Evidence map›Paper›PMID 35773653›Full record

ArticleBMC endocrine disorders2022

Clinical predictors for nondiabetic kidney diseases in patients with type 2 diabetes mellitus: a retrospective study from 2017 to 2021.

Yong-Qin Zeng, Yu-Xing Yang, Cheng-Jing Guan, Zi-Wei Guo, Bo Li, Hai-Yan Yu, Rui-Xue Chen, Ying-Qian Tang, Rui Yan

Open access · goldAbstract read
In one paragraph

Article in BMC endocrine disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
2.7field-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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Guideline
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025
    Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Renoprotective Impacts ofInternational journal of molecular sciences · 2023
    Article
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

9 authors at 2 institutions in 1 country.

Yong-Qin ZengDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China.
Yu-Xing YangDepartment of Endocrinology, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China.
Cheng-Jing GuanDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China.
Zi-Wei GuoDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China.
Bo LiDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China.
Hai-Yan YuDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China.
Rui-Xue ChenDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China.
Ying-Qian TangDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China.
Rui YanDepartment of Nephrology, The Affiliated Hospital of Guizhou Medical University, Guiyi Street, Yunyan District, Guiyang, 550004, China. yryr1234@126.com.
Affiliated Hospital of Guizhou Medical University · CNGuiyang Medical University · CN

Funding

Guizhou Provincial Natural Science Foundation ZK[2021]379Project of Guizhou Provincial Administration of Traditional Chinese Medicine QZYY-2021-020Science Technology Foundation of Guizhou Provincial Health Commission gzwjkj2020-1-002This work was supported by the National Natural Science Foundation of China 81960140
6 · The paper itself

Abstract

backgroundNondiabetic kidney disease (NDKD), which is prevalent among patients with diabetes mellitus (DM), is considerably different from diabetic kidney disease (DKD) in terms of the pathological features, treatment strategy and prognosis. Although renal biopsy is the current gold-standard diagnostic method, it cannot be routinely performed due to a range of risks. The aim of this study was to explore the predictors for differentiating NDKD from DKD to meet the urgent medical needs of patients who cannot afford kidney biopsy.

methodsThis is a retrospective study conducted by reviewing the medical records of patients with type 2 DM who underwent percutaneous renal biopsy at the Affiliated Hospital of Guizhou Medical University between January 2017 and May 2021. The demographic data, clinical data, blood test results, and pathological examination results of the patients were obtained from their medical records. Multivariate regression analysis was performed to evaluate the predictive factors for NDKD.

resultsA total of 244 patients were analyzed. The median age at biopsy was 55 (46, 62) years. Patients diagnosed with true DKD, those diagnosed with NDKD and those diagnosed with NDKD superimposed DKD represented 48.36% (118/244), 45.9% (112/244) and 5.74% (14/244), respectively, of the patient population. Immunoglobulin A nephropathy was the most common type of lesion in those with NDKD (59, 52.68%) and NDKD superimposed DKD (10, 71.43%). Independent predictive indicators for diagnosing NDKD included a DM duration of less than 5 years (odds ratio [OR] = 4.476; 95% confidence interval [CI]: 2.257-8.877; P < 0.001), an absence of diabetic retinopathy (OR = 4.174; 95% CI: 2.049-8.502; P < 0.001), a high RBC count (OR = 1.901; 95% CI: 1.251-2.889; P = 0.003), and a negative of urinary glucose excretion test result (OR = 2.985; 95% CI: 1.474-6.044; P = 0.002)..

conclusionsA DM duration less than 5 years, an absence of retinopathy, a high RBC count and an absence of urinary glucose excretion were independent indicators for the diagnosis of NDKD, suggesting that patients with NDKD may require a different treatment regimen than those with DKD.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesDiabetic RetinopathyGlucoseHumansKidneyRetrospective StudiesGlucoseDiabetes durationDiabetic retinopathyNondiabetic kidney diseaseRed blood cellType 2 diabetes mellitusUrinary glucose

Identifiers

PMID35773653
PMCPMC9248150
OpenAlexW4283727796

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Registered trials

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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.