Evidence map›Paper›PMID 34777740›Full record

ArticleTherapeutic advances in chronic disease2021

The association between NAFLD and risk of chronic kidney disease: a cross-sectional study.

Ying Cao, You Deng, Jingjing Wang, Hong Zhao, Jingyu Zhang, Wen Xie

Open access · goldAbstract read
In one paragraph

Article in Therapeutic advances in chronic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 pooled it
5.4field-weighted citation impact, top 3% 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

23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 49 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Ying CaoCenter of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
You DengCenter of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Jingjing WangCenter of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Hong ZhaoCenter of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Jingyu ZhangCenter of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Wen XieCenter of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, No. 8, Jingshun East Street, Chaoyang District, Beijing 100015, China.ORCID https://orcid.org/0000-0002-7314-8175
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe aim of this study was to evaluate the association between nonalcoholic fatty liver disease (NAFLD) and NAFLD with different comorbidities and risk of chronic kidney disease (CKD) and abnormal albuminuria. MATERIALS AND

methodsA total of 3872 Chinese individuals excluding those with hepatitis B or C infection and absence of alcohol abuse were included in the study. NAFLD was diagnosed by abdominal ultrasonography. The liver fibrosis was assessed by NAFLD fibrosis score (NFS) and fibrosis-4 index (FIB-4). CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m

resultsThe prevalence of CKD and abnormal albuminuria was higher in individuals with NAFLD than in those without NAFLD (15.8% vs 11.9%,

conclusionsNAFLD and NAFLD with traditional comorbidities are strongly associated with risk of prevalence of CKD and abnormal albuminuria. Patients with NAFLD especially those with coexisting comorbidities were recommended to carefully access the development of CKD.

Indexed as

CKDliver fibrosisNAFLD

Identifiers

PMID34777740
PMCPMC8586173
OpenAlexW3214447661

What OpenQuestion holds

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LicenceCC BY-NC
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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.