Evidence map›Paper›PMID 29046327›Full record

ArticleDiabetes care2017

Time Trends of Dietary and Lifestyle Factors and Their Potential Impact on Diabetes Burden in China.

Yanping Li, Dong D Wang, Sylvia H Ley, Malik Vasanti, Annie Green Howard, Yuna He, Frank B Hu

Erratum issuedAbstract read
In one paragraph

Article in Diabetes care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 84 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 3 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

84 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Trial
  6. Trial
  7. Article
  8. A Novel Function of Nonadecanoic Acid in Regulating Glucose Homeostasis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  9. Article
  10. Article
  11. Observational
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Observational
  18. Article
  19. Article
  20. Article

24 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Yanping LiDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.ORCID 0000-0002-0412-2748
Dong D WangDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Sylvia H LeyDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Malik VasantiDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Annie Green HowardDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC.
Yuna HeDepartment of Nutritional Epidemiology, National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China.
Frank B HuDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA nhbfh@channing.harvard.edu.

Funding

UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SUSAN M. SMITH · 1999 to 2026
$31.6M
MONITORING SOCIAL CHANGE: HEALTH, REPRODUCTION, AGINGR01HD030880 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 1994 to 2024
$27.0M
Carolina Population CenterR24HD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MORGAN, SAMUEL PHILIP · 2005 to 2014
$10.0M
Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
CHILD HEALTH EFFECTS OF RAPID SOCIO-DEMOGRAPHIC CHANGER01HD038700 · NICHD · UNIVERSITY OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2000 to 2004
$1.5M
NICHD NIH HHS P2C HD050924NICHD NIH HHS R01 HD030880NICHD NIH HHS R01 HD038700NICHD NIH HHS R24 HD050924NIDDK NIH HHS P30 DK056350
6 · The paper itself

Abstract

objectiveTo examine the secular trends in risk factors, estimate their impact on type 2 diabetes burden from 1991 to 2011, and project trends in the next 20 years. RESEARCH DESIGN AND

methodsRisk factor distributions were based on data from the China Health and Nutrition Survey 1991-2011. Diabetes cases attributable to all nonoptimal levels of each risk factor were estimated by applying the comparative risk assessment method.

resultsIn 2011, high BMI was the leading individual attributable factor for diabetes cases in China responsible for 43.8 million diabetes cases with a population-attributable fraction of 46.8%. Low whole-grain intake and high refined grain intake were the leading dietary risk factors in China responsible for 37.8 million and 21.8 million diabetes-attributable cases, respectively. The number of attributable diabetes cases associated with low physical activity, high blood pressure, and current smoking was 29.5, 21.6, and 9.8 million, respectively. Although intakes of low-fat dairy products, nuts, fruit, vegetables, and fish and seafood increased moderately over time, the average intake was below optimal levels in 2011 and were responsible for 15.8, 11.3, 9.9, 6.0, 3.6, and 2.6 million diabetes cases, respectively. Meanwhile, intakes of processed meat, red meat, and sugar-sweetened beverage showed increasing trends over time and were responsible for 2.8, 1.8, and 0.5 million diabetes cases, respectively, in 2011.

conclusionsA high BMI and low intake of whole grains but high intake of refined grains are the most important individual risk factors related to Chinese diabetes burden; low physical activity and high blood pressure also significantly contributed.

Indexed as

DietLife StyleAdultAgedAged, 80 and overBeveragesBlood PressureBody Mass IndexChinaDairy ProductsDiabetes Mellitus, Type 2ExerciseFemaleFollow-Up StudiesFruitHumansNutritive Sweeteners

Identifiers

PMID29046327
PMCPMC5862128

What OpenQuestion holds

Textmetadata
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.