Evidence map›Paper›PMID 28120569›Full record

ArticleYonsei medical journal2017

Cystatin C is Better than Serum Creatinine for Estimating Glomerular Filtration Rate to Detect Osteopenia in Chronic Kidney Disease Patients.

Young Eun Kwon, Mi Jung Lee, Kyoung Sook Park, Seung Hyeok Han, Tae Hyun Yoo, Kook Hwan Oh, Joongyub Lee, Kyu Beck Lee, Wookyung Chung, Yeong Hoon Kim and 2 more

Open access · goldAbstract read
In one paragraph

Article in Yonsei medical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. Association of renal function with bone mineral density and fracture risk in the Longitudinal Aging Study Amsterdam.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2018
    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

12 authors at 6 institutions in 1 country.

Young Eun KwonDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
Mi Jung LeeDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
Kyoung Sook ParkDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
Seung Hyeok HanDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
Tae Hyun YooDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
Kook Hwan OhDepartment of Internal Medicine, Seoul National University, Seoul, Korea.
Joongyub LeeMedical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea.
Kyu Beck LeeDepartment of Internal Medicine, Kangbuck Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
Wookyung ChungDivision of Nephrology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea.
Yeong Hoon KimDepartment of Nephrology, College of Medicine, Inje University, Busan, Korea.
Curie AhnDepartment of Internal Medicine, Seoul National University, Seoul, Korea.
Kyu Hun ChoiDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea. khchoi6@yuhs.ac.
Yonsei University · KRSeoul National University · KRGachon University Gil Medical Center · KRInje University · KRSeoul National University Hospital · KRSungkyunkwan University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeRecent studies have reported that loss of bone mass is associated with renal function decline and increased fracture risks in chronic kidney disease (CKD) patients. The aim of this study was to investigate the best estimated glomerular filtration rate (eGFR) equation to detect osteopenia in CKD patients. MATERIALS AND

methodsThis was a cross-sectional study, and 780 patients aged 50 years or above were classified into normal bone mass or osteopenia groups according to the -1.0 of T-scores at total hip and femur neck. Comparisons of area under the receiver operating characteristic (ROC) curves (AUC) were performed to investigate significant differences among three eGFR formulas: Modification of Diet in Renal Disease, CKD-Epidemiology Collaboration (EPI) creatinine, and CKD-EPI cystatin C (CKD-EPI-Cys).

resultsThe mean age was 61 years old and the proportion of females was 37.3%. The total hip osteopenia group showed lower CKD-EPI-Cys eGFR levels (osteopenia group, 33.3±19.0 mL/min/1.73 m²; normal group, 48.1±26.2 mL/min/1.73 m², p<0.001). In multiple logistic regression analysis, CKD-EPI-Cys eGFR was independently associated with osteopenia at the total hip (per 1 mL/min/1.73 m² increase, odds ratio 0.98, 95% confidence interval 0.97-0.99, p=0.004) after adjusting for confounding variables. ROC curve analyses indicated that CKD-EPI-Cys shows the largest AUC for osteopenia at the total hip (AUC=0.678, all p<0.01) and the femur neck (AUC=0.665, all p<0.05).

conclusionDecreased renal function assessed by CKD-EPI-Cys equation correlates with osteopenia better than creatinine-based methods in CKD patients, and the CKD-EPI-Cys formula might be a useful tool to assess skeletal-related event risks.

Indexed as

Glomerular Filtration RateAdultAgedBiomarkersBone Diseases, MetabolicChronic Kidney Disease-Mineral and Bone DisorderCreatinineCross-Sectional StudiesCystatin CFemaleHumansMaleMiddle AgedRenal Insufficiency, ChronicROC CurveBiomarkersCreatinineCystatin Cbone diseaseschronicglomerular filtration ratemetabolicRenal insufficiency

Identifiers

PMID28120569
PMCPMC5290018
OpenAlexW2580114221

What OpenQuestion holds

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LicenceCC BY-NC
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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.