Evidence map›Paper›PMID 36328996›Full record

ArticleKidney research and clinical practice2023

Distribution of pathological types and epidemiological characteristics based on kidney biopsy in Northwest China.

Yunlong Qin, Jin Zhao, Xiao Wei, Yuwei Wang, Zixian Yu, Yumeng Zhang, Shiren Sun

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Article in Kidney research and clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Yunlong QinDepartment of Nephrology, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Jin ZhaoDepartment of Nephrology, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Xiao WeiDepartment of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China.
Yuwei WangDepartment of Nephrology, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Zixian YuDepartment of Nephrology, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Yumeng ZhangDepartment of Nephrology, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Shiren SunDepartment of Nephrology, Xijing Hospital, Air Force Military Medical University, Xi'an, China.
Xijing Hospital · CNAir Force Medical University · CNBethune International Peace Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe spectrum of biopsy-confirmed kidney disease varies with regions and periods. We describe the distribution of pathological types and epidemiological characteristics of kidney diseases in Northwest China due to regional differences in geographical environment, social economy, and dietary habits.

methodsKidney biopsy cases from 2005 to 2020 in Xijing Hospital were retrospectively analyzed. Pathological characteristics of patients in different periods were analyzed using the t test or chi-square test. Joinpoint regression was used to analyze trends in pathological types and disease spectrum.

resultsA total of 10,528 eligible patients were included. Primary glomerular disease (PGD) accounted for the majority of the cases and exhibited an obvious downward trend, whereas secondary glomerular disease (SGD) showed an obvious upward trend. Among PGD, immunoglobulin A nephropathy (IgAN) remained the most common pathological type, and the detection rate of membranous nephropathy (MN) was significantly increased. Among SGD, Henoch-Schönlein purpura nephritis (HSPN) was the most common pathological type and may present a significant characteristic of Northwest China. Diabetic nephropathy (DN) exhibited the most obvious upward trend in the whole process, whereas the fastest growth since 2012 was in hypertensive nephropathy.

conclusionThe proportion of SGD increased whereas PGD declined. IgAN remained the most common PGD, and HSPN was the most common SGD. MN and DN showed the most obvious upward trend among PGD and SGD, respectively. Changes in the spectrum of kidney disease, especially the constituent ratio of SGD, pose a great challenge to public health.

Indexed as

BiopsyChinaEpidemiologyKidney diesasePatholog

Identifiers

PMID36328996
PMCPMC9902739
OpenAlexW4294793797

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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.