Evidence map›Paper›PMID 37161594›Full record

ArticleEuropean journal of medical research2023

A new, alternative risk score for sarcopenia in Chinese patients with type 2 diabetes mellitus.

Qinghua He, Xiuzhi Wang, Caizhe Yang, Xiaoming Zhuang, Yanfen Yue, Hongjiang Jing, Jing Hu, Mingxiao Sun, Lixin Guo

Open access · goldAbstract readMulticenter StudyValidation Study
In one paragraph

Article in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
1.1field-weighted citation impact, top 24% 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. 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

9 authors at 5 institutions in 1 country.

Qinghua HeDepartment of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China. 13811620484@163.com.
Xiuzhi WangDepartment of Endocrinology, Pinggu Hospital, Beijing Traditional Chinese Medicine Hospital, Beijing, 10120, China.
Caizhe YangDepartment of Endocrinology, Air Force Characteristic Medical Center, Beijing, 100142, China.
Xiaoming ZhuangDepartment of Endocrinology, Fuxing Hospital Affiliated to Capital Medical University, Beijing, 100038, China.
Yanfen YueDepartment of Nutrition, Pinggu Hospital, Beijing Traditional Chinese Medicine Hospital, Beijing, 10120, China.
Hongjiang JingDepartment of Nutrition, Air Force Characteristic Medical Center, Beijing, 100142, China.
Jing HuDepartment of Nutrition, Fuxing Hospital Affiliated to Capital Medical University, Beijing, 100038, China.
Mingxiao SunDepartment of Endocrinology, Beijing Yide Hospital, Beijing, 100195, China.
Lixin GuoDepartment of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Air Force General Hospital PLA · CNBeijing Hospital of Traditional Chinese Medicine · CNCapital Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNBeijing Hospital · CN

Funding

the Science and Technology Commission, Beijing, China D141107005314005
6 · The paper itself

Abstract

objectiveTo develop a new, alternative sarcopenia risk score to screen for sarcopenia in type 2 diabetes patients in China and to demonstrate its validity. RESEARCH DESIGN AND

methodsThe data for this study came from a multicenter, cross-sectional study that had been designed to estimate the prevalence of sarcopenia among adults with type 2 diabetes and had been conducted in several hospitals in Beijing, China. A total of 1125 participants were randomly divided into two groups: an exploratory population and a validation population. A multivariable logistic regression model using the backward stepwise likelihood ratio method to estimate the probability of sarcopenia was fitted with candidate variables in the exploratory population. A new, alternative sarcopenia risk score was developed based on the multivariable model. The internal and external validations were performed in the exploratory and validation populations. The study was registered at Chinese Clinical Trial Registry (ChiCTR-EOC-15006901).

resultsThe new, alternative sarcopenia risk score included five variables: age, gender, BMI, total energy intake per day, and the proportion of calories supplied by protein. The score ranged from - 2 to 19. The area under the receiver operating characteristic (ROC) curve of the risk score for the prediction of sarcopenia in type 2 diabetes patients was 0.806 (95% CI 0.741-0.872) and 0.836 (95% CI 0.781-0.892) in the exploratory and validation populations, respectively. At the optimal cutoff value of 12, the sensitivity and specificity of the score for the prediction of sarcopenia were 70.9% and 81.0% in the exploratory population and 53.7% and 88.8% in the validation population, respectively. The Hosmer-Lemeshow goodness-of-fit test showed a good calibration with the risk score in external validation (χ

conclusionsThe new, alternative sarcopenia risk score appears to be an effective screening tool for identification of sarcopenia in Chinese patients with type 2 diabetes in clinical practice. Clinical trial registration Chinese Clinical Trial Registry, ChiCTR-EOC-15006901.

Indexed as

Diabetes Mellitus, Type 2SarcopeniaAdultCross-Sectional StudiesEast Asian PeopleHumansRandom AllocationRisk FactorsRisk scoreSarcopeniaType 2 diabetes

Identifiers

PMID37161594
PMCPMC10170735
OpenAlexW4376115208

What OpenQuestion holds

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LicenceCC BY
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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.