Evidence map›Paper›PMID 25785585›Full record

ArticlePloS one2015

Optimal cut-off points of fasting plasma glucose for two-step strategy in estimating prevalence and screening undiagnosed diabetes and pre-diabetes in Harbin, China.

Chundan Bao, Dianfeng Zhang, Bo Sun, Li Lan, Wenxiu Cui, Guohua Xu, Conglan Sui, Yibaina Wang, Yashuang Zhao, Jian Wang and 1 more

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

11 authors.

Chundan BaoDepartment of Epidemiology, Public Health College, Harbin Medical University, Harbin, Heilongjiang Province, P. R. China.
Dianfeng ZhangDepartment of Epidemiology, Public Health College, Harbin Medical University, Harbin, Heilongjiang Province, P. R. China; Department of Chronic Noncommunicable Disease Control and Prevention, Harbin Center for Disease Control and Prevention, Harbin, Heilongjiang Province, P. R. China.
Bo SunDepartment of Chronic Noncommunicable Disease Control and Prevention, Harbin Center for Disease Control and Prevention, Harbin, Heilongjiang Province, P. R. China.
Li LanDepartment of Chronic Noncommunicable Disease Control and Prevention, Harbin Center for Disease Control and Prevention, Harbin, Heilongjiang Province, P. R. China.
Wenxiu CuiDepartment of Chronic Noncommunicable Disease Control and Prevention, Harbin Center for Disease Control and Prevention, Harbin, Heilongjiang Province, P. R. China.
Guohua XuDepartment of Chronic Noncommunicable Disease Control and Prevention, Harbin Center for Disease Control and Prevention, Harbin, Heilongjiang Province, P. R. China.
Conglan SuiDepartment of Chronic Noncommunicable Disease Control and Prevention, Harbin Center for Disease Control and Prevention, Harbin, Heilongjiang Province, P. R. China.
Yibaina WangDepartment of Epidemiology, Public Health College, Harbin Medical University, Harbin, Heilongjiang Province, P. R. China.
Yashuang ZhaoDepartment of Epidemiology, Public Health College, Harbin Medical University, Harbin, Heilongjiang Province, P. R. China.
Jian WangDepartment of Chronic Noncommunicable Disease Control and Prevention, Harbin Center for Disease Control and Prevention, Harbin, Heilongjiang Province, P. R. China.
Hongyuan LiDepartment of Epidemiology, Public Health College, Harbin Medical University, Harbin, Heilongjiang Province, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To identify optimal cut-off points of fasting plasma glucose (FPG) for two-step strategy in screening abnormal glucose metabolism and estimating prevalence in general Chinese population. A population-based cross-sectional study was conducted on 7913 people aged 20 to 74 years in Harbin. Diabetes and pre-diabetes were determined by fasting and 2 hour post-load glucose from the oral glucose tolerance test in all participants. Screening potential of FPG, cost per case identified by two-step strategy, and optimal FPG cut-off points were described. The prevalence of diabetes was 12.7%, of which 65.2% was undiagnosed. Twelve percent or 9.0% of participants were diagnosed with pre-diabetes using 2003 ADA criteria or 1999 WHO criteria, respectively. The optimal FPG cut-off points for two-step strategy were 5.6 mmol/l for previously undiagnosed diabetes (area under the receiver-operating characteristic curve of FPG 0.93; sensitivity 82.0%; cost per case identified by two-step strategy ¥261), 5.3 mmol/l for both diabetes and pre-diabetes or pre-diabetes alone using 2003 ADA criteria (0.89 or 0.85; 72.4% or 62.9%; ¥110 or ¥258), 5.0 mmol/l for pre-diabetes using 1999 WHO criteria (0.78; 66.8%; ¥399), and 4.9 mmol/l for IGT alone (0.74; 62.2%; ¥502). Using the two-step strategy, the underestimates of prevalence reduced to nearly 38% for pre-diabetes or 18.7% for undiagnosed diabetes, respectively. Approximately a quarter of the general population in Harbin was in hyperglycemic condition. Using optimal FPG cut-off points for two-step strategy in Chinese population may be more effective and less costly for reducing the missed diagnosis of hyperglycemic condition.

Indexed as

AdultAgedBiomarkersBlood GlucoseChinaCross-Sectional StudiesDiabetes MellitusFastingFemaleGlucose IntoleranceGlucose Tolerance TestHumansHyperglycemiaMaleMass ScreeningMiddle AgedBiomarkersBlood Glucose

Identifiers

PMID25785585
PMCPMC4364753

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.