Observational studyJournal of diabetes research2020

Risk Factors for Sarcopenia in the Elderly with Type 2 Diabetes Mellitus and the Effect of Metformin.

Fenqin Chen, Shuai Xu, Yingfang Wang, Feng Chen, Lu Cao, Tingting Liu, Ting Huang, Qian Wei, Guojing Ma, Yuhong Zhao and 1 more

Registry-linked trialOpen access · goldFull text readObservational Study
In one paragraph

Observational study in Journal of diabetes research, 2020. The graph read 1 number from its abstract, feeding 1 cell of the map, but none could be read as for or against, so it casts no vote. It also reports an association that does not count as treatment evidence, such as OR 2.54 (1.48 to 4.37) for body weight & composition. It is linked to trial NCT06958302 (The Relation Between Diabetic Neuropathy and Muscle Mass in Type 2 Diabetes Mellitus Patients), which is not on this map. Cited by 45 papers, 2 of them syntheses that pooled it.

1number the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 2 pooled it
5.3field-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.

Read, but not usablea number the graph found but could not read as for or against

Body weight & compositionan association or prognostic statement, not a treatment comparison · head-to-head · t2dfeeds one cell of the map
OR 2.541.48 to 4.37
In the multivariate analysis, sex, age, educational level, and BMI were risk factors for sarcopenia, with women more likely to develop sarcopenia relative to men (OR = 2.539, 95% CI = 1.475-4.371; CONCLUSIONS: We showed that being female and at an older age, lower educational level, and lower BMI were risk factors for sarcopenia in elderly T2DM and that metformin acted as a protective agent against sarcopenia in these patients.

clause the extractor read what became the number

2 · Its place on the map

Where it lands on the map

Rows are treatments, columns are outcomes. The coloured squares are the cells this paper feeds, coloured by the vote it casts there. Click one to jump to what this paper adds to it.

supports the treatmentfavours the comparatorno clear differenceread, but no usable result
3 · What it changes

What it adds to each cell

For every cell the paper feeds: the belief in the claim with and without this paper, and this paper's estimate drawn against every other readable study in the cell. The ringed dot is this paper.

Metformin×body weight & composition

No readable resultOpen on the map →What to test next →

19 readable studies in this cell: 7 favour the treatment, 6 find no difference, 6 favour the comparator.

Belief with this paper
0.50contested · 8 families support, 4 contradict · against placebo
Without itNot a counted family in this claim, so removing it changes nothing.
← favours the comparatorfavours the treatment →
0 · no effect
NCT018093271,186 enrolled · 2013
Δ -0.90-1.60 to -0.20
NCT008598981,093 enrolled · 2009
Δ -1.37-2.03 to -0.71
NCT00643851994 enrolled · 2008
Δ -0.05-0.72 to 0.61
NCT02932475831 enrolled · 2017
Δ 0.04
NCT00676338820 enrolled · 2008
Δ -0.04-0.61 to 0.53
NCT02980276535 enrolled · 2017
Δ -0.70-1.30 to -0.20
Δ 17.05.00 to 29.0
increase 1.26-0.24 to 2.75
weight loss -16.2-60.2 to -4.40

This paper's own estimate is on a different scale from the rest of the cell, so it is not drawn here.

4 · 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.

NCT06958302 recruitingnot on this mapstarted 2025, after this paper: background citation

The Relation Between Diabetic Neuropathy and Muscle Mass in Type 2 Diabetes Mellitus Patients

TypeobservationalSponsorAin Shams UniversityRan2025 to 2026Enrolled60ConditionsDiabetic Neuropathy, Sarcopenia
5 · Its place in the literature

Who cites it

45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 74 citations in OpenAlex.

  1. Pooled it
  2. Prevalence of Sarcopenia in Africa: A Systematic Review.Clinical interventions in aging · 2023
    Pooled it
  3. Trial
  4. Trial
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Role of muscle glucometabolism derived fromQuantitative imaging in medicine and surgery · 2025
    Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
6 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

7 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Fenqin ChenDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.ORCID https://orcid.org/0000-0002-9534-7693
Shuai XuDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Yingfang WangDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Feng ChenDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Lu CaoDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Tingting LiuDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Ting HuangDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Qian WeiDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Guojing MaDepartments of Geriatrics, The First Affiliated Hospital, China Medical University, Shenyang 110001, China.
Yuhong ZhaoDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang 110004, China.
Difei WangDepartment of Geriatrics, Shengjing Hospital of China Medical University, Shenyang 110004, China.ORCID https://orcid.org/0000-0002-9711-8501
First Hospital of China Medical University · CNChina Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

8 · The paper itself

Abstract

The marked sentences are the ones the graph read a number from.

aimsSarcopenia is a common condition in older individuals, especially in the elderly with type 2 diabetes mellitus (T2DM). The aim of the present study was to examine the risk factors for sarcopenia in elderly individuals with T2DM and the effects of metformin.

methodsA total of 1732 elderly with T2DM were recruited to this cross-sectional observational study, and we analyzed the data using logistic regression analyses. Skeletal muscle mass, grip strength, and usual gait speed were measured to diagnose sarcopenia according to the criteria of the Asian Working Group for Sarcopenia, combined with expert consensus on sarcopenia in China.

resultsThe overall prevalence of sarcopenia was 10.37% of the participants. In the multivariate analysis, sex, age, educational level, and BMI were risk factors for sarcopenia, with women more likely to develop sarcopenia relative to men (OR = 2.539, 95% CI = 1.475-4.371;

conclusionsWe showed that being female and at an older age, lower educational level, and lower BMI were risk factors for sarcopenia in elderly T2DM and that metformin acted as a protective agent against sarcopenia in these patients.

Indexed as

AdolescentAdultAgedAged, 80 and overCross-Sectional StudiesDiabetes Mellitus, Type 2ExerciseFemaleHand StrengthHumansHypoglycemic AgentsMaleMetforminMiddle AgedMultivariate AnalysisMuscle, SkeletalHypoglycemic AgentsMetformin

Identifiers

PMID33083494
PMCPMC7563046
OpenAlexW3091867259

What Socratic holds

Textfull text, public
LicenceCC BY
Read underepoch 390

Registered trials

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 Socratic graph.