Evidence map›Paper›PMID 36517108›Full record

ArticleBMJ open diabetes research & care2022

Prevalence of non-diabetic kidney disease and inability of clinical predictors to differentiate it from diabetic kidney disease: results from a prospectively performed renal biopsy study.

Madhurima Basu, Smartya Pulai, Subhasis Neogi, Mainak Banerjee, Nitai P Bhattacharyya, Sanghamitra Sengupta, Pradip Mukhopadhyay, Arpita Ray Chaudhury, Sujoy Ghosh

Open access · goldAbstract read
In one paragraph

Article in BMJ open diabetes research & care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 17% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
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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.

Madhurima BasuEndocrinology and Metabolism, Institute of Postgraduate Medical Education and Research, Kolkata, India.ORCID 0000-0003-4693-5795
Smartya PulaiNephrology, Institute of Postgraduate Medical Education and Research, Kolkata, India.
Subhasis NeogiEndocrinology and Metabolism, Institute of Postgraduate Medical Education and Research, Kolkata, India.
Mainak BanerjeeEndocrinology and Metabolism, Institute of Postgraduate Medical Education and Research, Kolkata, India.
Nitai P BhattacharyyaEndocrinology and Metabolism, Institute of Postgraduate Medical Education and Research, Kolkata, India.
Sanghamitra SenguptaBiochemistry, University of Calcutta, Kolkata, West Bengal, India.
Pradip MukhopadhyayEndocrinology and Metabolism, Institute of Postgraduate Medical Education and Research, Kolkata, India.ORCID 0000-0002-4580-9589
Arpita Ray ChaudhuryNephrology, Institute of Postgraduate Medical Education and Research, Kolkata, India.
Sujoy GhoshEndocrinology and Metabolism, Institute of Postgraduate Medical Education and Research, Kolkata, India drsujoyghosh2000@gmail.com.ORCID 0000-0001-5397-961X
Institute of Post Graduate Medical Education and Research · INUniversity of Calcutta · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRenal involvement in type 2 diabetes mellitus (T2DM) may be due to diabetes (diabetic kidney disease (DKD)), causes other than diabetes (non-diabetic kidney disease (NDKD)) or overlap of DKD and NDKD (mixed kidney disease group). Prevalence of NDKD and predictive value of clinical or biochemical indicators have been explored in retrospective cohorts with preselection biases warranting the need for prospectively conducted unbiased renal biopsy study. RESEARCH DESIGN AND

methodsConsecutive subjects aged >18 years with T2DM and renal involvement with estimated glomerular filtration rate of 30-60 mL/min/m

resultsWe screened 6247 subjects with T2DM of which 869 fulfilled inclusion criteria for biopsy. Of the 869 subjects, biopsy was feasible in 818 subjects. Out of 818, we recruited first 110 subjects who agreed to undergo renal biopsy. Among those 110 subjects, 73 (66.4%) had DKD; 20 (18.2 %) had NDKD; and 17 (15.4 %) had mixed kidney disease. Subjects with NDKD as compared with DKD had shorter duration of diabetes (p<0.001), absence of retinopathy (p<0.001) and absence of neuropathy (p<0.001). Logistic regression revealed that only presence of retinopathy and duration of diabetes were statistically significant to predict histopathological diagnosis of DKD. 30% of DKD did not have retinopathy, thereby limiting the utility of the same as a discriminator. Use of traditional indicators of biopsy would have indicated a need for renal biopsy in 87.2% of subjects, though 64.5% of the subjects had DKD, who would not have benefitted from biopsy.

conclusionNDKD and mixed kidney disease in T2DM with renal involvement are very common and traditionally used parameters to select biopsies are of limited value in clinical decision making.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesRetinal DiseasesBiopsyHumansPrevalenceRetrospective StudiesDiabetes Mellitus, Type 2Kidney Diseases

Identifiers

PMID36517108
PMCPMC9756194
OpenAlexW4311474334

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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.