Evidence map›Paper›PMID 42662030›Full record

ArticleBioinformation2026

Diagnostic accuracy of UACR in CKD screening among diabetics: A community study.

Utsav Haldar, Niranjan Gopal, Anand Chellappan, Dnyanesh Amle, Nandeesh Kuravatti, Neha Patel, Jyoti E John, Deepa Telgote

Abstract read
In one paragraph

Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Utsav HaldarDepartment of Biochemistry, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.
Niranjan GopalDepartment of Biochemistry, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.
Anand ChellappanDepartment of Nephrology, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.
Dnyanesh AmleDepartment of Biochemistry, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.
Nandeesh KuravattiDepartment of Community Medicine, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.
Neha PatelDepartment of Biochemistry, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.
Jyoti E JohnDepartment of Biochemistry, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.
Deepa TelgoteDepartment of Biochemistry, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic kidney disease (CKD) is a major microvascular complication of diabetes in rural India, where access to diagnostic facilities is limited. 24-hour urine protein estimation, through the reference method is impractical for community screening. Therefore, it is of interest to evaluate the diagnostic accuracy of urinary albumin-to-creatinine ratio (UACR) for CKD screening among 201 known diabetic individuals from rural Nagpur. Elevated UACR was observed in 99% of participants, whereas 59% had abnormal 24-hour urine protein excretion. UACR demonstrated 100% sensitivity but low specificity (4%), with an overall diagnostic accuracy of 60% and a negative predictive value of 100%. UACR correlated significantly with glycaemic indices, eGFR and proteinuria and its screening utility improves when combined with serum creatinine and eGFR in resource-limited community settings.

Indexed as

chronic kidney disease (CKD)Diabetes mellitus (DM)diabetic kidney disease (DKD)diagnostic accuracyrural populationurinary albumin-to-creatinine ratio (UACR)

Identifiers

PMID42662030
PMCPMC13519431

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.