Evidence map›Paper›PMID 31846221›Full record

ReviewJournal of diabetes2020

The Jiangnan diet, a healthy diet pattern for Chinese.

Jiqiu Wang, Xu Lin, Zachary T Bloomgarden, Guang Ning

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of diabetes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

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

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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

4 authors.

Jiqiu WangShanghai National Clinical Research Center for Metabolic Diseases, Department of Endocrinology and Metabolism, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China.ORCID https://orcid.org/0000-0002-9383-7656
Xu LinShanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences (SIBS), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Zachary T BloomgardenDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Guang NingShanghai National Clinical Research Center for Metabolic Diseases, Department of Endocrinology and Metabolism, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China.

Funding

National Key Research and Development Program of China 2018YFC1313800
6 · The paper itself

Abstract

Geographically, the Qinling Mountain-Huai River line divides China into two parts, Northern and Southern. Surprisingly, the line also divides the high prevalence of obesity and metabolic syndrome in Northern China from the low prevalence of Southern China. In past decades, the diet-center hypothesis has gained much support from the apparent cardiometabolic disease-protection effect of the Mediterranean diet. Questions include the following: Does the diet pattern explain the disease prevalence difference between two parts with similar genetic background? What kind of diet pattern is suitable for future national diet recommendation for Chinese, as the Mediterranean diet does for the Western countries? Here, we review the main healthy diet components, which the native inhabitants in the Yangtze River Delta region have eaten for several hundreds of years, and refer to this healthy diet as "Southern River ()-style dietary pattern" or "Jiangnan Diet."

Indexed as

Cardiovascular DiseasesChinaDietDiet, HealthyFeeding BehaviorGeographyHumansMetabolic SyndromeObesityPrevalenceRisk FactorsdietJiangnan dietMediterranean dietmetabolic syndromeobesity代谢综合症地中海饮食江南饮食肥胖饮食

Identifiers

PMID31846221
PMCPMC7216939

What OpenQuestion holds

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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.