgruoundThe optimal level of glycosylated hemoglobin (HbA1c) to prevent adverse clinical outcomes is unknown in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM).

methodsWe analyzed 707 patients with CKD G1-G5 without kidney replacement therapy and T2DM from the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease (KNOW-CKD), a nationwide prospective cohort study. The main predictor was time-varying HbA1c level at each visit. The primary outcome was a composite of development of major adverse cardiovascular events (MACEs) or all-cause mortality. Secondary outcomes included the individual endpoint of MACEs, all-cause mortality, and CKD progression. CKD progression was defined as a ≥50% decline in the estimated glomerular filtration rate from baseline or the onset of end-stage kidney disease.

resultsDuring a median follow-up of 4.8 years, the primary outcome occurred in 129 (18.2%) patients. In time-varying Cox model, the adjusted hazard ratios (aHRs) for the primary outcome were 1.59 (95% confidence interval [CI], 1.01 to 2.49) and 1.99 (95% CI, 1.24 to 3.19) for HbA1c levels of 7.0%-7.9% and ≥8.0%, respectively, compared with <7.0%. Additional analysis of baseline HbA1c levels yielded a similar graded association. In secondary outcome analyses, the aHRs for the corresponding HbA1c categories were 2.17 (95% CI, 1.20 to 3.95) and 2.26 (95% CI, 1.17 to 4.37) for MACE, and 1.36 (95% CI, 0.68 to 2.72) and 2.08 (95% CI, 1.06 to 4.05) for all-cause mortality. However, the risk of CKD progression did not differ between the three groups.

conclusionThis study showed that higher HbA1c levels were associated with an increased risk of MACE and mortality in patients with CKD and T2DM.

Indexed as

Diabetes Mellitus, Type 2Renal Insufficiency, ChronicCohort StudiesDisease ProgressionGlycated HemoglobinGlycemic ControlHumansProspective StudiesGlycated HemoglobinCardiovascular diseasesDiabetes mellitus, type 2Glycated hemoglobin ARenal insufficiency, chronic

Identifiers

PMID37096377
PMCPMC10404520
OpenAlexW4366990304

What OpenQuestion holds

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LicenceCC BY-NC
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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.